Keywords: Analyse des réseaux, perte de fonction сriblage, profilage du transcriptome, CRISPR/Cas9, voies moléculaires MasterPATH: network analysis of functional genomics screening data Network analysis, loss-of-function screening, RNA interference, transcriptome profiling, CRISPR/Cas9, molecular pathway

A l'aide de la méthode masterPATH on a analysé les données de la perte de fonction сriblage de l'acide ribonucléique micro et l'analyse de transcription de la différenciation terminal musculaire et les données de la perte de fonction сriblage du procès de la réparation de l'ADN. On peut trouver le code initial de la méthode si l'on suit le lien https://github.com/daggoo/masterPATH.

1 Titre : MasterPATH : l'analyse de réseau des données expérimentales de la génomique fonctionnelle.

Résumé : Les technologies « omics » et notamment les technologies de la génomique fonctionnelle utilisent des approches expérimentales à haut débit dont le but est déterminer des composants biologiques (des gènes, l'acide ribonucléique messager, l'acide ribonucléique micro, des protéines…) relevant de ce phénotype. La perte de fonction de l'étude basée au mécanisme ARNinterférence ou CRISPR/Cas9 et le profilage du transcriptome (en utilisant des puces d l'ADN ou des méthodes RNA-seq) sont les technologies « omics » les plus répandues de nos jours. Telles études génèrent de grands volumes des données qui sont représenté d'habitude par une liste rangée des composants biologiques d'où on peut obtenir une soi-disant « hit-liste » en utilisant des méthodes statistiques convenantes. Bien qu'il suffit pour définir les composants biologiques principaux qui sont importants pour ce phénotype, les méthodes de rangement ne peuvent pas aider à déterminer la figure du système biologique étudié la plus détaillée.

Depuis 15 ans on a élaboré une grande quantité des méthodes calculatoires pour l'interprétation des données « omics » et la définition des mécanismes moléculaires qui sont la base de ce phénotype. On peut diviser ces méthodes en deux catégories : (1) les méthodes de l'analyse des voies moléculaires définissant les voies moléculaires canoniques ou les listes annotées des composants biologiques qui sont sur-ou sous-représenté dans la liste rangée (ou dans la « hitliste »); (2) les méthodes de l'analyse de réseau qui utilisent les réseaux des interactions moléculaires comme l'information supplémentaire. La plupart des méthodes de l'analyse de réseau publiées définissent un sous-réseau qui est considérablement enrichi par les composants biologiques de la liste rangée (ou la « hit-liste »). Telle sous-réseau peut aider à définir des relations importantes entre les composants biologiques dans le contexte de ce système biologique.

Dans ce travail nous avons élaboré une nouvelle méthode de l'analyse de réseau à définir des membres possibles des voies moléculaires qui sont important pour ce phénotype en utilisant la « hit-liste » des expériences « omics » qui travaille dans le réseau intégré (le réseau comprend des interactions protéine-protéine, de transcription, l'acide ribonucléique micro-l'acide ribonucléique messager et celles métaboliques). La méthode tire des sous-réseaux qui sont construit des voies de quatre types les plus courtes (qui ne se composent des interactions protéine-protéine, ayant au minimum une interaction de transcription, ayant au minimum une interaction l'acide ribonucléique micro-l'acide ribonucléique messager, ayant au minimum une interaction métabolique) entre des hit -gènes et des soi-disant « exécuteurs terminaux » -les composants biologiques qui participent à la réalisation du phénotype finale (s'ils sont connus) ou entre les hitgènes (si « des exécuteurs terminaux » sont inconnus). La méthode calcule la valeur de la centralité de chaque point culminant et de chaque voie dans le sous-réseau comme la quantité des voies les plus courtes trouvées sur la route précédente et passant à travers le point culminant et la voie. L'importance statistique des valeurs de la centralité est estimée en comparaison avec des valeurs de la centralité dans les sous-réseaux construit des voies les plus courtes pour les hit-listes choisi occasionnellement. Il est supposé que les points culminant et les voies avec les valeurs de la centralité statistiquement signifiantes peuvent être examinés comme les membres possibles des voies moléculaires menant à ce phénotype. S'il y a des valeurs expérimentales et la P-valeur pour un grand nombre des points culminant dans le réseau, la méthode fait possible de calculer les valeurs expérimentales pour les voies (comme le moyen des valeurs expérimentales des points culminant sur la route) et les P-valeurs expérimentales (en utilisant la méthode de Fischer et des transpositions multiples). TABLE 2-

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Background

Functional genomics technologies

Three the most widespread functional genomics technologies are described below.

RNAi-based loss-of-function screening

This section is influenced by Bertil Daneholt's paper on RNA interference [START_REF] Daneholt | The discovery of RNA interference[END_REF].

The biological mechanism for repression of gene expression by causing the degradation of mRNA is exploited in the RNA interference(RNAi) based loss-of-function screening.

RNA interference (RNAi) was first detected at least in early 1980s although it was known by other names and the different findings were considered as unrelated processes. First, it was discovered that about 100 nucleotides RNA molecules can bind to a complementary mRNA sequence and inhibit translation in Escherichia coli in 1984 and 1994. Also, a phenomenon called gene (or RNA) silencing was shown in plants around 1990 in the works of [START_REF] Matzke | Reversible methylation and inactivation of marker genes in sequentially transformed tobacco plants[END_REF] and [START_REF] Wassenegger | RNA-directed De-Novo methylation of genomic sequences in plants[END_REF]. It was shown that the gene activity can be induced, stimulated and the expression of homologous sequences can be inhibited in transgenic plants with an incorporated transgene into the genome. The latter was called homology-dependent gene silencing. The inhibition of gene activity was reported at the transcriptional level (transcriptional gene silencing, TGS) [START_REF] Matzke | Reversible methylation and inactivation of marker genes in sequentially transformed tobacco plants[END_REF][START_REF] Wassenegger | RNA-directed De-Novo methylation of genomic sequences in plants[END_REF] and at the posttranscriptional level (posttranscriptional gene silencing, PTGS) [START_REF] Napoli | Introduction of a Chimeric Chalcone Synthase Gene into Petunia Results in Reversible Co-Suppression of Homologous Genes in trans[END_REF][START_REF] Van Der Krol | Flavonoid Genes in Petunia: Addition of a Limited Number of Gene Copies May Lead to a Suppression of Gene Expression[END_REF]. A process similar to PTGS called quelling was also observed in the fungus Neurospora crassa [START_REF] Romano | Quelling: transient inactivation of gene expression in Neurospora crassa by transformation with homologous sequences[END_REF]. Also, two groups reported about regulation of translation by antisense RNA in the nematode worm Caenorhabditis elegans in 1993 (Rosalind C. [START_REF] Lee | The C. elegans Heterochronic Gene lin-4 Encodes Small RNAs with Antisense Complementarity to lin-14[END_REF][START_REF] Wightman | Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans[END_REF]. A second example of a small regulatory RNA -the 21-nucleotide let-7 RNA was found in Caenorhabditis elegans [START_REF] Reinhart | The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans[END_REF] which was also present in other species was reported in 2000.

The immense potential of all these finding became evident after Andrew Fire and Craig Mello published their break-through study in 1998 [START_REF] Fire | Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans[END_REF] and a large number of small RNA molecules, called microRNAs (miRNAs), were revealed in 2001 [START_REF] Lagos-Quintana | Identification of novel genes Coding for RNAs of Small expressed RNAs[END_REF][START_REF] Lau | An abundant class of tiny RNAs with probably regulatory roles in Caenorhabditis elegans[END_REF][START_REF] Lee | An extensive class of small RNAs in Caenorhabditis elegans[END_REF].

Andrew Fire and Craig Mello studied the phenotypic effect of sense, antisense and annealed sense/antisense RNA introduced into the nematode worm Caenorhabditis elegans in their paper. They showed that only introduction of the annealed sense/antisense RNA could cause the predicted phenotype and led to an efficient degradation of the target mRNA. The main results of the paper can be summarized as follows (cited from [START_REF] Daneholt | The discovery of RNA interference[END_REF]:

First, silencing was triggered efficiently by injected double-stranded RNA (dsRNA), but weakly or not at all by sense or antisense single-stranded RNAs. Second, silencing was specific for an mRNA homologous to the dsRNA; other mRNAs were unaffected. Third, the dsRNA had to correspond to the mature mRNA sequence; neither intron nor promoter sequences triggered a response. This indicated a posttranscriptional, presumably cytoplasmic mechanism. Fourth, the targeted mRNA disappeared suggesting that it was degraded. Fifth, only a few dsRNA molecules per cell were sufficient to accomplish full silencing. This indicated that the dsRNA was amplified and/or acted catalytically rather than stoichiometrically. Sixth, the dsRNA effect could spread between tissues and even to the progeny, suggesting a transmission of the effect between cells. Furthermore, Fire and Mello made the remark that RNAi could provide an explanation for a phenomenon studied in plants for several years: posttranscriptional gene silencing (PTGS). Finally, they ended their paper by speculating about the possibility that "dsRNA could be used by the organism for physiological gene silencing". Moreover, Fire provided evidences that dsRNAs target mRNA before translation and suggested that the RNAi mechanism could be a form of defense system to viral particles in lower organisms in his next paper published also in 1998 [START_REF] Montgomery | RNA as a target of double-stranded RNAmediated genetic interference in Caenorhabditis elegans[END_REF].

The presence of the RNAi mechanism was shown in other organisms, including fruit flies, trypanosomes, plants, planaria, hydra and zebrafish [START_REF] Tuschl | Targeted mRNA degradation by double-stranded RNA in vitro[END_REF] very rapidly after these publications. However, specific dsRNA-mediated mRNA degradation by RNAi was not detected in commonly used mammalian cell cultures first. But later it was archived by introducing very short 21-nucleotide dsRNAs [START_REF] Elbashir | Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells[END_REF].

Next, it was shown that dsRNA is cleaved into 21-23 nucleotide long siRNAs (small interfering RNA) and it was proposed that these siRNAs work as guide to cleave mRNA [START_REF] Zamore | RNAi: double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals[END_REF]. The process was detected in vivo in Caenorhabditis elegans in 2000: it was observed that dsRNAs are cleaved into about 25-nucleotide RNAs and that antisense RNAs trigger specific dsRNA-mediated mRNA degradation via base-pairing to mRNA [START_REF] Parrish | Functional anatomy of a dsRNA trigger: differential requirement for the two trigger strands in RNA interference[END_REF].

Later it was demonstrated in Drosophila cells that a large protein complex called RISC (RNAinduced silencing complex) is guided to the mRNA by a short antisense RNA and after that mRNA is cleaved and degraded [START_REF] Hammond | An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells[END_REF]. The protein complex RISC contained at least one member of the argonaute protein family and it was suggested that it acted as an endonuclease. It was also shown that a ribonuclease III-like nuclease, called Dicer, trimmed dsRNA to form short RNAs [START_REF] Bernstein | Role for bidentate ribnuclease in the initiation site of RNA interference[END_REF] (Summarized in figure 1-1).

Meanwhile RNAi pathway for miRNAs was also studied. It was found that in contrary to exogenous nature of siRNAs, most miRNA are transcribed from independent transcription units [START_REF] Lagos-Quintana | Identification of novel genes Coding for RNAs of Small expressed RNAs[END_REF][START_REF] Lau | An abundant class of tiny RNAs with probably regulatory roles in Caenorhabditis elegans[END_REF][START_REF] Lee | An extensive class of small RNAs in Caenorhabditis elegans[END_REF] or from the introns of pre-mRNAs (about a quarter of the human miRNA genes). miRNAs are transcribed into pri-miRNAs (Y. [START_REF] Lee | MicroRNA maturation: stepwisee processing and subcellular localization[END_REF], that are cleaved by a protein complex called Drosha/Pasha into miRNA precursor molecules called pre-miRNAs (Y. [START_REF] Lee | MicroRNA maturation: stepwisee processing and subcellular localization[END_REF][START_REF] Zeng | Sequence requirements for micro RNA processing and function in human cells[END_REF].

pre-miRNAs as shRNAs are transported into the cytoplasm by a mechanism involving Ran-GTP protein and the export receptor Exportin-5 [START_REF] Lund | Nuclear Export of MicroRNA[END_REF][START_REF] Yi | Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs[END_REF]. pre-miRNAs as shRNAs are processed by the enzyme Dicer (Y. [START_REF] Lee | The nuclear RNase III Drosha initiates microRNA processing[END_REF] which cleaves shRNAs into mature siRNAs and pre-miRNAs into miRNAs. siRNA and miRNA single strands are incorporated into RISC complexes and guide the complex to target mRNAs (Elbashir, Lendeckel, et al., 2001;[START_REF] Vents Elbashir | Functional anatomy of siRNAs for mediating efficient RNAi in Drosophila melanogaster embryo lysate[END_REF][START_REF] Hammond | An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells[END_REF][START_REF] Martinez | Single-stranded antisense siRNAs guide target RNA cleavage in RNAi[END_REF]. The fate of the targeted mRNA depends on the extent of sequence pairing and it can be cleaved, destroyed or translation can be inhibited [START_REF] Bartel | MicroRNAs: Genomics, Biogenesis, Mechanism, and Function[END_REF] Reprinted with permission from [START_REF] Sigoillot | Vigilance and Validation: Keys to Success in RNAi Screening[END_REF].

The importance of RNAi discovery is difficult to overestimate. One of the most significant consequences of the discovery was that the sequence specific action of RNAi immediately suggested that it can be used to suppress specific genes and study the resulting phenotype.

Initially it was used to silence individual genes of interest but the development of libraries for several organisms that contain synthesized sequence specific dsRNAs targeting most of the genes in the genome allowed to perform genome wide loss of function screening.

Different types of dsRNAs such as siRNAs, endoribonuclease-prepared siRNAs (esiRNAs) or siRNA precursors (such as short hairpin RNAs (shRNAs) or long double-stranded RNAs (dsRNAs) are used to perform screening. Typically, small interfering siRNAs or shRNA are used for mammalian cells (S. E. [START_REF] Mohr | RNAi screening comes of age: improved techniques and complementary approaches[END_REF]. Targeting non-coding RNA became also possible with development of libraries of reagents that inhibit or mimic microRNAs (S. E. [START_REF] Mohr | RNAi screening comes of age: improved techniques and complementary approaches[END_REF].

The way RNAi reagents are introduced into the cells differ for different cell types and reagents: siRNAs or esiRNAs can be transfected into cells; shRNAs can be virally transduced into cells; dsRNAs in solution can be applied to D. melanogaster cells; dsRNAs (fruit fly or Caenorhabditis elegans) or shRNAs (fruit fly or mice) can be expressed from transgenic constructs; dsRNAs can be microinjected (Caenorhabditis elegans, fruit fly and some nonmodel insects); E. coli expressing dsRNAs can be fed to model organisms (Caenorhabditis elegans or planaria) (S. E. [START_REF] Mohr | RNAi screening comes of age: improved techniques and complementary approaches[END_REF].

Typical components of a genome wide RNAi loss of function screening are (as described by [START_REF] Boutros | The art and design of genetic screens: RNA interference[END_REF]:

Developing readout assay (primary screening assay) -this step includes designing a readout assay specific for the biological process of interest, choosing positive and negative controls "to achieve high signal with the positive controls and low noise with the negative controls" and developing scoring strategy which can be either qualitative or quantitative. The readout assay for genome wide screening is often a compromise between specificity and feasibility.

As examples of readout assay, one could mention simple visual assays of morphological changes, changes in the expression of GFP reporters, or imaging assays for cell culture-based screens which capture an image of each well and then each individual cell is scored with several phenotypic descriptors.

The pilot screening -this step includes a small-scale screening of a few hundred random genes, positive and negative controls. The pilot screen is needed to adjust RNAi reagents doses, incubation times, other experimental variables; to optimize readout assay responses to negative and positive control reagents; to assess the feasibility to perform the screen on a large scale [START_REF] Sharma | RNAi screening: tips and techniques[END_REF].

The genome-wide screening -this step includes genome-wide screening which is usually conducted in duplicates or more with several non-overlapping RNAi reagents per gene. This helps to avoid false positives caused by off-target effects and to increase confidence in true positives.

A cell-culture based RNAi screening is typically performed in either arrayed or pooled format. The library is randomly introduced into cells in the pooled format screening such that any given cell will contain approximately one gene-specific RNAi reagent. A library containing viral-encoded shRNAs is typically used for mammalian cells. Then the cells can be selected for presenting a certain feature (e.g. resistance to some treatment, being positive in the assay). The polymerase chain reaction (PCR) is used to amplify RNAi reagent present in the selected cells and sequencing is used to determine the RNAi reagents. The presence of the RNAi reagent after selection suggests the corresponding gene is a hit (S. [START_REF] Mohr | Genomic Screening with RNAi: Results and Challenges[END_REF].

Another option to perform selection is to use several subsets of cells or even different cell types that can be treated differently to create a "reference set" and one or more "experimental sets" (S. [START_REF] Mohr | Genomic Screening with RNAi: Results and Challenges[END_REF]. It can be done before or after introducing RNAi library into the cells depending in the readout assay. PCR amplification and microarray analysis or sequencing are used then to detect which RNAi reagents are present in each set (e.g. using molecular "barcode"). This gives the information about which RNAi reagents are present and which quantities in the experimental and reference sets (S. [START_REF] Mohr | Genomic Screening with RNAi: Results and Challenges[END_REF].

In contrary to the pooled format, each gene is targeted separately by RNAi reagents in individual wells of a microtiter plate (that contain e.g. 96 or 384 wells) in arrayed format.

The readout assays are usually use colorimetric, fluorescence, or luminescent measurements at the well level or fluorescent measurements at the cellular or subcellular level using imaging (S. [START_REF] Mohr | Genomic Screening with RNAi: Results and Challenges[END_REF]. Genome scale RNAi arrayed screenings use automated equipment for liquid handling and readout assays.

Secondary validation -this step includes secondary screening which validates positives from the genome-wide screening. This can be archived by using multiple RNAi reagents distinct from the genome-wide screening, improving specificity of the readout assay, developing a readout assay for testing for nonspecificity, using different biological system or using RNAi independent techniques. Data analysis -this step includes raw data analysis from the genome-wide screening and secondary validation. The pipeline is specific to the screening and readout assay technologies, but typically includes data preprocessing, quality control, removal of spatial biases per plate and normalization between plates for arrayed format, microarray or sequencing analysis for pooled format, creating a ranked list of genes with experimental measurements and detection of statistically significant hits. Software packages such as cellHTS [START_REF] Boutros | Analysis of cell-based RNAi screens[END_REF], RNAither [START_REF] Rieber | RNAither, an automated pipeline for the statistical analysis of high-throughput RNAi screens[END_REF], CARD [START_REF] Dutta | An interactive web-based application for Comprehensive Analysis of RNAiscreen Data[END_REF], shALIGN and shRNAseq [START_REF] Sims | High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing[END_REF], edgeR (Y. [START_REF] Chen | edgeR : differential expression analysis of digital gene expression data User ' s Guide[END_REF] which implement the pipeline are available.

One important point should be mentioned about RNAi loss-of-function screening. Soon after completion of the first genome-wide RNAi screenings, it became evident that many genomewide RNAi screenings hits were false positives due to off-target effects (OTEs) [START_REF] Birmingham | 3' UTR seed matches, but not overall identity, are associated with RNAi off-targets[END_REF][START_REF] Jackson | Expression profiling reveals off-target gene regulation by RNAi[END_REF][START_REF] Mohr | Genomic Screening with RNAi: Results and Challenges[END_REF]. These OTEs can be grouped into two main categories: sequence-independent off-target effects and sequence-specific off-target effects [START_REF] Sigoillot | Vigilance and Validation: Keys to Success in RNAi Screening[END_REF]. The main causes sequence-independent off-target effects are (1) shRNAs can interfere with processing mechanism for microRNAs [START_REF] Grimm | Fatality in mice due to oversaturation of cellular microRNA/short hairpin RNA pathways[END_REF]; (2) siRNAs can displace microRNAs from RISC complex [START_REF] Khan | Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs[END_REF]; (3) non-specific immune response can be induced after introduction of synthetic RNAi reagents [START_REF] Bridge | Induction of an interferon response by RNAi vectors in mammalian cells[END_REF][START_REF] Sledz | Activation of the interferon system by short-interfering RNAs[END_REF]; (4) high concentration of siRNAs can induce cell-stress response [START_REF] Persengiev | Nonspecific, concentration-dependent stimulation and repression of mammalian gene expression by small interfering RNAs (siRNAs)[END_REF][START_REF] Semizarov | Specificity of short interfering RNA determined through gene expression signatures[END_REF]. The main cause of sequence-specific offtarget effects is the potential of each individual RNAi reagent to act like a miRNA and to downregulate the expression of several genes, when besides inducing the cleavage of the target mRNA with perfect base-pairing, the RNAi reagent can act as miRNA by interacting with target mRNA with incomplete base-pairing inside a short seed region [START_REF] Carthew | Identifying protein interaction subnetworks by a bagging Markov random field-based method[END_REF][START_REF] Doench | siRNAs can function as miRNAs[END_REF]. Several computational and experimental approaches were developed to address this issue, including computational prediction of potential OTEs using seed sequence alignments (e.g. Genome-wide Enrichment of Seed Sequence matches (GESS) [START_REF] Sigoillot | A Bioinformatics Method Identifies Prominent Off-targeted Transcripts in RNAi Screens[END_REF]; designing RNAi reagents that will result in fewer OTEs (e.g. using as control altered RNAi reagent sequence to prevent perfect complementarity [START_REF] Buehler | siRNA off-target effects in genome-wide screens identify signaling pathway members[END_REF]; using multiple RNAi reagents with different sequence per gene [START_REF] Bassik | Rapid Creation and Quantitative Monitoring of High Coverage shRNA Libraries[END_REF][START_REF] Mohr | RNAi screening comes of age: improved techniques and complementary approaches[END_REF].

CRISPR/Cas9-based loss-of-function screening

CRISPR/Cas system is bacteria and archaea adaptive immunity mechanism [START_REF] Jinek | A Programmable Dual-RNA-Guided DNA Endonuclease in Adaptive Bacterial Immunity[END_REF]. The first description of repeat structures that would later be called CRISPR was published in 1987 by a group of Japanese scientists from Osaka university who cloned by accident iap gene from the E. Coli genome with a part of CRISPR locus. These repeats consisted of a series of 29-nucleotide repeated sequence separated by unique 32-nucleotide 'spacer' sequences [START_REF] Ishino | Nucleotide sequence of the iap gene, responsible for alkaline phosphatase isozyme conversion in Escherichia coli, and identification of the gene product[END_REF], however its function was not known at that time. A group from Netherlands published two papers about similar repeat clusters in Mycobacterium tuberculosis genome in 1993 [START_REF] Groenen | Nature of DNA polymorphism in the direct repeat cluster of Mycobacterium tuberculosis; application for strain differentiation by a novel typing method[END_REF][START_REF] Van Soolingen | Comparison of various repetitive DNA elements as genetic markers for strain differentiation and epidemiology of Mycobacterium tuberculosis[END_REF]. A cluster of interrupted direct repeats (DR) was found and the diversity of the DRintervening spacers was studied in different strains of Mycobacterium tuberculosis in these papers. At the same time in 1993, similar repeated structures were observed by researchers from Spain in archaeal microbe Haloferax mediterranei (F. J.M. [START_REF] Mojica | Transcription at different salinities of Haloferax mediterranei sequences adjacent to partially modified PstI sites[END_REF] and first hypotheses for the function of these structures were proposed (it was proposed that these repeats could have a role in partitioning of the replicated DNA) (F. J M [START_REF] Mojica | Long stretches of short tandem repeats are present in the largest replicons of the Archaea Haloferax mediterranei and Haloferax volcanii and could be involved in replicon partitioning[END_REF]. Similar repeat clusters were recognized using bioinformatics methods in many genomes of Archaea and Bacteria in 2000 and 2002 and were named clustered regularly interspaced palindromic repeats (CRISPR) [START_REF] Jansen | Identification of genes that are associated with DNA repeats in prokaryotes[END_REF][START_REF] Mojica | Biological significance of a family of regularly spaced repeats in the genomes of Archaea, Bacteria and mitochondria[END_REF]. It was also reported that CRISPR was accompanied by a set of homologous genes that were called cas genes (CRISPR-associated systems) [START_REF] Jansen | Identification of genes that are associated with DNA repeats in prokaryotes[END_REF]. Helicase and nuclease motifs were identified in the Cas proteins what suggested that these proteins might be involved into organizing the structure of the CRISPR loci [START_REF] Jansen | Identification of genes that are associated with DNA repeats in prokaryotes[END_REF]. Three independent groups showed that some CRISPR spacers matched phage and extrachromosomal DNA in 2005 [START_REF] Bolotin | Clustered regularly interspaced short palindrome repeats (CRISPRs) have spacers of extrachromosomal origin[END_REF][START_REF] Mojica | Intervening sequences of regularly spaced prokaryotic repeats derive from foreign genetic elements[END_REF][START_REF] Pourcel | CRISPR elements in Yersinia pestis acquire new repeats by preferential uptake of bacteriophage DNA, and provide additional tools for evolutionary studies[END_REF] which suggested that CRISPR loci encoded an adaptive immune system mechanism to protect bacteria and archaea. The first experimental evidence that proofed this suggestion was published in 2007 [START_REF] Barrangou | CRISPR Provides Acquired Resistance Against Viruses in Prokaryotes[END_REF]. It was shown that the CRISPR loci of the phage resistance strains of Streptococcus thermophilus contained phage-derived sequences. The number of acquired spacers correlated with the increased resistance. It was also shown that bacteria needed Cas7 protein to gain phage resistance suggesting that Cas7 was important for spacer acquisition. On the other hand, Cas9 protein was necessary for preventing phage infection.

Later, it was observed that a complex of Cas proteins cleaved CRISPR RNA that is transcribed from the CRISPR locus into spacer-containing RNA molecules (crRNA) [START_REF] Brouns | Small CRISPR RNAs Guide Antiviral Defense in Prokaryotes[END_REF]. In the same year, it was also observed that CRISPR targeted DNA rather than RNA to prevent conjugation in Staphylococcus epidermidis (Marraffini et al., 2008) which was confirmed two years later by a study that gave direct experimental evidences that CRISPR-Cas system cleaves both strands of plasmid DNA in S. thermophiles [START_REF] Garneau | The CRISPR/cas bacterial immune system cleaves bacteriophage and plasmid DNA[END_REF]. The latter study also showed that the Cas9 nuclease cuts DNA at sequence specific positions encoded by crRNAs. Two more years later, Jinek et al. [START_REF] Jinek | A Programmable Dual-RNA-Guided DNA Endonuclease in Adaptive Bacterial Immunity[END_REF] reported that the core CRISPR/Cas mechanism in S. pyogenes is based on a dual-RNA structure that consists of trans-activating crRNA (tracrRNA) and precursor crRNA hybridized together. This hybrid guides Cas9 endonuclease to introduce sequence specific double-stranded breaks in target DNA.

Nowadays the CRISPR defense system mechanism is divided into three stages [START_REF] Crispr Makarova | Evolution and classification of the CRISPR-Cas systems[END_REF] It is known that ~40% of sequenced bacterial genomes and ~90% of sequenced archaeal genomes contain at least one CRISPR locus encoding CRISPR/Cas system [START_REF] Grissa | The CRISPRdb database and tools to display CRISPRs and to generate dictionaries of spacers and repeats[END_REF].

The current formal classification [START_REF] Makarova | CRISPR Interference Limits Horizontal Gene Transfer in Staphylococci by Targeting DNA[END_REF] Type I and type III CRISPR-Cas systems are distinguished by the presence of the signature genes cas3 and cas10 respectively. The multiprotein crRNA effector complexes mediate the processing and interference stages of the CRISPR defense system [START_REF] Makarova | CRISPR Interference Limits Horizontal Gene Transfer in Staphylococci by Targeting DNA[END_REF]. In type I systems, this complex is known as the CRISPR-associated complex for antiviral defense (Cascade) complex. In type III-A and type III-B systems the complexes are known as Csm and Cmr complexes respectively [START_REF] Makarova | CRISPR Interference Limits Horizontal Gene Transfer in Staphylococci by Targeting DNA[END_REF]. Type IV systems are rare and lack proteins responsible for adaptation stage.

Class 2 CRISPR-Cas systems are defined by the presence of a single subunit crRNA effector complex. This class includes type II and V CRISPR-Cas systems.

Type II CRISPR-Cas system is distinguished by the presence of cas9 gene which combines the functions of the crRNA-effector complex and is the only protein that is required to perform DNA cleavage. Cas9 protein is also takes part in spacer adaptation and in processing of pre-crRNA. Besides pre-crRNA, another RNA known as tracrRNA is transcribed from the repeat region of CRISPR locus. tracrRNA forms dsRNA with pre-crRNA in the repeat region via basepairing. The dsRNA is targeted by the housekeeping dsRNA-specific ribonuclease RNase III but not Cas protein in the presence of the Cas9 protein to produce crRNAs [START_REF] Hale | RNA-Guided RNA Cleavage by a CRISPR RNA-Cas Protein Complex[END_REF]. Type V CRISPR-Cas system is distinguished by the presence of cas12 gene which combines the functions of the crRNA-effector complex. This type did not require the additional tracrRNA for target cleavage unlike type II [START_REF] Koonin | Diversity, classification and evolution of CRISPR-Cas systems[END_REF].

After Marraffini and Sontheimer's 2008 paper that showed that CRISPR targeted and cut DNA in crRNA guided manner, big work devoted to adaptation of the mechanism to cutting, and as result, editing specific genomic loci had started, at the same time with continuation of investigation of basic biology of CRISPR-Cas defense system mechanism.

Two molecular techniques were mainly in use to edit eukaryotic DNA in a sequence specific manner within the cell at that time: zinc finger nucleases (ZFNs) and transcription activatorlike effector nucleases (TALENs) by 2008. Zinc finger nuclease is an artificial fusion of DNAbinding domain of eukaryotic transcription factors (zinc finger proteins, which consist of several zinc finger motifs each recognizing approximately 3 bp of DNA) with a DNAcleavage domain of the FokI restriction enzyme [START_REF] Urnov | Genome editing with engineered zinc finger nucleases[END_REF]. Transcription activator-like effector nuclease is an artificial fusion of DNA binding domain of transcription activator-like effectors (TALE) proteins (these proteins contain nearly identical 34-aminoacid repeats with only two variable amino acids per repeat, each repeat recognize a single base pair in target DNA) [START_REF] Boch | TALEs of genome targeting[END_REF] from the pathogenic bacterium Xanthomonas with a DNA-cleavage domain of the FokI restriction enzyme.

These sequence-specific nucleases introduce double stranded breaks in the DNA and promotes primarily either homology-directed repair (HDR) pathway in the presence of a template DNA that contain the mutation of interest, thereby archiving site specific mutagenesis, or non-homologous end-joining (NHEJ) pathway without a template DNA to disrupt a target gene. The latter pathway is an error-prone repair mechanism that can introduce short indel mutations at repair sites which cause a coding frameshift, resulting in a premature stop codon, as well as an early frameshift mutation or large indels resulting in a non-functional protein [START_REF] Shalem | High-throughput functional genomics using[END_REF].

Both approaches require engineering of new proteins for each target sequence which is hardly feasible for large scale experiments. On the other hand, type II CRISPR/Cas system required only short crRNA: tracrRNA hybrid and Cas9 protein to introduce sequence-specific double stranded breaks and it would provide a versatile tool for cutting/editing of DNA if it could be made to work in mammalian cells. It took several years to make it work. The first papers reporting that CRISPR/Cas9 system from Streptococcus pyogenes engineered in zebrafish embryos, human and mouse cells can induce targeted genetic modifications via NHEJ or HDR-mediated pathways appeared in 2013 [START_REF] Cong | Multiplex Genome Engineering Using CRISPR/VCas Systems[END_REF][START_REF] Hwang | Efficient In Vivo Genome Editing Using RNA-Guided Nucleases Woong[END_REF]Mali et al., 2013). Since these initials studies, many laboratories used CRISPR/Cas9 system for genome editing applications including developing and construction of targeting reagents for functional screenings.

The field of functional screenings using CRISPR/Cas9 reagents is rapidly developing.

According to literature, the screening methodology consists of several steps [START_REF] Miles | Design, execution, and analysis of pooled in vitro CRISPR/Cas9 screens[END_REF][START_REF] Shalem | High-throughput functional genomics using[END_REF]:

Choosing CRISPR library CRISPR knockout libraries are the most common libraries for CRISPR/Cas9 functional screenings. They are available as pooled plasmid libraries (see screening format section below), containing either two separate plasmids for cas9 and sgRNA or a single plasmid with both cas9 and sgRNA. Genome-wide knockout libraries contained sgRNAs targeting over 18 000 genes and about 1000 miRNAs by 2016.

CRISPR-based interference (CRISPRi) libraries contain sgRNAs with catalytically dead Cas9 (dCas9) protein that does not cause permanent DNA modification but repress transcription via blocking RNA polymerase or via effector domain if fused with repressive effector domain like Kruppel-associated box (KRAB).

CRISPR gene activation (CRISPRa) libraries also contain sgRNAs with dCas9, but this time dCas9 is fused with activation domain proteins [START_REF] Maeder | CRISPR RNAguided activation of endogenous human genes[END_REF][START_REF] Perez-Pinera | Transcription Factors[END_REF], causing an increase in gene expression level.

Choosing cell line

The main considerations for choosing a cell line for CRISPR/Cas9 functional screening are ploidy (or more precisely gene copy number) and state of the DNA repair pathways. The first one is important because complete gene knockout is easier to archive when there are few gene copies in the genome. The latter is important because if both HDR and NHEJ pathways are active in the cell line, the DSB can be repaired via HDR using sister chromatid as a template, so the probability of a complete gene knockout is lower than for cell lines with defective HDR.

Choosing screening format

In general, both arrayed and pooled screening formats can be used for CRISPR/Cas9 functional screening. However, all 84 high-throughput screenings performed in human cell lines submitted to GenomeCRISPR database by October 2016 were pooled screenings [START_REF] Rauscher | GenomeCRISPR -a database for high-throughput CRISPR/Cas9 screens[END_REF]. One possible explanation for this is that pooled format screenings are less expensive and labor intensive; moreover, screening reagents for pooled formats are easier to produce [START_REF] Shalem | High-throughput functional genomics using[END_REF]. However as mentioned for RNAi screenings, pooled formats are limited to simple phenotype readout assays (as cell proliferation or survival) [START_REF] Shalem | High-throughput functional genomics using[END_REF].

The selection of the cells is performed after viral delivery in pooled screening. The goal of positive selection is to leave after selection only cells with survival-enhancing perturbation.

The goal of the negative selection is to leave after selection only cells that could survive despite the perturbation.

The DNA is extracted, and PCR amplification is used to prepare samples for sequencing after selection.

Data analysis

The data analysis includes sequencing data processing, quality control, quantification of representation of each sgRNA, determination of the statistical significance of the changes in sgRNA representation and hit selection [START_REF] Miles | Design, execution, and analysis of pooled in vitro CRISPR/Cas9 screens[END_REF]. The publicly available tools like edgeR (Y. [START_REF] Chen | edgeR : differential expression analysis of digital gene expression data User ' s Guide[END_REF] or MAGeCK [START_REF] Li | MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens[END_REF] can be used to perform each step.

Validation

The goal of validation is to separate true positive hits from false positive screens. This can be done in a manner similar to RNAi screenings (secondary screening, orthogonal assay), however additional validation that the target gene was modified by Cas9 dependent DSB (for knockout libraries) or that transcription level changed due CRISPRi/CRISPRa methods should be performed.

Transcriptome profiling

Transcriptome profiling allows to study a cellular system at the transcriptome level.

Transcriptome is the set of all RNA molecules in a particular cell. The most widespread techniques for transcriptome profiling are DNA microarrays and RNA-seq techniques [START_REF] Lowe | Transcriptomics technologies[END_REF].

DNA microarray consists of a solid surface with thousands of microscopic spots attached to it. These spots contain probes, short specific (usually 25-60bp) DNA sequences, to which target fluorescently labeled complementary DNA (cDNA) hybridize via complementary base pairing between two DNA strands. Higher number of complementary bases means stronger bond between strands, that can stand washing step, when weaker non-specific bonding sequences are removed. The fluorescent label of cDNA generates signal for each spot. The strength of the signal depends on the amount of the cDNA bound to the probes. Comparing the strength of the signals from the same spot in two different conditions (e.g. control and experiment) microarrays allow to quantify relative changes in expression levels.

The two main types DNA microarrays from manufacturing point of view are spotted and insitu microarrays. The probes for spotted microarrays are synthesized and stored in wells before placing on the array [START_REF] Derisi | Use of a cDNA microarray to analyse gene expression patterns in human cancer[END_REF]. Then they are placed at certain location using a robotic arm and spotting pins. This manufacturing technique allows to produce "in house" microarrays that can be highly customizable, since the set of probes can be specified for a particular experiment. On the other hand for in-situ DNA microarrays, probes are synthesized directly on the array surface, using for example photolithographic synthesis technique [START_REF] Fodor | Light-Directed, Spatially Addressable Parallel Chemical Synthesis[END_REF]. This technique relies on light-sensitive masking agents and one nucleotide is added at a time to applicable probes over the whole array. The applicable probes are unmasked using UV light allowing attachment of nucleotide from a solution of a single nucleotide.

DNA microarrays can be used for two-channel or one-channel detection. Two-channel microarrays can be hybridized with two samples simultaneously (e.g. control and experiment). The samples are labeled with two different fluorescent dyes which has different fluorescence emission wavelengths. The microarray scanner excites the dyes with specific wavelengths and quantifies the relative intensities. The one-channel microarray can be hybridized with only one sample, so comparison between two samples requires two separate hybridizations.

A typical workflow of a DNA microarray experiment for measuring expression changes is:

1) Sample preparation. This step includes acquiring samples, extraction of the RNA, generating cDNA via reverse transcription, PCR amplification, labelling with fluorescent dye and mixing with hybridization solution.

2) Hybridization and washes. This step includes adding the mixture to the array, hybridization, washing away non-specific bonding sequences and drying.

3) Scanning. This step includes scanning the array with laser beam that excite fluorescent dye and quantifying the intensities of each spot. 4) Data analysis. This step includes normalization, quality control, filtering (spots with low intensity, noisy replicates, missing values) and statistical analysis. This can be done either by creating custom data analysis pipeline (e.g. using BioConductor [START_REF] Gentleman | Bioconductor: open software development for computational biology and bioinformatics[END_REF] packages as affy [START_REF] Gautier | Affy -Analysis of Affymetrix GeneChip data at the probe level[END_REF], affyPLM) or using integrated tools such as EXPANDER [START_REF] Shamir | EXPANDER--an integrative program suite for microarray data analysis[END_REF] or Affymetrix Power Tools (APT).

RNA-seq refers to the combination of a high-throughput sequencing techniques with computational methods to measure the presence of transcripts in an RNA extract [START_REF] Lowe | Transcriptomics technologies[END_REF].

A typical workflow of a RNA-seq experiment for measuring expression changes is:

1) Library preparation. During this step RNA is isolated from the cells. The isolated can be filtered to include only mRNA or ribosomal RNA (rRNA) can be depleted. The RNA is converted to cDNA using reverse transcription and each fragment can also be amplified.

2) Sequencing. During this step the fragments are sequenced.

3) Data analysis. This step includes quality control of the raw sequences, alignment to reference genome (if known) or de novo assembly (if reference genome is unknown), quantification of transcripts expression and differential analysis between samples. These steps can be performed using such software tools as RNA workbench [START_REF] Grüning | The RNA workbench: Best practices for RNA and high-throughput sequencing bioinformatics in Galaxy[END_REF] or RNA-seq workflow in Bioconductor [START_REF] Love | RNA-Seq workflow: gene-level exploratory analysis and differential expression[END_REF]

Computational analysis of functional genomics data

The result of functional genomics experiment is usually used either as a ranked list of biological components (genes, mRNAs, ..) where each gene is associated with an experimental score and a p-value or as a 'hit list' which contains only biological components with scores and p-values list with the scores resp. p-values that are above resp. below a certain threshold level. Numerous computational methods to interpret functional screening data and infer molecular machinery underlying the given phenotype can be grouped into two categories. The most widespread approaches can be grouped into two categories.

The first category is the pathway analysis methods that identify which canonical pathways or annotated gene sets are over-or underrepresented in a ranked list or a hit list. According to [START_REF] Khatri | 10 Years of Pathway Analysis : Current Approaches and Outstanding Challenges -Table S2[END_REF], three generations of the pathway analysis approaches can be distinguished.

The first generation is over representation analysis (ORA) approaches which can be still considered as the gold standard approach for analysis of results of functional screenings.

Typically, such methods include the following steps: 1) counting number of genes in the input list that belong to each pathway;

2) testing each pathway for over-or underrepresentation in the input list using statistical test such as hypergeometric, Fisher's exact, chi-square, or binomial tests.

The major limitations of such approaches are: 1) treating each gene in the input list equally, disregarding the experimental measurements;

2) using typically only hit list, disregarding other genes in the ranked list;

3) disregarding possible dependency between genes; 4) disregarding possible overlap between pathways. Some examples of methods that implement this approach are ClueGO [START_REF] Bindea | ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks[END_REF], GOstat [START_REF] Beißbarth | GOstat: Find statistically overrepresented Gene Ontologies with a group of genes[END_REF], Enrichr [START_REF] Kuleshov | Enrichr: a comprehensive gene set enrichment analysis web server 2016 update[END_REF], David [START_REF] Huang | Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[END_REF] and iGA [START_REF] Breitling | Iterative Group Analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments[END_REF] tools.

ClueGO [START_REF] Bindea | ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks[END_REF]) is a Cytoscape [START_REF] Shannon | Cytoscape: A software Environment for integrated models of biomolecular interaction networks[END_REF] plugin which facilitates visualization, annotation, creation annotation networks and performing over representation analysis. It uses one-or two-sided tests based on the hypergeometric distribution and Gene Ontology(GO) terms as well as KEGG and BioCarta pathways databases as annotated gene sets.

Enrichr, David, GOstat are web based tools that uses either Fisher's exact test or chi-square tests to test overrepresentation.

Iterative Group Analysis (iGA) [START_REF] Breitling | Iterative Group Analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments[END_REF] tool addresses the problem of using only the hit genes and disregarding others by using iterative approach. The tool uses ranked input list, assigns each gene to a class, e.g. based on GO term and determines the probability of change of each class by adding a gene at a time and finding the minimum probability to observe this many members of the class in the top of the list by chance [].

The second generation is Functional Class Scoring (FCS) approaches which aim at detecting coordinated impact of genes on a pathway. Typically, such methods include the following steps:

1) computing single pathway-level statistics using gene level statistics such as the Kolmogorov-Smirnov statistics, sum, mean of gene-level statistic;

2) testing the significance of the pathway-level statistics e.g. using phenotype or pathway permutation.

These approaches address the following limitations of the ORA approaches: 1) use experimental measurements;

2) use information from the whole ranked list;

3) take into account possible dependency between genes.

The major limitations of such approaches are: 1) treat each pathway independently;

2) usually consider only ranking but not exact values of experimental measurements.

Examples: GSEA [START_REF] Subramanian | Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles[END_REF], Enrichr [START_REF] Kuleshov | Enrichr: a comprehensive gene set enrichment analysis web server 2016 update[END_REF] tools.

Gene Set Enrichment Analysis (GSEA) is the first and one of the most popular tool implementing FSC approach. GSEA tool takes as an input a ranked list of genes and determines whether the members of a canonical pathway (or any other annotated gene set) are randomly distributed throughout the input list or mainly found in the top of the input list [START_REF] Subramanian | Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles[END_REF].

The three main steps of GSEA according to [START_REF] Subramanian | Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles[END_REF] are:

1) Calculation of an Enrichment Score (ES) for a pathway (or any other annotated gene set). This is done by starting in the top of the input list, moving down in the list increasing the running sum statistics if current gene belong to the pathway and decreasing otherwise. The increment is weighted using experimental measurements.

ES is calculated as maximum of the sum across all genes in the input genes and it corresponds to a weighted Kolmogorov-Smirnov-like statistic.

2) Testing the significance of ES is done by permuting phenotype labels in the input list, calculating ES for permuted data and constructing distribution for ES. The p-value for the ES from step 1 is calculated using this distribution.

3) Adjusting p-value from step 3 for multiple hypothesis testing (if many canonical pathways are tested). This is done by normalizing each ES by the size of the pathway and computing permutation based False Discovery Rates (FDR).

Enrichr [START_REF] Kuleshov | Enrichr: a comprehensive gene set enrichment analysis web server 2016 update[END_REF] is another popular tool. Enrichr contains 125 frequently updated gene set libraries (divided into categories: transcription, pathways, ontologies, diseases/drugs, cell types and miscellaneous) and implements ORA using Fisher exact test as well as FSC method. The latter method takes as an input list of genes with coefficients from 0 to 1 that represent 'the grade of membership'. The 'grade of membership' for a gene set is defined as a sum of 'gene grades' and the statistical significance is calculated using hypergeometric distribution.

The third generation is Pathway Topology (PT) approaches. It is difficult to generalize about this category, but essentially, these approaches perform the same steps as FCS approaches, however for computing pathway level statistics they utilize not only experimental measurements but also topological information about pathways:

1) computing single pathway-level statistics using gene level statistics calculated using experimental measurements and topological information about pathways and/or molecular interactions information;

2) testing the significance of the pathway-level statistics.

The major limitations of such approaches are:

1) dependency on the pathway topology information which can be incomplete.

Examples: SPIA [START_REF] Tarca | A novel signaling pathway impact analysis[END_REF], EnrichNet [START_REF] Glaab | EnrichNet: Network-based gene set enrichment analysis[END_REF].

Signaling pathway impact analysis (SPIA) method was proposed in 2009 by [START_REF] Tarca | A novel signaling pathway impact analysis[END_REF]. The method assesses the pathway significance using information from both standard over representation analysis and from possible abnormal perturbation of the pathway measured using expression changes of every gene in the pathway (the method was suggested for the analysis of transcriptome profiling data). To be able to quantify perturbation of the pathway, the authors first introduce a gene perturbation factor, which is calculated as a sum of the expression change of the gene and a normalized and weighted sum of perturbation factors of the upstream genes in the pathway, where the weights quantify the strength of the interactions in the pathway if such information is available.

Next, the author introduced the net perturbation accumulation of every gene which is the perturbation factor minus expression change, and total net perturbation accumulation of entire pathway as a sum of net perturbation accumulation of every gene. Then, the p-value of getting a total perturbation accumulation of a pathway higher than given is calculated by permuting gene IDs in the pathway and constructing the null distribution. The total p-value for a pathway is calculated as a probability to observe p-value from the standard ORA and pvalue for perturbation accumulation as low as for a given pathway.

EnrichNet method was proposed in 2012 by [START_REF] Glaab | EnrichNet: Network-based gene set enrichment analysis[END_REF]. The method takes a list of genes, annotated gene sets (e.g. canonical pathways) and a network of molecular interactions as input data. The method calculates the scores associated with the distance between the list of genes and annotated gene sets using a random walk with restart algorithm (RWR) and compares these scores with a background model. A random walk with restart algorithm enables to estimate the proximity of two nodes when each following step of the walk starting from a seed node on a graph is chosen randomly and the walk can be restarted with a certain probability. The authors argue that such distance measure better capture possible relationships between two nodes than for example the shortest path distance. Each annotated gene set is assigned a vector where each position is a RWR score from randomly chosen input gene to a gene in the pathway. Then, the deviation of these vectors from a background model, which is defined as "average distribution across all pathways" is assessed.

The second category of computational approaches for analysis and interpretation of the functional screening data is network analysis methods which usually use protein-protein interaction networks as complementary information. These methods aim at improving hit identification, finding functionally related biological components, finding significantly enriched/maximum weight subnetworks using ranked list or hit list. This is usually done by introducing network-based scoring methods that use network topology information and screening results. Below are examples.

(L. Wang et al., 2009) suggested a method implementing "guilt by association" principle and NePhe scoring system to address the problem of false positives and false negatives associated with RNAi screenings. The "guilt by association" principle was used in the sense that if a gene has many connections with other hit genes, then it is a true positive if it is a hit and false negative if it is not hit, and vice versa. The quantification of the connectedness between two genes was done using either direct neighbor information or the shortest path distance or diffusion kernels or association analysis-based transformations. [START_REF] Kaplow | RNAiCut: automated detection of significant genes from functional genomic screens[END_REF] suggested RNAiCut method to identify thresholds for ranked functional screening data by using connectivity of subgraphs of protein-protein interaction networks.

The main hypothesis of the method is that true positive hits are densely interconnected in the protein-protein interactions networks [START_REF] Kaplow | RNAiCut: automated detection of significant genes from functional genomic screens[END_REF]. The method ranks the screening data and for each set of first k genes in the ranked list it computes the number of edges (connectivity) in the subgraph retrieved from PPI network. Then, the method estimates the p-value of getting such connectivity if the subnetwork was constructed of random nodes conserving the degree distribution. The number of genes k associated with the lowest p-value is used as the threshold.

(S. [START_REF] Ma | Differential regulation enrichment analysis via the integration of transcriptional regulatory network and gene expression data[END_REF] suggested NEST (Network Essentiality Scoring Tool) to assess gene essentiality and improve the quality of loss-of-function screening. The NEST score is calculated as a sum of expression values or scores from loss of function screenings information of neighbor genes in molecular network weighted by the interaction confidence.

The authors showed the NEST scores are significantly higher for essential genes. [START_REF] Cornish | SANTA: Quantifying the Functional Content of Molecular Networks[END_REF] suggested a method called SANTA that also uses "guilt by association" principle to quantify association between a gene list and a network (including functionally annotates molecular networks). The authors introduced K net and K node scores that use adapted Ripley's K-Function for networks to measure network scores (K net ) and networkbased nodes scores (K node ). The K net scores assess quantity and strength of hits located in the proximity of each other in the network. This score can help to measure functional association of gene list with functionally annotates molecular networks. The K node score quantify each node's neighborhood for strong and close hits. By extracting nodes with the highest K node score, significantly enriched for hits subnetworks can be identified. [START_REF] Kairov | Network analysis of gene lists for finding reproducible prognostic breast cancer gene signatures[END_REF] suggested a method called OFTEN (Optimally Functionally Enriched Network) implemented in BiNoM [START_REF] Bonnet | BiNoM 2.0, a Cytoscape plugin for accessing and analyzing pathways using standard systems biology formats[END_REF] Cyroscape [START_REF] Shannon | Cytoscape: A software Environment for integrated models of biomolecular interaction networks[END_REF] plugin to associate a ranked gene list with PPI subnetwork. The method aims at finding the largest connected component of a subnetwork formed by optimal number of top-ranked genes in the input list. The optimal number is identified by finding the number of top-ranked genes after which the percolation score rapidly goes down. The percolation score computes using the size of the largest connected component of a subnetwork, the number of genes that were used to extract subnetwork and the mean value of the size of the largest connected component formed by randomly selected genes. [START_REF] Beisser | BioNet: An R-Package for the functional analysis of biological networks[END_REF] and [START_REF] Dittrich | Identifying functional modules in protein-protein interaction networks: An integrated exact approach[END_REF] suggested a method called BioNet for finding maximum-weight connected subgraph which represent active subnetwork in the network.

The method first annotates each gene with experimentally derived p-values. Then, these pvalues are aggregated into one number (if several experimental conditions were tested simultaneously). The scoring function model both the signal and the noise component of the p-value using a beta-uniform mixture model, controlling false discovery rate. Then, the maximum-weight connected subgraph is found using linear programming algorithm.

Several other methods besides OFTEN and BioNet exist for finding active subnetworks.

These are : jActiveModules [START_REF] Efficient Ideker | Discovering regulatory and signalling circuits in molecular interaction networks[END_REF], ClustEx [START_REF] Gu | Identification of responsive gene modules by networkbased gene clustering and extending: Application to inflammation and angiogenesis[END_REF], PinnacleZ [START_REF] Chuang | Network-based classification of breast cancer metastasis[END_REF], kwalk [START_REF] Zheng | GenRev: exploring functional relevance of genes in molecular networks[END_REF], WMAXC [START_REF] Amgalan | WMAXC: A Weighted Maximum Clique method for identifying condition-specific sub-network[END_REF], BMRF (L. [START_REF] Carthew | Identifying protein interaction subnetworks by a bagging Markov random field-based method[END_REF], COSINE (H. [START_REF] Ma | COSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method[END_REF]. These methods as well as BioNet share the general idea: first, to introduce a subnetwork scoring function; then, to introduce an algorithm to find subnetwork with maximum or close to maximum score.

A completely different method one should mention is CausalR suggested by [START_REF] Bradley | CausalR -extracting mechanistic sense from genome scale data[END_REF] and [START_REF] Chindelevitch | Causal reasoning on biological networks: Interpreting transcriptional changes[END_REF]. This method works with a network that consists of casual interactions that show the effect of the interaction between two proteins (inhibition or activation). It takes as starting points each gene (and indication whether it is up-or downregulated or there is no change) in a list of genes and finds points of convergence (usually upstream regulators) in the network that can explain the differential expression pattern seen in the starting points.

As for limitations, both pathway analysis and network analysis approaches have several general limitations. Firstly, pathway and network information can be incomplete and inaccurate. Secondly, both approaches do not give much information about the genes that are not included into pathways or networks. Lastly, networks are biased towards well studied genes.

Network analysis of functional genomics screening data

Some parts of this section are verbatim copy of two research papers being prepared for publication. One of them describes masterPATH method and its application to the analysis of the results of miRNA loss-of-function screening and transcriptomic profiling of terminal muscle differentiation and of 'druggable' loss-of-function screening of the DNA repair process; the other one is devoted to the study of the shortest path approach on the human interactome.

Here a new network analysis method to analyze functional genomics screening data -masterPATH -is presented. The method aims at elucidating members of molecular pathways leading to the studied phenotype using functional genomics experiments data in the hit list form. The method works on an integrated network that represent human interactome. The method constructs the integrated network from 8 databases: HPRD [START_REF] Peri | Development of human protein reference database as an initial platform for approaching systems biology in humans[END_REF], HIPPIE [START_REF] Schaefer | Hippie: Integrating protein interaction networks with experiment based quality scores[END_REF], Signor [START_REF] Surdo | SIGNOR: A database of causal relationships between biological entities-a short guide to searching and browsing[END_REF], SignaLink [START_REF] Fazekas | SignaLink 2 -a signaling pathway resource with multi-layered regulatory networks[END_REF], tFacts [START_REF] Essaghir | Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data[END_REF], KEGG Metabolic Pathways [START_REF] Ogata | KEGG: Kyoto encyclopedia of genes and genomes[END_REF], transMir (J. Wang et al., 2009), mirTarBase [START_REF] Hsu | MiRTarBase: A database curates experimentally validated microRNA-target interactions[END_REF]; and calculates basic topological properties. The method extracts subnetwork built from the shortest paths of 4 different types (with only protein-protein interactions, with at least one transcription interaction, with at least miRNA-mRNA interaction, with at least one metabolic interaction) between hit genes to so called "final implementers" -genes that are involved in molecular events responsible for final phenotypical realization (if known) or between hit genes (if "final implementers" are not known). The method calculates centrality score for each node and each linear path in the subnetwork as a number of the shortest paths found in the previous step that pass through the node and the linear path. Then, the statistical significance (pvalue net ) of each centrality score is assessed by comparing it with centrality scores in subnetworks built from the shortest paths for randomly sampled hit lists (summarized in Figure 3-1). It is hypothesized that the nodes and the linear paths with statistically significant centrality score can be considered as putative members of molecular pathways leading to the studied phenotype. In case the experimental scores and p-values are available for a large number of nodes in the network, the method can also calculate experiment-based scores (score exp ) and experiment-based p-values (p-values exp ) for the shortest paths or linear paths.

The score exp is calculated as an average of the experimental scores of the nodes in the path.

The p-value exp is calculating by aggregation of the p-values of the nodes in the path using Fisher's combined probability test and permutation approach.

The method is illustrated by analyzing the results of miRNA loss-of-function screening and transcriptomic profiling of terminal muscle differentiation and of 'druggable' loss-of-function screening of the DNA repair process. It is shown that known and new potentially interesting components for both biological systems are identified by the method.

masterPATH algorithm

The following notions are used in the mechanistic model of pathway construction: an unweighted graph G=(V, E) represents a network of molecular interactions, where V are nodes that can be proteins, genes, small molecules or miRNAs; E are edges represent molecular interaction between nodes, interactions can be direct or undirect. List of hit genes of size n is as a set

H = {hi : hi ∈ V for ∀ i ∈ [1..n]}. List of "final implementers" of size m as a set F = {fi : fi ∈ V for ∀ I ∈ [1.
.m]}. A simple linear paths p between a pair of nodes (v,u) : v,u ∈ V is as set of pairs of nodes that represent existing edge in the graph G: p(v,u)= (v,v1),(v1,v2) ... (vn,u) where vi ∈ V for ∀ i and each node vi is distinct. Length L of the path p(v,u) is the number of edges in the path p. We distinguish 4 different types of paths:

• protein-protein paths if all edges represent protein-protein interactions;

• transcriptional paths if there exist at least one edge that represent transcriptional interaction;

• miRNA paths if there exist at least one edge that represent miRNA-mRNA interaction;

• metabolic paths if there exist at least one edge that represent enzymatic reaction.

The algorithm of the method is the following. For a given network G, hit list H, list of "final implementers" F the method finds for each pair of hit gene and "final implementer" (hi, fj) all the shortest paths {pi} of four abovementioned types of length less or equal the maximum length Lmax (defined by the user) in the network G. The search is done using breadth-first algorithm. Then the centrality score which resembles centrality score c is calculated for each node v and each path q (of length of several interactions) as a number of the shortest paths from {pi} that pass through the node v and the path q : c(v)= |p ∈ {pi} : v ∈ p| ; c(q) = |p ∈ {pi} : q ∈ p| . After that, the statistical significance of each score is assessed. 10000 random hit lists are sampled from the set of nodes N preserving or not preserving the degree distribution of the initial hit lit. The probability (p-value Net ) of getting a node v or a path q with specific centrality score by chance is calculated as a proportion of sampled hit lists for which a node or a short path has the same or greater centrality score.

In case the experimental scores and p-values are available for the large number of nodes in the network G, experimental scores (score exp ) and p-values (p-value exp ) can also be calculated for the shortest path p, a path q, a node v. The score exp is calculated as an average of the absolute values of the experimental scores of the nodes in a path for a shortest path pi and a path q; and equal to the absolute value of the experimental score for a node v. The p-value exp of the shortest path p and a path q is aggregated using Fisher's combined probability test. The path is considered as a set of unique nodes. First, the test statistic

𝑋 𝑒𝑥𝑝 = -2 ∑ log 𝑝 𝑖 𝑚 𝑖=1
is calculated for a path, where 𝑝 𝑖 is an experimental p-value of a node 𝑖. Then, a permutation test is used to assess the p-value of the 𝑋 𝑒𝑥𝑝 (p-value exp ): the set of experimental p-values is shuffled many times (e.g. 1000), the test statistic 𝑋 𝑝𝑒𝑟𝑚 is calculated for each shuffled sample, the p-value of the 𝑋 𝑒𝑥𝑝 is calculated as a proportion of samples for which 𝑋 𝑝𝑒𝑟𝑚 is at lease the same as 𝑋 𝑒𝑥𝑝 . The p-value exp a node v equal to the experimental p-value. The nodes without experimental values are not taken into account.

Databases

The following databases were used to construct integrated network: HIPPIE, Signor, SignaLink, tFacts, KEGG Metabolic pathways, transMir, mirTarBase; and protein-protein networks: Human Protein Reactions Database (HPRD) v9.1 and Human Integrated Protein-Protein Interaction rEference (HIPPIE) v2.0. All databases contain experimentally validated protein-protein interactions for human cells except SignaLink database which contains a small number of predicted miRNA-mRNA interactions. We distinguish high confidence, medium confidence and low confidence interactions is HIPPIE database using the confidence score from the database. The confidence score is assigned for each interaction in HIPPIE database and is calculated as a weighted sum of the number of studies in which an interaction was detected, the number and quality of experimental techniques used to measure an interaction and the number of non-human organisms in which an interaction was reproduced("HIPPIE Howto," n.d.). Predefined confidence levels by HIPPIE team were All the databases use different types of gene ID. The IDs were converted to the HUGO gene nomenclature and this nomenclature was used to construct networks. Only interactions between proteins, complexes and small molecules were used from Signor database. The information about the databases is summarized in Table 2-

Software implementation

The masterPATH method is implemented in Java program. Java code was developed using Java JDK 1.8, NetBeans IDE 8.0.2, GitHub version control systems and is available on GitHub page https://github.com/daggoo/masterPath.

The program consists of the following modules.

Database module:

• Loading databases from text/xml files.

• Unifying IDs (HGNC IDs).

Network module:

• Creating networks from databases.

• Extracting node/edge attributes from databases files.

• Merging networks.

• Loading lists hit genes and final implementers.

• Finding all length-bound pathways for two lists of nodes.

Pathway module:

• Finding the shortest paths of 4 different types for a set of pathways.

• Calculating scores exp and p-values exp for a set of pathways.

• Filtering pathways.

• Creating files for Cytoscape Software (network visualization).

• Finding the 'strongest' paths using scores exp and p-values exp .

Centrality module:

• Calculating nodes' and paths' centrality scores.

• Calculating paths' degree.

• Calculating nodes' and paths' scores exp and p-values exp .

• Filtering paths.

Random module:

• Sampling random hit lists preserving or not degree distribution.

• Generating random networks preserving degree distribution.

• Calculating p-values net .

Network topology module:

• Calculating basic topological properties for networks (average clustering coefficient, number of connected components, average number of neighbors, density, diameter, average length of the shortest path).

The detailed description for masterPATH Java code in the form of JavaDoc created with the help of the Doxygen program is in Appendix 2.

The study of the shortest path approach

This chapter is devoted to the study whether the shortest path approach gives valid molecular paths. In particular, does at least one of the shortest paths between biologically meaningful start and end points built in the cell interactome represents a valid molecular path and, if not, does the analysis of the centrality measure introduced in the previous chapters help at least partially reconstruct valid molecular path?

To do this parts of the NF-kappa B, MAPK, Jak-STAT, mTOR, ErbB, Wnt, TGF-beta signaling pathways, the signaling part of the apoptotic process canonical pathways from KEGG database [START_REF] Ogata | KEGG: Kyoto encyclopedia of genes and genomes[END_REF] that consist only of protein-protein interactions were taken for the analysis (Figures 12345678).

46 simple linear protein-protein paths of length from 2 to 5 interactions were found for 8 canonical pathways (Table 2-2 The following steps were performed:

1. The shortest paths were found for each source and target points. If a source or a target point was a complex or implies several homologs all proteins were taken.

2. The average length of the shortest counterparts was calculated for the canonical paths of length 2, 3, 4, 5.

3. The canonical paths were compared with the shortest counterparts.

4. The centrality score was calculated for each node/path in the subnetwork constructed from the shortest paths (the shortest paths subnetwork) for each source/target points. The number of nodes/paths with centrality score ≥ 2 that are members of the canonical path were calculated.

It was found that the average length of the shortest paths is less by at least 1 interaction for the canonical paths of length of 4 and 5 interactions (

Topological properties of the PPI and the integrated networks

Application: human muscle differentiation miRNA loss -of-function screening and transcriptome profiling

The screening data from the study by A. Polesskaya et al. [START_REF] Polesskaya | Genome-Wide Exploration of miRNA Function in Mammalian Muscle Cell Differentiation[END_REF] was taken as the hit list for terminal human muscle differentiation process. In this study, genome-wide miRNA loss-of-function screening on late differentiating human muscle precursor cell line (LHCN) was performed in two-step approach. The primary screening was done in duplicate with LNA antisense inhibitors library targeting 870 miRNAs and a readout assay that detects multinucleated Myosin Heavy Chain (MHC) positive myotubes. Those miRNAs whose depletion resulted in differences to the negative control ≥ 2 SD were selected for the secondary screen which was done in triplicate. The total number of nuclei was checked in addition to the readout assay from the primary screen. A total of 63 miRNAs whose depletion resulted in differences to the negative control ≥ 2 SD were confirmed in the secondary screen (Table 2-4). The screening data from the study by J. [START_REF] Kropp | miR-98 delays skeletal muscle differentiation by down-regulating E2F5[END_REF] was taken as the second hit list for terminal human muscle differentiation process. Transcriptomic profiling for proliferation and late differentiation stages in human muscle precursor cell line (LHCN)

was performed using Affymetrix Human Gene 1.1 ST arrays [START_REF] Kropp | miR-98 delays skeletal muscle differentiation by down-regulating E2F5[END_REF]. A total of 571 differentially expressed genes during late differentiation compared to proliferation stage were found with 2-fold change and p-value≤0.05 threshold (Table 2345).

11 proteins were used as a list of "final implementers". These are MSTN, IGF2, ACTA1, MYH1, MYLPF, ARF6, CD81, CD9, CDC42, EHD2, MYOF proteins responsible for inhibition, activation, and facilitating of fusion of myotubes [START_REF] Alzhanov | Long range interactions regulate Igf2 gene transcription during skeletal muscle differentiation[END_REF][START_REF] Bourmoum | The GTPase ARF6 controls ROS production to mediate angiotensin II-induced vascular smooth muscle cell proliferation[END_REF][START_REF] Doherty | The endocytic recycling protein EHD2 interacts with myoferlin to regulate myoblast fusion[END_REF][START_REF] Gunning | Alpha-skeletal actin induces a subset of muscle genes independently of muscle differentiation and withdrawal from the cell cycle[END_REF]; S.-J. [START_REF] Lee | The C. elegans Heterochronic Gene lin-4 Encodes Small RNAs with Antisense Complementarity to lin-14[END_REF][START_REF] Tachibana | Role of transmembrane 4 superfamily (TM4SF) proteins CD9 and CD81 in muscle cell fusion and myotube maintenance[END_REF][START_REF] Vasyutina | The small Gproteins Rac1 and Cdc42 are essential for myoblast fusion in the mouse[END_REF][START_REF] Wang | Fast skeletal muscle regulatory light chain is required for fast and slow skeletal muscle development[END_REF]. The analysis was performed in the integrated human interactome. interactions and centrality score ≥ 3, with p-value < 0.05 (Table 23456789) were taken for further analysis. The scores exp and p-values exp were also calculated for each of these nodes/paths.

ABCA1 ABCC3 ABHD5 ABLIM3 ACCN2 ACOT11 ACTA1 ADAM9 ADAMTS6 ADAMTSL1 ADAMTSL5 AFAP1L1 AFAP1L2 AGPAT5 AGRN AIF1L AIM1 AKAP1 ALCAM AMIGO2 ANK2 ANKRD10 ANKRD13A ANKS1A APOBEC2 APOBEC3C APOC1 APOD APOE AQP3 ARHGAP9 ARHGEF2 ARHGEF3 ARHGEF6 ARPC1B ARPP21 ASNS ASPM ASS1 ASTN2 ATOH8 ATP10A ATP1A1 ATP6V0D1 ATP6V0E2 ATP8B1 ATXN7L1 B3GALT5 BAIAP2L1 BASP1 BBS12 BCAM BCHE BCL2L11 BCL6 BCL7A BDKRB1 BEND7 BHLHE41 BIN1 BIRC5 BLCAP BNC1 BNIP3 BTG1 BTG3 C10orf10 C10orf72 C11orf71 C12orf34 C14orf159 C14orf43 C1S C1orf105 C2 C20orf103 C21orf119 C21orf33 C21orf59 C21orf88 C22orf13 C3 C3orf39 C4A C7orf55 C7orf58 C9orf46 CABLES1 CACNA1S CACNA2D1 CACNB1 CACNG1 CAMK2D CASP9 CASQ2 CASS4 CASZ1 CBFB CCDC102B CCDC99 CCNA2 CCND3 CD36 CD68 CDC25B CDH2 CDK1 CDK15 CDKN1C CDKN3 CDON CELA2B CENPA CENPE CGB CGB1 CGB2 CGB5 CGB7 CHCHD8 CHD7 CHIT1 CHRNB1 CHRND CHRNG CIAO1 CKB CKM CLCA2 CLCN5 CLSTN2 COL5A3 COL6A2 COL6A3 CORO2B CORO7 CPA4 CPNE2 CPS1 CREB3L1 CREG1 CRIM1 CTNNAL1 CTSH CUEDC1 CUZD1 CYB5R1 DAAM2 DAG1 DBH DBNDD1 DCBLD1 DCP2 DDI2 DGKD DIAPH3 DIO2 DLG2 DLL1 DMBT1 DNAJB1 DNASE1L1 DOCK10 DOCK11 DOCK5 DPT DSP DTNA EAF1 EBF1 ECH1 ECM1 EDN1 EFHD2 EGLN3 ELN ENO3 ENPP1 ENPP2 EPHB2 ERBB3 ERO1L EZR FAIM2 FAM101B FAM171A2 FAM174A FAM65B FBN1 FBXL22 FGF1 FGF2 FGFR4 FGL2 FILIP1 FITM1 FLVCR2 FMN2 FMNL1 FMO3 FNDC5 FOXM1 FRK FRMPD1 FSTL3 FXYD1 FXYD5 FXYD6 FYCO1 G6PD GAB2 GADD45G GALNT5 GATM GCNT1 GCOM1 GGH GGT5 GLIPR1 GLRX GMPR GNAZ GPM6B GPNMB GPR1 GPR125 GPR153 GPR155 GPR176 GREM1 GSTT2 GTF2F2 GUSB H1F0 HES1 HES6 HEY1 HFE2 HIP1 HIPK3 HIST1H3J HIST2H4A HIVEP1 HOMER2 HOXA7 HRC HSD11B2 HSD17B10 HSPA4L HSPB8 ID1 ID2 ID3 IFFO1 IGF1 IGFBP6 IL1R1 IL34 IMPA2 IMPAD1 INHBA INPP4B INPPL1 ITGA10 ITGA7 ITGB1BP2 ITGB1BP3 ITM2C KCNA7 KCNIP3 KCNJ15 KCNJ2 KCNN3 KCNQ4 KDELR3 KDM4B KDM6B KIAA1632 KIAA1644 KIF13B KIF1B KIF20A 60 KLHL28 KREMEN1 KY LAMA4 LAMA5 LAPTM5 LDB3 LDLRAD3 LETMD1 LMBRD2 LMOD3 LNX1 LOC645166 LOC654342 LOX LRIG1 LRRC16A LRRC17 LRRFIP1 LRRN1 LRRN4CL LSP1 LSS MACF1 MACROD1 MAMLD1 MAN2A2 MAP3K14 MAPRE3 MDM4 MEF2D MEGF10 MEOX1 MFAP4 MFAP5 MGC39372 MICAL1 MICAL2 MIR133B MIR206 MIR503 MLLT6 MOAP1 MOBKL1A MORC4 MPI MPP6 MRAP2 MRC2 MT1E MTSS1 MTSS1L MUSK MYBPC2 MYBPH MYF5 MYH3 MYH8 MYL4 MYL5 MYL6B MYLPF MYO18B MYO5A MYOG MYOZ2 NCAPG NCOA1 NDC80 NDRG2 NDRG4 NEB NEDD4 NES NEU1 NIPAL3 NOTCH3 NRG1 NRP2 NTM NUDT14 NUP93 OLFM2 OLFML2A OPHN1 OSR2 OTOF OTUD3 OXTR P2RX6 PAAF1 PACS2 PADI2 PAN2 PAR5 PAWR PCBD1 PCK2 PDE2A PDGFC PDK1 PDLIM1 PFKM PGA3 PGA4 PGA5 PGAM2 PGBD5 PGD PGM2 PGM3 PID1 PLAC9 PLAU PLEKHA4 PLOD2 PLXNA1 PLXNA2 PODN PODXL PORCN PPFIA4 PRICKLE2 PRKAG3 PRKAR1A PRKX PRUNE PSEN2 PSG4 PTGDS PTGFR PTGS1 PTN PTPRF PTTG1 PURB PUS7L RAB15 RAB3B RAI14 RALA RAPH1 RARRES3 RASA1 RASSF4 RBM24 RCL1 RCSD1 RDH5 RGR RGS16 RGS2 RNASE3 ROS1 RPL39L RPRD1B RTN2 RYR1 SARS2 SBK2 SCARNA13 SCD SCG2 SCGB1C1 SCN4A SEMA6B SEPT4 SEPW1 SERPINB8 SERPINE1 SERPINE2 SERPINF1 SETD7 SGOL1 SHD SHF SIM1 SIRT2 SIX4 SLC12A2 SLC12A7 SLC29A1 SLC38A3 SLC38A4 SLC38A6 SLC3A2 SLC43A2 SLC7A5 SLC7A7 SMAD1 SMPX SMURF2 SMYD1 SNAI1 SNED1 SNORD113-4 SNORD116-17 SNORD116-20 SNORD116-3 SOX8 SPARCL1 SPOCK1 SPPL2B SPRY1 SREBF1 SREBF2 SRF SRGN SRPK3 SSPO SSX4 ST3GAL5 ST6GALNAC4 ST8SIA5 STAC3 STARD5 STAT3 STK10 SUN2 SYNPO TAS1R1 TBC1D2
There are 18 miRNAs among these 328 nodes (Table 2-8 B). 4 of them (hsa-mir-125b, hasmir-133a, has-mir-133b, has-mir-145) are the hit miRNAs in the loss-of-function screen. 5

with the highest centrality score are hsa-mir-125b, hsa-mir-29a, hsa-mir-371, hsa-mir-216a, hsa-mir-1. All these miRNAs except hsa-mir-371 were shown to be involved in muscle differentiation and/or proliferation previously [START_REF] Callis | MicroRNAs in skeletal and cardiac muscle development[END_REF][START_REF] Crist | microRNas gain magnitude in muscle[END_REF][START_REF] Meyer | TNF-α and IGF1 modify the microRNA signature in skeletal muscle cell differentiation[END_REF][START_REF] Winbanks | TGF-β regulates miR-206 and miR-29 to control myogenic differentiation through regulation of HDAC4[END_REF]. Moreover, almost half of these miRNAs are known to participate in terminal muscle differentiation, and potential roles in myogenesis could be predicted for other miRNAs in this list e.g that regulate cellular proliferation (such as miR-132, miR-145 or miR-224), as well as cardiac hypertrophy (miR-378). Interestingly, the majority of these miRNAs were not found in the original loss-of-function screen, most likely due to the redundancy of miRNA family members. Indeed, as the miRNAs of the same family share the seed sequence, an efficient loss-of-function screen should have contained not only individual miRNA inhibitors, but also the inactivators of whole miRNA families, in order to avoid false negative results. In this sense, our analysis of these data have been very

important in supplementing a group of miRNA targets that could have been overlooked. This possibility is highlighted by the presence of known myogenesis regulatory miRNAs (miR-1, miR-29a, miR-216a) in the list resulting from the analysis, whereas they have not been picked up by the original experimental screen.

The analysis of paths allowed to identify a potentially novel and interesting pathway in et al., 2002;[START_REF] Nakao | Ubiquitin Ligase Cbl-b Is a Negative Regulator for Insulin-Like Growth Factor 1 Signaling during Muscle Atrophy Caused by Unloading[END_REF][START_REF] Zappia | E2F function in muscle growth is necessary and sufficient for viability in Drosophila[END_REF]. Most of the paths pass through nodes with high centrality score (E2F2, KIT, MDM2, CD81, HNRNPD, IGF1R, ESR1). One of the paths with the highest score consists of CDKN1A, MDM2, TCAP, MSTN proteins. The interaction between MDM2 and TCAP is known to be important for cardiac hypertrophy [START_REF] Tian | MDM2 interacts with and downregulates a sarcomeric protein, TCAP[END_REF], it was also shown that TCAP controls secretion of MSTN [START_REF] Nicholas | Titin-cap associates with, and regulates secretion of, myostatin[END_REF]. The analysis shows that this path might be activated by the depletion of hsa-mir-17, hsa-mir-106a, hsa-mir-125a, hsa-mir-145, hsa-mir-93 and/or by interaction with differentially expressed genes products (Figure 2-15). I can also be noted, that not only androgen receptor (AR), but also the estrogen receptor ESR1 can play a role in human skeletal myogenesis.

Interestingly, specific integrins (ITGB1, ITGA6) and adaptor proteins (CRKL) have also been We also found that 14 paths from the analysis of transcriptomic profiling have miRNAs hits from the loss-of-function screening (Table 2-12) and 60 paths from the analysis of loss-offunction screening have hit genes from the transcriptomic profiling on them (Table 2345678910111213).

These are the paths from the analysis of transcriptomic profiling that pass through hsa-mir-125b which controls IGF2 gene and the paths that pass through has-mir-145 that control TRIP10 protein which (Figure 2 The data from the siRNA functional screening that identified 'druggable' genes involved in oxidative damaged DNA repair [START_REF] Guyon | Φ-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cellbased assays[END_REF][START_REF] Robinson | Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields[END_REF] were used as the hit list. The screening was performed on genetically engineered HeLa cells that express OGG1-GFP fusion protein. OGG1 is a DNA glycosylate protein that is recruited to chromatin to initiate the repair of oxidized chromatin [START_REF] Guyon | Φ-score: A cell-to-cell phenotypic scoring method for sensitive and selective hit discovery in cellbased assays[END_REF]. Each 'druggable' gene was targeted by 3 siRNAs [START_REF] Robinson | Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields[END_REF]. The intensity of chromatin-bound OGG1-GFP was detected after inducing DNA damage and 18 hit genes were identified (Table 234567891011121314). All 18 hit genes were used as a list of "final implementers". The analysis was performed in the protein-protein human interactome.

It was found 4876 the shortest protein-protein paths of length from 1 to 4 interactions from each gene product in the hit list to each gene product in the list of "final implementers". The subnetwork constructed from these paths consists of 381 nodes and 1764 edges without duplicated edges. The centrality score and the p-value were calculated for each node and path in the subnetwork according the procedure described in the masterPATH algorithm section. 28 nodes with centrality score ≥ 3 and with p-value < 0.05 (Table 2-15) and 64 paths of length of 2 to 3 interactions, with centrality score ≥ 3 and p-value < 0.05 (Table 2-16) were found.

The top 10 nodes with the highest centrality score are histones 3H proteins: HIST1H3A, HIST1H3B, HIST1H3C, HIST1H3D, HIST1H3E, HIST1H3F, HIST1H3G, HIST1H3H, HIST1H3I, HIST1H3J. It is known that the DNA damage is associated with higher level of chromatin mobility [START_REF] Dion | Increased mobility of double-strand breaks requires Mec1, Rad9 and the homologous recombination machinery[END_REF][START_REF] Miné-Hattab | Increased chromosome mobility facilitates homology search during recombination[END_REF][START_REF] Strzyz | DNA repair: Histones have got to go[END_REF] and it was shown recently that the increase in chromatin mobility is governed by the proteasome-mediated degradation of core histones [START_REF] Hauer | Histone degradation in response to DNA damage enhances chromatin dynamics and recombination rates[END_REF]. Other proteins with high centrality score are SETDB1 protein -a member of the SET1 family of proteins; WDR5 protein -a core component of SET1 family complexes [START_REF] Ruthenburg | Histone H3 recognition and presentation by the WDR5 module of the MLL1 complex[END_REF]; TP53BP1 -a binding partner of the tumour suppressor protein p53. SETDB1 and WDR5 are associated with posttranslational histone modification which allows recruitment of the chromatin-associated proteins and protein complexes [START_REF] Odho | Characterization of a novel WDR5-binding site that recruits RbBP5 through a conserved motif to enhance methylation of histone H3 lysine 4 by mixed lineage leukemia protein-1[END_REF][START_REF] Schultz | SETDB1 : a novel KAP -1 -associated histone H3 , lysine 9 -specific methyltransferase that contributes to HP1 -mediated silencing of euchromatic genes by KRAB zinc -finger proteins[END_REF]. TP53BP1 protein is known to be an important regulator of the cellular response to DNA double-strand breaks [START_REF] Panier | Double-strand break repair: 53BP1 comes into focus[END_REF].

Figure 3-4 presents subnetworks visualized with Cytoscape software [START_REF] Shannon | Cytoscape: A software Environment for integrated models of biomolecular interaction networks[END_REF] for the paths of length 3 interactions with centrality score 3. Figure 2-17a shows that the method identified that two cohesin proteins SMC3 and SMC1A can act by interacting with RAD21 protein, known cohesin-RAD21 complex [START_REF] Losada | Cohesin in cancer: chromosome segregation and beyond[END_REF] enriched at DNA doublestrand break sites and facilitates recombinational DNA repair [START_REF] Watrin | The cohesin complex is required for the DNA damageinduced G2/M checkpoint in mammalian cells[END_REF]. Figure 2-17b shows possible mechanism of involvement of PSMA1, PSMA3, PSMA4 proteins, all members of the 20S proteasome [START_REF] Coux | Structure and functions of the 20S and 26S proteasomes[END_REF], through interaction with AURKB, Aurora Kinase B [START_REF] Shu | Human Aurora-B binds to a proteasome α-subunit HC8 and undergoes degradation in a proteasome-dependent manner[END_REF], which in turn interacts with histones H3 [START_REF] Crosio | Mitotic Phosphorylation of Histone H3 : Spatio-Temporal Regulation by Mammalian Aurora Kinases Mitotic Phosphorylation of Histone H3 : Spatio-Temporal Regulation by Mammalian Aurora Kinases[END_REF]. The path ends with histones H3 -SETDB1 interaction. SETDB1 is a histone methyltransferase that specifically methylates histone H3 [START_REF] Schultz | SETDB1 : a novel KAP -1 -associated histone H3 , lysine 9 -specific methyltransferase that contributes to HP1 -mediated silencing of euchromatic genes by KRAB zinc -finger proteins[END_REF] and is also a member of the hit list.

The red arrow shows the direction of this interaction on Figure 2-17a. Considering this, histones H3 are the proteins where the signal converges from different members of the hit list and we hypothesize that histone H3 can be a "final implementer" for this system.

Discussion

Two different types of networks were used in this work. The first one was a mixed direct and indirect network constructed from PPI, transcriptional, post-transcriptional and metabolic data. The second network was indirect PPI network. PPI networks are the most common networks used in network analysis. PPI networks are known to be incomplete and biased towards the well-studied proteins. Incorporating transcriptional, post-transcriptional and metabolic data, does not solve the issues associated with PPI networks, but adds information on directionality, positive or negative effect of the interactions and gives the ability to build heterogeneous paths thus allowing to study biological system at different levels.

The possible bias in the results towards highly connected nodes/paths that pass through highly connected nodes is removed by random sampling hit lists. We used the threshold of 0.05 for p-value net . However, the nodes/paths with higher p-value net can also be considered, it is just not guaranteed that the value of the centrality score is due to the specificity of the node/path to the phenotype or due to its high connectivity.

Other two general problems in network analysis is network specificity for the biological system and lack of information relating to some members of the hit list. Networks that represent human interactome were used in this work. However, the nodes that are not active can be excluded from the network for some biological systems, based for example on the transcriptomic data, to create a smaller but more specific network. On the other hand, the hit nodes from the functional screening might be poorly studied or might even not be present in the network. Low confidence or predicted interactions for hit nodes might be added to the network in this case. This might be particular relevant for miRNA-mRNA interactions, since miRNA are thought to be important regulators of different biological processes, but the number of experimentally validated interactions is quite low.

The notion of "final implementer" was introduced in this work. A "final implementer" was denoted as a biological component that is involved in events responsible for final phenotypical realization of the biological process. Modern molecular biology accumulated vast amount of knowledge and for some biological processes such biological components are known, e.g. for apoptosis caspase 3, caspase 6 and caspase 7 could be considered as "final implementers". In case such biological components are unknown, the members of the hit list can be used as a list of "final implementers" for the analysis on the PPI network and by studying directionality of the paths candidates for "final implementers" could be found as it was demonstrated for the DNA repair process.

The shortest path approach was used to retrieve connections between biological components in this work. The shortest path approach is used in many contexts in systems biology e.g it is used to construct networks for a set of genes [START_REF] Yuan | Identification of Candidate Genes Related to Inflammatory Bowel Disease Using Minimum Redundancy Maximum Relevance, Incremental Feature Selection, and the Shortest-Path Approach[END_REF], to calculate topological properties for nodes prioritization [START_REF] Zhang | Predicting candidate genes based on combined network topological features: A case study in coronary artery disease[END_REF], to predict functional components and molecular pathways [START_REF] Bromberg | Design Logic of a Cannabinoid Receptor Signaling Network That Triggers Neurite Outgrowth[END_REF][START_REF] Nakamura | An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds[END_REF], to perform network modularization [START_REF] Cabusora | Differential network expression during drug and stress response[END_REF][START_REF] Spirin | Protein complexes and functional modules in molecular networks[END_REF] and to predict protein function [START_REF] Arnau | Iterative Cluster Analysis of Protein Interaction Data[END_REF][START_REF] Sharan | Network-based prediction of protein function[END_REF]. We performed an experiment to check whether this approach can give biologically valid paths by comparing the shortest paths between all possible source and target points on eight canonical pathways with the canonical counterparts. We found that the shortest paths between these points built in the human interactome constructed from HPRD or HIPPIE databases usually do not match the canonical counterpart. But we also found that the nodes and the paths with centrality score ≥2 in the network built from the shortest path between these points belong to the canonical counterpart for many pairs of source and target points.

One possible way to enhance the shortest paths approach is to find first all possible lengthbounded paths (e.g with maximum length 5, most probably the maximum length should not be very big since at least signaling cascades are not long); then to use score exp and/or pvalue exp to filter these paths in order to create a set of the "strongest" paths instead of the shortest (these two steps are also implemented in masterPATH). However, this is more appropriate if genome-wide loss-of-function screening data is available, because genes important for the biological prosses might not show differential expression profile.

Conclusion

We presented here masterPATH method. MasterPATH is a new exploratory network analysis method to find potential members of molecular pathways important for a given phenotype, which can work on both protein-protein and integrated networks. The method employs the shortest path approach, centrality score and phenotype label permutation approach to find nodes and paths with significant centrality scores in the subnetwork that is constructed from the shortest paths between hit genes and so called "final implementers" or between hit genes. Centrality score of a node or a path was defined as the number of the shortest paths found in the previous step that pass through the node and the path. "Final implementers"

were defined as biological components involved in molecular events responsible for final realization of a given phenotype. It was hypothesized that the nodes and the paths with significant centrality score can be considered as putative members of molecular pathways leading to the studied phenotype. To illustrate the method, the data from the miRNA loss-offunction screening and transcriptomic profiling of terminal muscle differentiation and from 'druggable' loss-of-function screening of the DNA repair process were analyzed with the method. masterPATH found known and new interesting components for both biological systems. The method is implemented in Java and the source code is available on GitHub page https://github.com/daggoo/masterPATH.

Also, the shortest path approach was studied on the human interactome. It was shown that the shortest paths between source and target points located close to each other on a canonical signaling pathway usually matched the canonical counterpart. It was not true for source and target points located far apart to each other. However, nodes and paths with centrality scores ≥2 in a subnetwork constructed from the shortest paths between source and target points belonged to the canonical counterpart for a large fraction of source and target points. The documentation for this class was generated from the following file:

• CentralityManager.java

masterPATH.DBManager Class Reference

Public Member Functions

Print all information about interaction in one line

The documentation for this class was generated from the following file:

• Interaction.java

masterPATH.MasterPATH Class Reference Static Public Member Functions

• static void main (String[ ] args) throws FileNotFoundException, IOException, InterruptedException

Detailed Description

Main class

Author Natalia Rubanova The documentation for this class was generated from the following file:

Member Function Documentation

• Network.java

Merge list of networks

Parameters n List of networks

The documentation for this class was generated from the following file:

• NetworkManager.java

masterPATH.NetworkTopology Class Reference

Public Member Functions

Generated by Doxygen

The documentation for this class was generated from the following file:

• NetworkTopology.java

masterPATH.Node Class Reference

Public Member Functions The documentation for this class was generated from the following file:

• RandomManager.java

masterPATH.Wrapper Class Reference

Public Member Functions

FIGURE 1 - 1 .

 11 FIGURE 1-1. DSRNA AND MIRNA INTERFERENCE PATHWAY. ............................................................... FIGURE 1-2. THREE STAGES OF CRISPR-CAS IMMUNITY MECHANISM. ................................................

FIGURE 2 - 1 .

 21 FIGURE 2-1. OVERVIEW OF MASTERPATH. ...........................................................................................

FIGURE 2 - 2 .

 22 FIGURE 2-2. NF-ΚB SIGNAL TRANSDUCTION PATHWAY. .......................................................................

FIGURE 2 -

 2 FIGURE 2-3. MAPK SIGNAL TRANSDUCTION PATHWAY. .......................................................................

FIGURE 2 -

 2 FIGURE 2-4. JAK/STAT SIGNAL TRANSDUCTION PATHWAY. .................................................................

FIGURE 2 -

 2 FIGURE 2-5 MTOR SIGNAL TRANSDUCTION PATHWAY. .......................................................................

FIGURE 2 -

 2 FIGURE 2-6 ERBB SIGNAL TRANSDUCTION PATHWAY. .........................................................................

FIGURE 2 -

 2 FIGURE 2-7. WNT SIGNAL TRANSDUCTION PATHWAY. ........................................................................

FIGURE 2 -

 2 FIGURE 2-8. TGF-BETA SIGNAL TRANSDUCTION PATHWAY. .................................................................

FIGURE 2 -

 2 FIGURE 2-9. APOPTOTIC PATHWAY.......................................................................................................

FIGURE 2 -

 2 FIGURE 2-10. AVERAGE LENGTH IN NUMBER OF INTERACTIONS OF THE SHORTEST PATHS. ..............

FIGURE 2 -

 2 FIGURE 2-11. RELATIVE NUMBER OF CANONICAL PATHS FOR WHICH THE SET OF THE SHORTEST PATHS CONTAINS THE CANONICAL COUNTERPART. ....................................................................

FIGURE 2 -

 2 FIGURE 2-12. RELATIVE NUMBER OF THE CANONICAL PATHS FOR WHICH AT LEAST ONE NODE WITH CENTRALITY SCORE ≥ 2 IN THE SHORTEST PATHS SUBNETWORK BELONG TO THE CANONICAL PATH. ............................................................................................................................................

FIGURE 2 -

 2 FIGURE 2-13. RELATIVE NUMBER OF CANONICAL PATHS FOR WHICH AT LEAST ONE PATH WITH CENTRALITY SCORE ≥ 2 IN THE SHORTEST PATHS SUBNETWORK BELONG TO THE CANONICAL PATH. ............................................................................................................................................ FIGURE 2-14. TRANSCRIPTOME PROFILING: MAX -AP2M1 -EHD2 PATH. ...........................................FIGURE 2-15. CDKN1A-MDM2-TCAP-MSTN PATH. ...............................................................................

FIGURE 2 -

 2 FIGURE 2-16. THREE PATHS FOR MUSCLE DIFFERENTIATION PROCESS. ..............................................

FIGURE 2 -

 2 FIGURE 2-17. SUBNETWORKS FOR TWO PATHS WITH CENTRALITY SCORE 3. .....................................

  1. DESCRIPTION OF THE DATABASES. ..................................................................................... TABLE 2-2. 46 SIMPLE LINEAR PROTEIN-PROTEIN PATHS OF LENGTH 2-5 INTERACTIONS FOUND FOR 8 CANONICAL PATHWAYS ...............................................................................................................TABLE 2-3. TOPOLOGICAL PROPERTIES OF THE PPI AND THE INTEGRATED NETWORKS. .................... TABLE 2-4. MIRNA LOSS-OF-FUNCTION SCREENING: HIT LIST. ............................................................. TABLE 2-5. TRANSCRIPTOME PROFILING: HIT LIST. ..............................................................................TABLE 2-6. MIRNA LOSS-OF-FUNCTION SCREENING: TOP 50 NODES WITH THE HIGHEST CENTRALITY SCORES.......................................................................................................................................... TABLE 2-7. MIRNA LOSS-OF-FUNCTION SCREENING: TOP 50 PATHS WITH THE HIGHEST CENTRALITY SCORES.......................................................................................................................................... TABLE 2-8. TRANSCRIPTOME PROFILING: TOP 50 NODES WITH THE HIGHEST CENTRALITY SCORES AND MIRNAS. ................................................................................................................................ TABLE 2-9. TRANSCRIPTOME PROFILING: TOP 50 PATHS WITH THE HIGHEST CENTRALITY SCORES. .. TABLE 2-10. COMMON NODES FOR MIRNA LOSS-OF-FUNCTION SCREENING AND TRANSCRIPTOME PROFILING. .................................................................................................................................... TABLE 2-11. COMMON PATHS FOR LOSS-OF-FUNCTION SCREENING AND TRANSCRIPTOME PROFILING. .................................................................................................................................... TABLE 2-12. TRANSCRIPTOME PROFILING PATHS THAT PASS THROUGH MIRNA HITS. ....................... TABLE 2-13. COMPARISON: TOP 50 MIRNA LOSS-OF-FUNCTION SCREENING PATHS THAT PASS THROUGH TRANSCRIPTOME PROFILING HITS. ............................................................................. TABLE 2-14. DNA REPAIR LOSS-OF-FUNCTION SCREENING: HIT LIST. .................................................. TABLE 2-15. DNA REPAIR LOSS-OF-FUNCTION SCREENING: NODES WITH THE HIGHEST CENTRALITY SCORES.......................................................................................................................................... TABLE 2-16. DNA REPAIR LOSS-OF-FUNCTION SCREENING: TOP 50 PATHS WITH THE HIGHEST CENTRALITY SCORES. ....................................................................................................................

  figure 1-1).

Figure 1 - 1 .

 11 Figure 1-1. dsRNA and miRNA interference pathway.

  /adaptation stage -during this stage viral DNA is captured and is inserted into the CRISPR locus in a form of spacers. Two Cas proteins, Cas1 and Cas2, are observed in most of types of CRISPR-Cas systems (classification of CRISPR-Cas systems see below) and responsible for spacer acquisition.2) Expression/processing stage -during this stage CRISPR is transcribed into long precursor CRISPR RNA (pre-crRNA) which is then processed into mature crRNA with the help of Cas proteins and other factors depending on the type of CRISPR-Cas system.3) Interference stage -during this stage Cas protein(s) guided by crRNA destroy target nucleic acid.

Figure 1 - 2 .

 12 Figure 1-2. Three stages of CRISPR-Cas immunity mechanism. Adapted from (Yongmin Yan, 2013).

  distinguishes two classes -Class I and Class II mostly defined by different sets of cas genes present in CRISPR locus. Class I comprises types I, III and IV systems. Class II comprises types II, V systems. Several types of CRISPR/Cas system can be present in a genome. Class 1 CRISPR-Cas systems are defined by the presence of a multiprotein crRNA effector complex. The class includes type I, type III and type IV CRISPR-Cas systems.

Figure 2 - 1 .

 21 Figure 2-1. Overview of masterPATH.

  used to construct networks with only High Confidence interactions -HIPPIE HC (confidence level 0.73 -third quartile of the HIPPIE score) and with High and Medium Confidence interactions -HIPPIE HC+MC (confidence level 0.63 -second quartile of the score distribution).

  1.

  ). All possible combination of source and target points preserving the direction of signal transduction were found on these 55 paths that gave 245 paths of the length from 2 to 5 interactions: 91 paths of the length 2 of interactions, 74 paths of the length of 3 interactions, 51 paths of the length of 4 interactions and 29 paths of the length of 5 interactions.

Figure 2 - 2 .

 22 Figure 2-2. NF-κB signal transduction pathway. Nuclear factor-κB proteins are transcription factors that regulate wide range of biological processes including immunity, stress responses, inflammation and cell survival. The PPI part is highlighted with black line. Adapted from KEGG database http://www.genome.jp/keggbin/show_pathway?hsa04064

Figure 2 -

 2 Figure 2-10. Average length in number of interactions of the shortest paths.

Figure 2 -

 2 Figure 2-11. Relative number of canonical paths for which the set of the shortest paths contains the canonical counterpart.

Figure 2 -

 2 Figure 2-12. Relative number of the canonical paths for which at least one node with centrality score ≥ 2 in the shortest paths subnetwork belong to the canonical path.

Figure 2 -

 2 Figure 2-13. Relative number of canonical paths for which at least one path with centrality score ≥ 2 in the shortest paths subnetwork belong to the canonical path.

  2609 the shortest paths of 4 types of length from 2 to 5 interactions from each miRNA in the hit list from loss-of-function miRNA screening to each protein in the list of "final implementers". The subnetwork constructed from these paths consists of 1063 nodes and 2710 edges without duplicated edges. Then the centrality score and the p-value were calculated for each node and path. 240 nodes with centrality score ≥ 3 and with p-value < 0.05 (Table2-6); 294 paths of length of 3 to 4 interactions, with centrality score ≥ 3 and pvalue < 0.05 (Table2-7) were taken for manual analysis. Analysis of the paths with high centrality scores had highlighted a possible role for a number of nuclear receptors (AR, ARDB1, NR3C1) in skeletal muscle differentiation, as well as suggested functions in myogenesis for such proteins as arrestin (ARRB1 and 2), intersectin (ITSN1), the Rho GTP echange factor VAV3, and the teratocarcinoma-derived growth factor (TDGF1). Interestingly, while the IGF1 regulatory role in myogenesis is very well studied, our approach allowed us to include the arrestin proteins in these pathways, and thus elaborate the known IGF1 network in skeletal muscle differentiation. Obviously, the MEF2D, p300, CCND1 functions in differentiation have been abundantly demonstrated, and their presence among the results serves as a proof of efficiency of the analysis.It was found 47714 the shortest paths of 4 types and of length from 1 to 5 interactions from each gene in the hit list from transcriptome profiling to each protein in the list of "final implementers". The subnetwork constructed from these paths consists of 2847 nodes (303 of which are genes) and 13032 edges without duplicated edges. The centrality score and the p-value were calculated for each node and each path in the subnetwork. 328 nodes with centrality score ≥ 3 and p-value < 0.05(Table 2-8A); 1623 paths of length of 3 to 4

  regulation of myogenesis, involving the Myc-associated factor X (MAX), the clathrin-coated pathway regulatory protein AP2M1, and the EH-domain protein EHD2, which links the clathrin coated transport to actin cytoskeleton, and also binds to myoferlin, a factor 65 promoting myotube fusion. Together with integrin subunits ITGA4 and ITGB1, the extracellular matrix component fibronectin (FN1), and the protein chaperon HSP90, these proteins indicate a possible involvement of specific protein transport pathways in terminal myogenic differentiation. In addition, there is a possibility of involvement of betacatenin (CTNNB1), C-KIT and PRKC in these processes. It should be noted that these three regulatory factors, while extensively studied in a multitude of biological models, have never been shown to be specifically implicated in skeletal myogenesis. Two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, have been highlighted, together with their previously known muscle-related targets (TGFB1, CDC42, CTCF). Also, they are linked to a number of proteins that have not been previously studied in the context of muscle differentiation (MAX, KIT, AP2M1…).

Figure 2 -

 2 Figure 2-14. Transcriptome profiling: MAX -AP2M1 -EHD2 path. 'P' denotes a protein. Nodes in yellow are hit genes' products. Node in green is 'final implementer'.

  found, confirming the importance of certain membrane/adherence structures. Strikingly, both the receptor of activated C kinase (RACK1), and the inhibitor of this kinase (YWHAB, a 14-3-3 protein), as well as multiple other protein-processing enzymes (peptidase inhibitor SERPIN1, casein kinase CSNK1A1, proprotein convertase FURIN, and activator or protein secretion CHRM3) were found by the analysis, attracting the attention to the role of protein metabolism in myogenesis. It was also very interesting to see the chromosome breakpoint generation factor FRA11B among these potential novel factors that might impact on the differentiation of human myoblasts. This comparison has shown potentially novel paths originating from well-known actors in muscle differentiation (such as IGF1R -RACK1 -CD81); and vice versa, has shown that previously unknown potential regulators of myogenesis, such as YWHAB or FRA11B, can act upon proteins that are well known to regulate myotube hypertrophy and/or fusion (IGFR1, CD81).The fact that the comparison resulted only in a few number of paths might indicate, that although these two experimental systems study one biological process, they interfere biological machinery on two different levels -on the level of translation (miRNAs) and on the level on transcription (transcriptome), which might account on two different regulation mechanisms.

  -16 A), according to OMIM database, has highest expression levels in skeletal muscle (McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University (Baltimore, n.d.) and interacts with CDC42 protein. Also, analyzing these paths one can notice the factors participating in at least three major cellular pathways, that, however, have not been extensively studied in skeletal muscle differentiation. These factors include beta-transduction repeat containing protein (BTRC), which has a strong impact on both beta-catenin and NF-kappa B signaling (Figure 2-16 B), as well as the p53-related

Figure 2 -

 2 Figure 2-16. Three paths for muscle differentiation process.'p' denotes a protein. Nodes in yellow are hit genes' products; node in green is 'final implementer'.

Figure 2 -

 2 Figure 2-17. Subnetworks for two paths with centrality score 3. The hit proteins are colored pink, the intermediate proteins are colored blue. The grey arrows show the direction of the paths when it is built by the method. The red arrows show the direction and the effect of the interaction found in literature.

Figure 1 .Figure 2 .

 12 Figure 1. The shortest paths network for ARP2/3 complex -LIMCH1 protein

Figure 3 .

 3 Figure 3. The shortest paths network for LIMCH1 -RB1 proteins.

Figure 4 .

 4 Figure 4. The shortest paths network for RAC complex -ARPIN protein.

Figure 6 .

 6 Figure 6. A path with high centrality score.

Table 2 -

 2 1. Description of the databases. PPI stands for Protein-Protein Interactions, TF stands for Transcription Factor.

				Types	Types	Direction
		Nodes	Interactions	of	of	of
				nodes	interactions	interactions
	HPRD	9 648	39 156	proteins	PPI	indirect
	HIPPIE	16 403	237 958	proteins	PPI	indirect
	HIPPIE
	(high and medium	15 857	193 576	proteins	PPI	indirect
	confidence)
	HIPPIE
	(high	9368	41520	proteins	PPI	indirect
	confidence)
				proteins,
	Signor	3977	13129	complexes, small	PPI, enzymatic	direct, indirect
				molecules
	SignaLink	3285	27295	proteins, genes, miRNAs	PPI, miRNA-mRNA, TF-gene	direct, indirect
	tFacts	2203	4312	TFs, genes	TF-gene	direct
	KEGG metabolic pathways	2921	8231	proteins, molecules small	reactions Enzymatic	direct
	transMir	324	647	TFs, miRNAs	TF-miRNA	direct
	mirTarBase	2269	3511	miRNAs, genes	miRNA-mRNA	direct

Table 2 -

 2 3 gives the information about the basic topological properties for the PPI and integrated networks. The exponent of the fitted power-law distribution in the degree distribution was calculated with powerlaw[START_REF] Alstott | Powerlaw: A python package for analysis of heavy-tailed distributions[END_REF] Python package.

Table 2 -

 2 3. Topological properties of the PPI and the integrated networks.

		HPRD	HIPPIE	HIPPIE	HIPPIE HC Integrated
				HC+MC		network
	Nodes	9 648	16 403	15 857	9 365	13 419
	Interactions	39 156	237 958	193 576	41 329	85 277
	Average clustering coefficient	0.2	0.26	0.23	0.31	0.11
	Number of connected	112	9	17	121	126
	components
	Average number of	7.8	28.6	24	8.6	9.2
	neighbors
	Density	0.0008	0.002	0.002	0.0009	0.0007
	Diameter	14	8	8	12	25
	Average length of the	4.2	3.2	3.3	4.1	4.5
	shortest path
	Exponent of fitted power-law	2.71	3.31	2.62	2.74	3.16
	distribution (total degree)

Table 2 -

 2 4. miRNA loss-of-function screening: hit list.

Table 2 -

 2 5. Transcriptome profiling: hit list.

Table 2 -

 2 8. Transcriptome profiling: top 50 nodes with the highest centrality scores and

	miRNAs.
	(A) Top 50 nodes with the highest centrality scores.

Table 2 -

 2 9. Transcriptome profiling: top 50 paths with the highest centrality scores. lists of nodes and the lists of paths found for loss-of-function miRNA screening and transcriptomic profiling were compared. It was found 29 nodes (Table2-10) and 16 paths (Table2-11) common for both for transcriptomic profiling and loss-of-function screening. 3 out of 5 nodes with the highest centrality score -IGF1R, CBL, E2F1 -have been suggested to play key roles in the growth, development, and differentiation of skeletal muscle(Fernández

	Centrality
	count

Table 2 -

 2 10. Common nodes for miRNA loss-of-function screening and transcriptome profiling.

(TF -transcriptome profiling; miRNA LOF -miRNA loss-of-function screening)

Table 2 -

 2

	Centrality	Centrality score	Node's		Centrality	Centrality score	Node's
	score TF		miRNA LOF	name		score TF	miRNA LOF	name
		151		127 pIGF1R		20	8 TGFBR1
		192		28 pCBL		5	17 HNF4A
				119 ESR1		16	5 pBNIPL
				121 IGF1R		10	10 pIGF2R
				60 E2F1		8	7 pACVR2B
				27 pACVR1B		10	3 pRARG
				14 ERRFI1		8	5 YWHAQ
				10 pIGF2		10	2 pITGB4
				16 CDKN1A		5	7 SOCS6
				4 pSOS1		6	5 pBIRC6
				9 CLTC		8	2 ITGB4
				33 FSCN1		7	3 FURIN
				3 MET		3	6 CSNK1A1
				5 AR		2	7 SNX6
				7 pITGA6		4	2 PNP
				3 pFURIN		2	4 KRT7
				3 PAK5		2	4 SMURF1
				6 pSERPINE1
	Length	Centrality score TP	Centrality score miRNA LOF			Path
	3			7	E2F1-pE2F1	pSTAT1-pE2F1	pKIT-pSTAT1
	4			6	E2F1-pE2F1	pMDM2-pE2F1	pTCAP-pMDM2	pTCAP-pMSTN
	3			6	E2F1-pE2F1	pMDM2-pE2F1	pTCAP-pMDM2
	4			5	CDKN1A-pCDKN1A pCDKN1A-pMDM2 pTCAP-pMDM2	pTCAP-pMSTN
	3			5	CDKN1A-pCDKN1A pCDKN1A-pMDM2 pTCAP-pMDM2
	3			4	pIGF1R-pRASA1	pRASA1-pKIT	pCD81-pKIT
	3			4	pRACK1-pIGF1R	pRACK1-pITGB1	pCD81-pITGB1
	3			4	pIGF1R-pMDM2	pMDM2-pHNRNPD pCD81-pHNRNPD
	3			4	pYWHAB-pIGF1R	pYWHAB-pITGB1	pCD81-pITGB1
	3			4	pFRA11B-pIGF1R	pKIT-pFRA11B	pCD81-pKIT
	3			4	pIGF1R-pCRK	pCRK-pKIT	pCD81-pKIT
	3			4	pIGF1R-pPIK3R2	pPIK3R2-pKIT	pCD81-pKIT
	3			4	pPTPN11-pIGF1R	pPTPN11-pKIT	pCD81-pKIT
	3			4	pIGF1R-pESR1	pESR1-pHNRNPD	pCD81-pHNRNPD
	3			4	pCRKL-pIGF1R	pCRKL-pKIT	pCD81-pKIT
	4			4	E2F1-pE2F1	pE2F1-pBRCA1	pHNRNPD-pBRCA1	pCD81-pHNRNPD
	3			3	CSNK1A1-pCSNK1A1 pCSNK1A1-pCHRM3 pARF6-pCHRM3

11. Common paths for loss-of-function screening and transcriptome profiling.

(TF -transcriptome profiling; miRNA LOF -miRNA loss-of-function screening.) 71 Figure 2-15. Comparison: CDKN1A-MDM2-TCAP-MSTN path.

'P' denotes a protein. Nodes in yellow are hit genes' products; node in green is 'final implementer'.

Table 2 -

 2

14. DNA repair loss-of-function screening: hit list.

Table 2 -

 2 15. DNA repair loss-of-function screening: nodes with the highest centrality scores.

	Node's name	Centrality score	pvalue	Node's name	Centrality score	pvalue
	pHIST1H3B	324 0.0462	pSETDB1		0.0291
	pHIST1H3F	324 0.0462	pRBBP7		0.0349
	pHIST1H3A	324 0.0463	pCXXC1		0.0448
	pHIST1H3D	324 0.0463	pATM		0.0367
	pHIST1H3C	324 0.0463	pCHAF1A		0.0342
	pHIST1H3G	324 0.0467	pING2		0.0317
	pHIST1H3J	324 0.0469	pMDC1		0.0405
	pHIST1H3E	324 0.0472	pHIST2H2AC		0.0401
	pHIST1H3H	324 0.0473	pRFC1		0.0156
	pHIST1H3I	324 0.0476	pMSH6		0.0308
	pMCM6	318 0.0059	pMXD1		0.0176
	pWDR5	148 0.0409	pPOLA1		0.0474
	pTP53BP1	90 0.0187	pNDC80		0.0492
	pAURKB	85 0.0409	pKMT2B		0.0344

Table 1 .

 1 Paths with centrality scores.Find short linear paths in a mixed direct/indirect subnetwork and calculate centrality scores

	Centrality score

Here are the classes, structs, unions and interfaces with brief descriptions: masterPATH.CentralityManager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.DBManager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.FoldersPaths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.MasterPATH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.NetworkManager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.NetworkTopology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.PathwayManager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.RandomManager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . masterPATH.Wrapper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . • void calculate_centrality_scores_for_nodes ( Network all, Map< String, String[ ]> hugo_by_id, NetworkManager nutils, String f_shortest_pathways, String output_file) throws FileNotFoundException, IOException • void calculate_centrality_scores_for_paths ( Network all, String inf, String outf, int min_length, int max← _length) throws FileNotFoundException, IOException • void calculate_centrality_scores_for_paths_ppi ( Network all, String inf, String outf, int min_length, int max_length) throws FileNotFoundException, IOException

• void add_hgnc_symbols_to_paths (String inf, String outf, Network all, Map< String, String[ ]> hugo_← by_id) throws IOException • void add_experimental_scores_and_pvalues_to_nodes (String nodes_file, String gw_file, String conv_← table, String out) throws FileNotFoundException, IOException • void add_experimnetal_scores_and_aggregated_pvalues_to_paths (String unique_over, String gw_file, String conv_table, String out) throws FileNotFoundException, IOException • void calculate_paths_degree (String unique_over, String path_con, String out) int number_of_permutations ) throws FileNotFoundException, IOException Calculate empirical distribution for Fisher's statistic

3 Member Function Documentation 2.2.3.1 loadHIPPIE()

	• void loadSignor () throws FileNotFoundException, IOException 2.2.3.3 loadHUGO() Parameters
	id	• void loadSignalink () throws FileNotFoundException, IOException id of the interaction
	• void loadtFacts () throws FileNotFoundException, IOException void masterPATH.DBManager.loadHUGO ( ) throws FileNotFoundException, IOException other_ids ids from the source databases • void loadKEGG () throws FileNotFoundException, IOException int1 first interactor • void loadTransmir () throws FileNotFoundException, IOException Loads HGNC nomenclature int2 second interactor • void loadmirTarBase () throws FileNotFoundException, IOException type type of the interaction
	sourcedb	source database
	2.2.3.4 loadKEGG() sourcedbentry entry in the source database 2.2.1 Detailed Description quality confidence parameter of the interaction
	void masterPATH.DBManager.loadKEGG ( ) throws FileNotFoundException, IOException dir direct or indirect interaction
	DBManager (p. 9) class contains methods to load databases
	Load KEGG database
	Author
	2.2.3.5 loadmirTarBase() Natalia Rubanova 2.4.3 Member Function Documentation
	2.2.2 Constructor & Destructor Documentation void masterPATH.DBManager.loadmirTarBase ( ) throws FileNotFoundException, IOException
	Load mitTarBase database 2.4.3.1 print()
	2.2.3.6 loadSignalink()
	void masterPATH.DBManager.loadSignalink ( ) throws FileNotFoundException, IOException
	Load Signalink database
	2.2.3.7 loadSignor()
	void masterPATH.DBManager.loadSignor ( ) throws FileNotFoundException, IOException
	Load Signor database
	2.2.3.8 loadtFacts()
	void masterPATH.DBManager.loadHIPPIE (
	boolean high ) throws FileNotFoundException, IOException void masterPATH.DBManager.loadtFacts ( ) throws FileNotFoundException, IOException
	Load HIPPIE database Load tFacts database
	Parameters
	high load all or only high confidence interactions 2.2.3.9 loadTransmir()
	void masterPATH.DBManager.loadTransmir ( ) throws FileNotFoundException, IOException
	Load TransMir database
	2.2.3.2 loadHPRD()
	The documentation for this class was generated from the following file:
		• DBManager.java
			Generated by Doxygen

• DBManager () throws IOException • void loadHUGO () throws FileNotFoundException, IOException • void loadHPRD () throws FileNotFoundException, IOException • void loadHIPPIE (boolean high) throws FileNotFoundException, IOException 2.2.2.1 DBManager() masterPATH.DBManager.DBManager ( ) throws IOException Constructor 2.2.void masterPATH.DBManager.loadHPRD ( ) throws FileNotFoundException, IOException Load HPRD database void masterPATH.Interaction.print ( )

2 Constructor & Destructor Documentation 2.6.2.1 Network()

	Parameters Parameters
	args nutils	NetworkManager (p. 16) object
	putils	PathwayManager (p. 24) object
	outils	CentralityManager (p. 3) object
	The documentation for this class was generated from the following file: dbutils DBManager (p. 9) object
	• MasterPATH.java
	2.6 masterPATH.Network Class Reference
	Public Member Functions 2.6.3.2 loadNetworkfromfile()
	• Network () void masterPATH.Network.loadNetworkfromfile (
	• void loadNetworkfromfile (String nodef, String intf) throws FileNotFoundException, IOException String nodef,
	• void saveNetworktofile (String nodef, String intf) throws IOException String intf ) throws FileNotFoundException, IOException
	• void removeAll ()
	• void loadNetwork2 ( NetworkManager nutils, PathwayManager putils, CentralityManager outils, DB← Manager dbutils) throws IOException Load network from two files *
	Parameters
	nodef file with information about nodes 2.6.1 Detailed Description intf file with information about interactions
	Author
	Natalia Rubanova
	2.6.3.3 removeAll()
	void masterPATH.Network.removeAll ( ) Remove all interactions and nodes from network 2.6.masterPATH.Network.Network ( ) 2.6.3.4 saveNetworktofile()
	Constructor
	void masterPATH.Network.saveNetworktofile (
		String nodef,
	2.6.3 Member Function Documentation String intf ) throws IOException
	Save all interactions and nodes in the network to the two output files
	Parameters
	2.6.3.1 loadNetwork2() nodef node file
	intf	interaction file
	2.5.2.1 main() void masterPATH.Network.loadNetwork2 (
		NetworkManager nutils,
	static void masterPATH.MasterPATH.main ( PathwayManager putils,
		String [ ] args ) throws FileNotFoundException, IOException, InterruptedException CentralityManager outils,
	[static]	DBManager dbutils ) throws IOException
	main method Load network from databases
		Generated by Doxygen

Network (p. 14) class describes an Network (p. 14) object

2.7 masterPATH.NetworkManager Class Reference Classes

  Network network, List< String > n1, List< String > n2, int max, String prefix, FileWriter out) throws IOException • List< PathUnit > find_pathway_BF_algorithm_ppi ( Network network, List< String > n1, List< String > n2, int max, String prefix, BufferedWriter out) throws IOException • void find_pathway_for_list_BF_algorithm ( Network nw, Map< String, String[ ]> hugo, int max, String outf, String prefix) throws IOException • void find_pathway_for_list_BF_algorithm_ppi ( Network nw, Map< String, String[ ]> hugo, int max, String outf, String prefix) throws IOException • void add_missing_genes_and_products ( Network nw)

	• class Depth_List_of_PathUnit
	• class PathUnit
	• class PathUnitwFP
	Public Member Functions
	• Network merge_list_of_networks (List< Network > n)
	• void build_map_of_neighbors ( Network nw)
	• void load_hitlist_and_finalimpl (String hgf, String fpf, Map< String, String[ ]> hugo) throws FileNotFound←
	Exception, IOException
	• List< PathUnit > find_pathway_BF_algorithm ( 2.7.1 Detailed Description
	NetworkManager (p. 16) class contains methods to deal with networks
	Author
	Natalia Rubanova
	2

.7.2 Member Function Documentation 2.7.2.1 add_missing_genes_and_products()

	void masterPATH.NetworkManager.add_missing_genes_and_products (
	Network nw )
	Add transcription information
	Parameters
	nw Network (p. 14)

Generated by Doxygen 2.7.2.2 build_map_of_neighbors()

	Parameters Parameters
	n1 max		List of Hit genes nodes Maximum length
	void masterPATH.NetworkManager.build_map_of_neighbors ( n2 List of Final implementers nodes outf Output file
	Network nw ) Maximum length prefix Prefix for interaction id max
	prefix Create links to all neighbors for every node in the network Prefix for interaction id out output file
	Parameters
	nw Network (p. 14)
	Arrays.equals(visited_nodes.get(n), visited_nodes.get(curn.current_seed)) || || path_type >= 1
	2.7.2.7 load_hitlist_and_finalimpl()
	Arrays.equals(visited_nodes.get(n), visited_nodes.get(curn.current_seed)) || || path_type >= 1
	void masterPATH.NetworkManager.load_hitlist_and_finalimpl ( tmp_visited_nodes.containsKey(n) String hgf,
			String fpf,
	2.7.2.3 find_pathway_BF_algorithm() Map< String, String[ ]> hugo ) throws FileNotFoundException, IOException
	2.7.2.5 find_pathway_for_list_BF_algorithm()
	List<PathUnit> masterPATH.NetworkManager.find_pathway_BF_algorithm ( Load hit genes and final implementers files
	Network network, Network nw, List< String > n1, void masterPATH.NetworkManager.find_pathway_for_list_BF_algorithm ( Parameters
	hgf	List< String > n2, Map< String, String[ ]> hugo, Hit genes file
	fpf	int max, int max, Final players file
	String prefix, String prefix ) throws IOException FileWriter out ) throws IOException String outf, hugo Link to the HGNC nomenclature
	Find length-bound pathways between two lists of nodes Wrapper (p. 38) for finding pathways
	Parameters Parameters
	network nw		Network (p. 14)
	n1 hugo 2.7.2.8 merge_list_of_networks() List of Hit genes nodes Link to HGNC nomenclature
	n2 max		List of Final implementers nodes Maximum length
	max outf		Maximum length Output file
	prefix prefix Prefix for interaction id Prefix for interaction id
	out		Output file
	tmp_visited_nodes.containsKey(n)
	2.7.2.6 find_pathway_for_list_BF_algorithm_ppi()
	2.7.2.4 find_pathway_BF_algorithm_ppi()
	void masterPATH.NetworkManager.find_pathway_for_list_BF_algorithm_ppi (
	List<PathUnit> masterPATH.NetworkManager.find_pathway_BF_algorithm_ppi ( Network nw,
			Network network, Map< String, String[ ]> hugo,
			List< String > n1, int max,
			List< String > n2, String outf,
			int max, String prefix ) throws IOException
			String prefix,
	BufferedWriter out ) throws IOException Wrapper (p. 38) for finding pathways in a PPI network
	Find a pathway between two lists of nodes in a PPI network Parameters
	Parameters nw	Network (p. 14)
	network hugo		Link to HGNC nomenclature

Network masterPATH.NetworkManager.merge_list_of_networks ( List< Network > n )

  Network all, String outfile) throws IOException • void get_degree_distribution_direct ( Network all, String outfile) throws IOException • void get_subnetwork_statistics ( Network all, String fname) throws FileNotFoundException, IOException

	2.8.2.3 calculate_diameter_direct() Parameters
	all Network (p. 14)
	void masterPATH.NetworkTopology.calculate_diameter_direct (
		Network all )
	Calculate diameter of a direct network
	Parameters
	all Network (p. 14) 2.8.2.7 get_degree_distribution_direct()
	2.8.1 Detailed Description
	void masterPATH.NetworkTopology.get_degree_distribution_direct (
	NetworkTopology (p. 19) class contains methods to calculate network's topological properties Network all,
		String outfile ) throws IOException
	Author Build degree distribution for direct network
	2.8.2.4 calculate_diameter_ppi() Natalia Rubanova Parameters
	void masterPATH.NetworkTopology.calculate_diameter_ppi ( all Network (p. 14)
	2.8.2 Member Function Documentation Network all ) outfile Output file
	Calculate diameter
	Parameters
	all Network (p. 14)
	2.8.2.1 calculate_average_clustering_coefficient_direct()
	2.8.2.8 get_degree_distribution_ppi()
	void masterPATH.NetworkTopology.calculate_average_clustering_coefficient_direct (
	Network all ) void masterPATH.NetworkTopology.get_degree_distribution_ppi (
		Network all,
	Calculate average clustering coefficient for direct network 2.8.2.5 calculate_number_of_connected_components_direct() String outfile ) throws IOException
	Parameters Build degree distribution for PPI network
	void masterPATH.NetworkTopology.calculate_number_of_connected_components_direct ( all Network (p. 14) Network all ) Parameters
	all	Network (p. 14)
	Calculate number of connected components for direct network outfile Output file
	Parameters
	all Network (p. 14)
	2.8.2.2 calculate_average_clustering_coefficient_ppi()
	void masterPATH.NetworkTopology.calculate_average_clustering_coefficient_ppi (
	Network all ) 2.8.2.9 get_subnetwork_statistics()
	Calculate average clustering coefficient for PPI network 2.8.2.6 calculate_number_of_connected_components_ppi() void masterPATH.NetworkTopology.get_subnetwork_statistics (
		Network all,
	Parameters	String fname ) throws FileNotFoundException, IOException
	all Network (p. 14) void masterPATH.NetworkTopology.calculate_number_of_connected_components_ppi (
	Network all ) Calculate subnetwork properties
	Calculate number of connected components PPI network Parameters
	all	Network (p. 14)
	fname File with pathways to create subnetwork
		Generated by Doxygen

• void calculate_average_clustering_coefficient_ppi ( Network all) • void calculate_average_clustering_coefficient_direct ( Network all) • void calculate_number_of_connected_components_ppi ( Network all) • void calculate_number_of_connected_components_direct ( Network all) • void calculate_diameter_ppi ( Network all) • void calculate_diameter_direct ( Network all) • void get_degree_distribution_ppi (

.10.2.10 filterPathways_by_centrality_score()

  Network all, Map< String, String > hg, Map< String, String > fpl, int d_ppi, int d_tf, int d_mirna, int d_kegg) throws FileNotFoundException, IO← Network all, Map< String, String > hg, Map< String, String > fpl) throws FileNotFoundException, IOException • void create_cyto_for_pathways (String path_file, String filename, String out_dir, Network all, Map< String, String > hg, Map< String, String > fpl) throws FileNotFoundException, IOException • void filterPathways_by_length (String inf, String outf, int min, int max) throws IOException • void filterPathways_by_centrality_score (String inf, String outf, int min, int max) throws IOException • void filterPathways (String foundf, String outname, Network all, Map< String, String > hg, Map< String, String > fpl, String mask, String gene, String type) throws FileNotFoundException, IOException • void filterPathways_2 (String foundf, String outname, Network all, Map< String, String > hg, Map< String,

			Class Documentation Class Documentation Class Documentation
	2.9.3 Member Function Documentation 2.10.1 Detailed Description Parameters 2.10.2.7 filter_pathways_by_connectivity() Network mirtarbase, 2.10.2.15 find_the_shortest_paths()
	all PathwayManager (p. 24) class contains methods to deal with pathways Network transmir, Network (p. 14) Map< String, String > hg, infile Input file outfile Output file 2.10.2.5 create_cyto_for_paths() void masterPATH.PathwayManager.filter_pathways_by_connectivity ( String in, 2.10.2.9 filterPathways_2() void masterPATH.PathwayManager.find_the_shortest_paths ( Map< String, String > fpl, 2.10.2.11 filterPathways_by_length() int length, String foundf,
	String out, String outname, int min_occ, void masterPATH.PathwayManager.create_cyto_for_paths ( Author String out_for_overrreps, void masterPATH.PathwayManager.filterPathways_2 ( void masterPATH.PathwayManager.filterPathways_by_length ( Network all, String mask ) throws FileNotFoundException, IOException 2.9.3.1 print() Natalia Rubanova String foundf, int in_min, String foundf, String inf, Map< String, String > hg,
	Network all, int in_max, String outname, String outf, Find and rank miRNAs inside paths Map< String, String > fpl,
	void masterPATH.Node.print ( ) Map< String, String > hg, int out_min, Network all, int min, int d_ppi,
	• Node (String id, String type, String id_type, String db_flag, List< String[ ]> ids) • String toString () • void print () Print node information in one line Map< String, String > fpl ) throws FileNotFoundException, IOException int out_max ) throws FileNotFoundException, IOException Map< String, String > hg, int max ) throws IOException Parameters int d_tf, 2.10.2 Member Function Documentation 2.10.2.3 compare_two_nodes_files() Create a file for Cytoscape software from the shortest paths Filter pathways by connectivity int d_mirna, Map< String, String > fpl, String gene, foundf Common paths file int d_kegg ) throws FileNotFoundException, IOException Filter pathways by length resf Output file int l1, The documentation for this class was generated from the following file: void masterPATH.PathwayManager.compare_two_nodes_files ( Parameters Parameters int l2, Parameters mirtarbase Link to mirTarBAse network Find the shortest pathways
	2.9.1 Detailed Description Node (p. 23) class describes a Node (p. 23) object Author Natalia Rubanova 2.9.2 Constructor & Destructor Documentation 2.9.2.1 Node() masterPATH.Node.Node ( String id, String type, String id_type, String db_flag, List< String[ ]> ids ) Constructor Parameters id id of the Node (p. 23) type type of the Node (p. 23) id_type type of the id(nomenclature) db_flag flag of the source database ids ids from other nomenclatures • Node.java 2.10.2.1 add_aggregated_pvalues_to_pathways() void masterPATH.PathwayManager.add_aggregated_pvalues_to_pathways ( Network all, Network all, Map< String, String[ ]> hugo_by_id, String f1, String f2, String out ) throws FileNotFoundException, IOException foundf File with paths all Network (p. 14) hg Hit genes list fpl Final players list in Input file out Output file out_for_overrreps Output file only for pathway without additional information in_min Minimum inward connectivity in_max Maximum inward connectivity int l3, int l4 ) throws FileNotFoundException, IOException Filter paths on condition ppi [3,4] & miRNA [6/5] or ppi [3,4] or miRNA [6/5] Parameters inf Input file outf transmir Link to transMir network hg Parameters Hit genes list Output file min fpl foundf Input file Final players List Minimum length length outname Output file Maximum length max Maximum length min_occ all Network (p. 14) Minimum occurrence 2.10 masterPATH.PathwayManager Class Reference String f_pathways, String gw_file, String conv_table, Compare two files with nodes Parameters Exceptions out_min Minimum outward connectivity out_max foundf mask hg Hit genes list Mask File with pathways fpl Final players list Maximum outward connectivity outname Output file d_ppi Length gap for protein-protein interactions Public Member Functions Exception • void find_the_strongest_pathways (String foundf, String outfile) throws IOException • void find_miRNAs_on_pathways (String foundf, String resf, Network mirtarbase, Map< String, String > hg, Map< String, String > fpl, int length, int min, String mask) throws FileNotFoundException, IOException • void find_miRNAs_on_paths (String foundf, String resf, Network mirtarbase, Network transmir, Map< String out, Map< String, String[ ]> hugo_by_id, CentralityManager centralityManager ) throws IOException Parameters all all Network (p. 14) hugo_by← _id HGNC ids map f1 File 1 f2 File 2 FileNotFoundException IOException Exceptions FileNotFoundException IOException all Network (p. 14) hg d_tf Length gap for transcriptional interactions Hit genes fpl d_mirna Length gap for miRNA-mRNA interactions Final implementers gene 2.10.2.12 find_hitgenes_on_paths() d_kegg Length gap for metabolic interactions Gene name l1 2.10.2.14 find_miRNAs_on_pathways() min length for ppi path void masterPATH.PathwayManager.find_hitgenes_on_paths ( the value of all f_pathways out Output file l2 max length for ppi path Network all, the value of f_pathways gw_file l3 min length for miRNA path void masterPATH.PathwayManager.find_miRNAs_on_pathways ( NetworkManager nutils, the value of gw_file conv_table the value of conv_table 2.10.2.6 create_cyto_for_pathways() l4 String foundf, Map< String, String[ ]> hugo_by_id, max length for miRNA path String f1, String resf, String, String > hg, Map< String, String > fpl, int length, int min_occ, String mask) throws FileNotFound← Exception, IOException out the value of out hugo_by_id the value of hugo_by_id centralityManager the value of centralityManager 2.10.2.4 compare_two_paths_files() void masterPATH.PathwayManager.create_cyto_for_pathways ( String path_file, String filename, String out_dir, 2.10.2.8 filterPathways() void masterPATH.PathwayManager.filterPathways ( String out ) throws FileNotFoundException, IOException Compare a file with paths with a hit list Parameters Network mirtarbase, 2.10.2.16 find_the_strongest_pathways() Map< String, String > hg, Map< String, String > fpl, int length, int min, String mask ) throws FileNotFoundException, IOException void masterPATH.PathwayManager.find_the_strongest_pathways ( String foundf, String outfile ) throws IOException • void create_cyto_for_paths (String foundf, String > fpl, String gene, int l1, int l2, int l3, int l4) throws FileNotFoundException, IOException Exceptions IOException void masterPATH.PathwayManager.compare_two_paths_files ( Network all, Map< String, String[ ]> hugo_by_id, String f1, String f2, String out ) throws FileNotFoundException, IOException Network all, Map< String, String > hg, Map< String, String > fpl ) throws FileNotFoundException, IOException Creates a file for Cytoscape software for a list of pathways by pathways ids Parameters String foundf, String outname, Network all, Map< String, String > hg, all Network (p. 14) nutils NetworkManager (p. 16) object Find and rank miRNAs inside pathways Find the strongest pathways 2void masterPATH.PathwayManager.filterPathways_by_centrality_score ( hugo_by← HGNC ids map Parameters Parameters _id String inf, Map< String, String > fpl, String outf, f1 File with paths foundf File with common paths foundf Input file String mask, String gene, int min, out OUtput file resf Output file outname Output file • void add_connectivity_to_pathways ( Network all, String infile, String outfile) throws IOException • void filter_pathways_by_connectivity (String in, String out, String out_for_overrreps, int in_min, int in_max, int out_min, int out_max) throws FileNotFoundException, IOException • void compare_two_paths_files ( Network all, Map< String, String[ ]> hugo_by_id, String f1, String f2, String out) throws FileNotFoundException, IOException • void compare_two_nodes_files ( Network all, Map< String, String[ ]> hugo_by_id, String f1, String f2, String out) throws FileNotFoundException, IOException • void find_hitgenes_on_paths ( Network all, NetworkManager nutils, Map< String, String[ ]> hugo_by_id, String f1, String out) throws FileNotFoundException, IOException • void add_aggregated_pvalues_to_pathways ( Network all, String f_pathways, String gw_file, String conv_table, String out, Map< String, String[ ]> hugo_by_id, CentralityManager centralityManager) throws IOException 2.10.2.2 add_connectivity_to_pathways() void masterPATH.PathwayManager.add_connectivity_to_pathways ( Network all, String infile, String outfile ) throws IOException Calculate pathways connectivity Compare two files with paths Parameters all Network (p. 14) hugo_by← _id Map with HGNC ids f1 String type ) throws FileNotFoundException, IOException int max ) throws IOException mirtarbase Link to mirTarBase network path_file File with pathways hg Hit genes list filename File with list of pathways by id out_dir Output folder all Network (p. 14) hg Hit genes list fpl Final players list Filter pathways by node names \ type Filter pathways by length Parameters fpl Final implementer list length The documentation for this class was generated from the following file: Maximum length Parameters foundf Input file outname Output file inf min Input file outf 2.10.2.13 find_miRNAs_on_paths() • PathwayManager.java mask Output file File1 f2 all Network (p. 14) min Minimum length File 2 out OUtput file hg Hit genes list fpl String resf, Final players list max Maximum length void masterPATH.PathwayManager.find_miRNAs_on_paths ( String foundf, 2.
	mask	Mask
	Generated by Doxygen gene Node (p. 23) name	Generated by Doxygen
	type	Node (p. 23) type

• void find_the_shortest_paths (String foundf, String outname,

11 masterPATH.RandomManager Class Reference Generated by Doxygen

	Parameters Parameters 2.11.2.8 permute_phenotype_label() PathwayManager putils, NetworkManager nutils,
	all outils		Network (p. 14) CentralityManager outils, CentralityManager (p. 3) object PathwayManager putils,
			DBManager dbutils, CentralityManager outils,
	nutils dbutils void masterPATH.RandomManager.permute_phenotype_label ( NetworkManager (p. 16) object String random_paths_file, DBManager (p. 9) object DBManager dbutils,
	putils random_paths_file String ovrreps_paths_file, PathwayManager (p. 24) object File name for pathways from shuffled hit genes String random_paths_file, Network all,
	outils ovrreps_paths_file String hitlists_file, CentralityManager (p. 3) object File with paths String ovrreps_paths_file, NetworkManager nutils,
	dbutils file_name_random Name for file to store information about pathways built on the random network DBManager (p. 9) object int hitlist_length, int position_of_overreps_count, Map< String, String[ ]> hugo_by_id ) throws IOException hitlists_file PathwayManager putils, String hitlists_file, File with hit genes hitlist_length CentralityManager outils, int hitlist_length, Length of hit list int position_of_overreps_count, DBManager dbutils,
	count position_of_overreps_count Position of the centrality count in the paths file Number of shuffling steps Map< String, String[ ]> hugo_by_id ) throws IOException String folder,
	hubs_file Parameters hugo_by_id	File with nodes and centrality scores HGNC ids map String prefix,
	overreps_file all Calculate p-values for paths for shuffled phenotype label for ppi network File with paths and centrality scores int count, List< String > hit_list,
	nutils Parameters putils		Map< String, String[ ]> hugo_by_id, String fplayers ) throws IOException
	outils all		Network (p. 14)
	dbutils nutils Permute phenotype label	NetworkManager (p. 16) object
	random_paths_file ovrreps_paths_file putils 2.11.2.5 calculate_p_values_phenotype_label_permutation_nodes() PathwayManager (p. 24) object outils Parameters CentralityManager (p. 3) object 2.11.2.2 calculate_p_values_nodes_random_networks_ppi() hitlists_file dbutils DBManager (p. 9) object all Network (p. 14)
	void masterPATH.RandomManager.calculate_p_values_nodes_random_networks_ppi ( hitlist_length position_of_overreps_count void masterPATH.RandomManager.calculate_p_values_phenotype_label_permutation_nodes ( random_paths_file nutils NetworkManager (p. 16) object File name for pathways from shuffled hit genes Network all, NetworkManager nutils, ovrreps_paths_file putils PathwayManager (p. 24) object File with paths Network all, NetworkManager nutils, hugo_by_id PathwayManager putils, hitlists_file File with hit genes outils CentralityManager (p. 3) object
	hitlist_length dbutils	PathwayManager putils, CentralityManager outils, Length of hit list DBManager (p. 9) object
	CentralityManager outils, DBManager dbutils, DBManager dbutils, Folder to store information about pathways position_of_overreps_count Position of the centrality count in the paths file Exceptions folder String random_paths_file, String ovrreps_paths_file, hugo_by_id prefix Prefix for pathways ids HGNC ids map String file_name_random, int count, String hubs_file, String hitlists_file, count Number of shuffling steps IOException int hitlist_length, hit_list Hit List
	hugo_by←	String overreps_file ) throws IOException Map< String, String[ ]> hugo_by_id ) throws IOException HGNC ids map
	_id
	Calculate p-values on for nodes random ppi network Calculate p-values for nodes for shuffled phenotype label fplayers Final implementers
	Parameters Parameters 2.11.2.7 create_random_network()
	all 2.11.2.4 calculate_p_values_phenotype_label_permutation() Network (p. 14) all Network (p. 14)
	nutils nutils Network masterPATH.RandomManager.create_random_network ( NetworkManager (p. 16) object NetworkManager (p. 16) object
	putils outils putils void masterPATH.RandomManager.calculate_p_values_phenotype_label_permutation ( PathwayManager (p. 24) object CentralityManager (p. 3) object Network all, PathwayManager (p. 24) object outils NetworkManager nutils, CentralityManager (p. 3) object Network all, PathwayManager putils,
	dbutils dbutils		DBManager (p. 9) object NetworkManager nutils, DBManager (p. 9) object CentralityManager outils,
	PathwayManager putils, random_paths_file File name for pathways from shuffled hit genes DBManager dbutils ) file_name_random File name to store information about pathways built on the random network count Number of shuffling steps hubs_file File with nodes and centrality scores CentralityManager outils, ovrreps_paths_file File with nodes DBManager dbutils, String random_paths_file, hitlists_file File with hit genes Create random network with the same degree distribution
	overreps_file hitlist_length Parameters	File with paths and centrality scores String ovrreps_paths_file, Length of hit list
	hugo_by_id	String hitlists_file, HGNC ids map
	all	int hitlist_length, Network (p. 14)
	nutils	int position_of_overreps_count, NetworkManager (p. 16) object
	putils	Map< String, String[ ]> hugo_by_id ) throws IOException PathwayManager (p. 24) object
	Calculate p-values for paths for shuffled phenotype label outils CentralityManager (p. 3) object
	dbutils DBManager (p. 9) object
	2.11.2.3 calculate_p_values_paths_random_networks() Parameters 2.11.2.6 calculate_p_values_phenotype_label_permutation_ppi()
	all		Network (p. 14)
	void masterPATH.RandomManager.calculate_p_values_paths_random_networks ( nutils NetworkManager (p. 16) object
	putils		Network all, NetworkManager nutils, PathwayManager (p. 24) object
			Generated by Doxygen

void masterPATH.RandomManager.calculate_p_values_phenotype_label_permutation_ppi ( Network all,

2 Member Function Documentation 2.12.2.1 adenocarcinoma_part1()

  CentralityManager outils, DBManager dbutils) throws IOException • void case2_part4 ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils) throws IOException • void case2_random ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils, RandomManager rand) throws IOException • void case2_random2 ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils, RandomManager rand) throws IOException Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils) throws IOException • static void mirna63Sys_part4 ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils) throws IOException • static void case2_part2_2 ( Network all, NetworkManager nutils, PathwayManager putils, Centrality← Manager outils, DBManager dbutils) throws IOException DBManager dbutils ) throws IOException Part 2 of pipeline for miRNA muscle differentiation screen in mixed network DBManager dbutils ) throws IOException Part 3 of pipeline for miRNA muscle differentiation screen in mixed network

	NetworkManager nutils, 2.12.2.9 arp_part1() Parameters Part 1 of pipeline for arp2/3 screen in ppi network Parameters NetworkManager nutils, Parameters NetworkManager nutils, Parameters
	all all all all	PathwayManager putils, Network (p. 14) Network (p. 14) PathwayManager putils, Network (p. 14) PathwayManager putils, Network (p. 14)
		CentralityManager outils, CentralityManager outils, CentralityManager outils,
	2.12.2.2 adenocarcinoma_part1_ppi() DBManager dbutils ) throws IOException nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object void masterPATH.Wrapper.arp_part1 ( Network all, nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object 2.12.2.14 arp_part3() DBManager dbutils, RandomManager rand ) throws IOException nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object DBManager dbutils, nutils NetworkManager (p. 16) object 2.12.2.21 case2_part4() RandomManager rand ) throws IOException putils PathwayManager (p. 24) object
	Part 2 of pipeline for adenocarcinoma screen in ppi network outils CentralityManager (p. 3) object NetworkManager nutils, outils CentralityManager (p. 3) object outils CentralityManager (p. 3) object outils CentralityManager (p. 3) object
	void masterPATH.Wrapper.adenocarcinoma_part1_ppi ( Network all, Parameters dbutils DBManager (p. 9) object PathwayManager putils, Network all, Network all, network CentralityManager outils, dbutils DBManager (p. 9) object void masterPATH.Wrapper.arp_part3 ( Part 'p-values' of pipeline for arp2/3 screen in ppi network dbutils DBManager (p. 9) object void masterPATH.Wrapper.case2_part4 ( Part 'p-values on random networks' of pipeline for transcriptome profiling muscle differentiation screen in mixed dbutils DBManager (p. 9) object
	Static Public Member Functions NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils ) throws IOException Part 1 of pipeline for adenocarcinoma screen in ppi network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object all DBManager dbutils ) throws IOException NetworkManager nutils, Parameters NetworkManager nutils, Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object outils Part 1 of pipeline for arp2/3 screen in mixed network Parameters PathwayManager putils, CentralityManager outils, DBManager dbutils ) throws IOException all PathwayManager putils, Parameters Network (p. 14) nutils CentralityManager outils, all Network (p. 14) NetworkManager (p. 16) object putils PathwayManager (p. 24) object DBManager dbutils ) throws IOException nutils NetworkManager (p. 16) object CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.7 arp_add_connections() Network masterPATH.Wrapper.arp_add_connections ( Network all, NetworkManager nutils, PathwayManager putils, all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.12 arp_part2() void masterPATH.Wrapper.arp_part2 ( Network all, NetworkManager nutils, PathwayManager putils, Part 3 of pipeline for arp2/3 screen in mixed network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.19 case2_part2_2() static void masterPATH.Wrapper.case2_part2_2 ( Network all, NetworkManager nutils, PathwayManager putils, Part 4 of pipeline for transcriptome profiling muscle differentiation screen in mixed network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.26 mirna63Sys_part3() void masterPATH.Wrapper.mirna63Sys_part3 ( Network all, NetworkManager nutils, PathwayManager putils, • static void mirna63Sys_part3_3 ( 2.12.1 Detailed Description outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object DBManager dbutils, DBManager dbutils ) throws IOException dbutils DBManager (p. 9) object 2.12.2.17 case2_part1() DBManager dbutils ) throws IOException [static] dbutils DBManager (p. 9) object 2.12.2.5 adenocarcinoma_random() CentralityManager outils, CentralityManager outils, outils CentralityManager (p. 3) object CentralityManager outils, outils CentralityManager (p. 3) object CentralityManager outils,
	Map< String, String[ ]> hugo, 2.12.2.24 mirna63Sys_part1()
	void masterPATH.Wrapper.adenocarcinoma_random ( String hitlist ) throws IOException Part 2 of pipeline for arp2/3 screen in mixed network Part 2 + filter of pipeline for transcriptome profiling muscle differentiation screen in mixed network
	Wrapper (p. 38) class contains wrappers implementing the whole pipeline Network all, void masterPATH.Wrapper.case2_part1 (
	NetworkManager nutils, Add low confident interactions for LIMCH1 protein 2.12.2.10 arp_part1_LIMCH1() Parameters Network all, Parameters void masterPATH.Wrapper.mirna63Sys_part1 ( Parameters
	Author Parameters all all all	PathwayManager putils, CentralityManager outils, Network (p. 14) NetworkManager nutils, PathwayManager putils, Network (p. 14) Network all, NetworkManager nutils, Network (p. 14)
	Natalia Rubanova 2.12.2.3 adenocarcinoma_part2() DBManager dbutils, RandomManager rand ) throws IOException all Network (p. 14) void masterPATH.Wrapper.arp_part1_LIMCH1 ( Network all, nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object 2.12.2.15 arp_random() CentralityManager outils, DBManager dbutils ) throws IOException nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object PathwayManager putils, nutils NetworkManager (p. 16) object 2.12.2.22 case2_random() CentralityManager outils, putils PathwayManager (p. 24) object
	nutils outils outils outils	NetworkManager (p. 16) object NetworkManager nutils, CentralityManager (p. 3) object CentralityManager (p. 3) object DBManager dbutils ) throws IOException CentralityManager (p. 3) object
	void masterPATH.Wrapper.adenocarcinoma_part2 ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils ) throws IOException Part 'p-values' of pipeline for adenocarcinoma screen in mixed network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object PathwayManager putils, CentralityManager outils, DBManager dbutils ) throws IOException Part 1 of pipeline for arp2/3 screen in mixed network Hit list only LMCH1 gene Parameters dbutils DBManager (p. 9) object void masterPATH.Wrapper.arp_random ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils, RandomManager rand ) throws IOException Part 1 of pipeline for transcriptome profiling muscle differentiation screen in mixed network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object dbutils DBManager (p. 9) object void masterPATH.Wrapper.case2_random ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils, RandomManager rand ) throws IOException Part 1 of pipeline for miRNA muscle differentiation screen in mixed network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object dbutils DBManager (p. 9) object 2.12.void masterPATH.Wrapper.adenocarcinoma_part1 ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils ) throws IOException Part 2 of pipeline for adenocarcinoma screen in mixed network all Network (p. 14) nutils NetworkManager (p. 16) object putils outils CentralityManager (p. 3) object Network all ) throws FileNotFoundException, IOException PathwayManager putils, CentralityManager outils, putils PathwayManager (p. 24) object CentralityManager outils, putils PathwayManager (p. 24) object PathwayManager putils, PathwayManager (p. 24) object 2.12.2.8 arp_filter() void masterPATH.Wrapper.arp_filter ( putils outils NetworkManager nutils, nutils NetworkManager (p. 16) object NetworkManager nutils, nutils NetworkManager (p. 16) object dbutils DBManager (p. 9) object Network all, all Network (p. 14) Network all, all Network (p. 14) CentralityManager (p. 3) object void masterPATH.Wrapper.arp_part2_ppi ( Parameters void masterPATH.Wrapper.case2_part3 ( Parameters PathwayManager (p. 24) object dbutils DBManager (p. 9) object Parameters outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object all Network (p. 14) nutils NetworkManager (p. 16) object 2.12.2.13 arp_part2_ppi() Part 'p-values' of pipeline for arp2/3 screen in mixed network outils putils PathwayManager (p. 24) object CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.20 case2_part3() Part 'p-values' of pipeline for transcriptome profiling muscle differentiation screen in mixed network outils CentralityManager (p. 3) object 2
	Part 1 of pipeline for adenocarcinoma screen in mixed network dbutils DBManager (p. 9) object 2.12.2.6 adenocarcinoma_random_ppi() Filter pathways for arp2/3 screen in mixed network DBManager dbutils ) throws IOException outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.18 case2_part2() outils CentralityManager (p. 3) object DBManager dbutils ) throws IOException dbutils DBManager (p. 9) object 2.12.2.25 mirna63Sys_part2()
	CentralityManager outils, DBManager dbutils, Map< String, String[ ]> hugo) throws IOException • void ogg1_part1_ppi ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils) throws IOException void masterPATH.Wrapper.adenocarcinoma_random_ppi ( Parameters Part 2 of pipeline for arp2/3 screen in ppi network Part 3 of pipeline for transcriptome profiling muscle differentiation screen in mixed network Parameters all Network all, NetworkManager nutils, all Network (p. 14) void masterPATH.Wrapper.case2_part2 ( void masterPATH.Wrapper.mirna63Sys_part2 ( 2.12.2.11 arp_part1_ppi() Parameters Network all, Parameters Network all, Network (p. 14) nutils NetworkManager (p. 16) object PathwayManager putils, CentralityManager outils, void masterPATH.Wrapper.arp_part1_ppi ( all Network (p. 14) NetworkManager nutils, PathwayManager putils, all NetworkManager nutils, Network (p. 14) PathwayManager putils,
	putils outils 2.12.2.4 adenocarcinoma_part2_ppi() PathwayManager (p. 24) object CentralityManager (p. 3) object DBManager dbutils, RandomManager rand ) throws IOException Network all, NetworkManager nutils, nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object 2.12.2.16 arp_random_ppi() CentralityManager outils, DBManager dbutils ) throws IOException nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object 2.12.2.23 case2_random2() CentralityManager outils,
	dbutils DBManager (p. 9) object PathwayManager putils, outils CentralityManager (p. 3) object outils CentralityManager (p. 3) object
	void masterPATH.Wrapper.adenocarcinoma_part2_ppi ( Network all, CentralityManager outils, DBManager dbutils ) throws IOException dbutils DBManager (p. 9) object void masterPATH.Wrapper.arp_random_ppi ( Network all, void masterPATH.Wrapper.case2_random2 ( dbutils DBManager (p. 9) object Network all,
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• Network ogg1_add_connections ( Network all, NetworkManager nutils, PathwayManager putils, • void ogg1_part2_ppi ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils) throws IOException • void ogg1_random_ppi ( Network all, NetworkManager nutils, PathwayManager putils, Centrality← Manager outils, DBManager dbutils, RandomManager rand) throws IOException • void ogg1_part4_ppi ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils) throws IOException • void case2_part3 ( Network all, NetworkManager nutils, PathwayManager putils, Part 'p-values' of pipeline for adenocarcinoma screen in ppi network Part 2 of pipeline for transcriptome profiling muscle differentiation screen in mixed network

.12.2.27 mirna63Sys_part3_3()

  Part 4 of pipeline for miRNA muscle differentiation screen in mixed network

	Parameters	NetworkManager nutils,
	all	PathwayManager putils, Network (p. 14)
		CentralityManager outils,
	DBManager dbutils, NetworkManager (p. 16) object 2.12.2.28 mirna63Sys_part4() nutils Map< String, String[ ]> hugo ) throws IOException putils PathwayManager (p. 24) object
	outils	CentralityManager (p. 3) object
	static void masterPATH.Wrapper.mirna63Sys_part4 ( Add low confident connections for OGG1 protein dbutils DBManager (p. 9) object
		Network all,
	Parameters	NetworkManager nutils,
	all	PathwayManager putils, CentralityManager outils, Network (p. 14)
	nutils	DBManager dbutils ) throws IOException [static] NetworkManager (p. 16) object
	putils	PathwayManager (p. 24) object
	hugo Parameters outils dbutils DBManager (p. 9) object HGNC ids map CentralityManager (p. 3) object 2.12.2.33 ogg1_part2()
	all void masterPATH.Wrapper.ogg1_part2 ( Network (p. 14)
	nutils	NetworkManager (p. 16) object Network all,
	putils	NetworkManager nutils, PathwayManager (p. 24) object PathwayManager putils,
	outils	CentralityManager (p. 3) object CentralityManager outils,
	dbutils DBManager (p. 9) object DBManager dbutils ) throws IOException
	2.12.2.31 ogg1_part1() Part 2 of pipeline for DNA repair screen in mixed network
	void masterPATH.Wrapper.ogg1_part1 ( Parameters
	all	Network all, Network (p. 14)
	NetworkManager nutils, PathwayManager putils, NetworkManager (p. 16) object 2.12.2.29 mirna63Sys_random() nutils CentralityManager outils, putils PathwayManager (p. 24) object
	outils	DBManager dbutils ) throws IOException CentralityManager (p. 3) object
	void masterPATH.Wrapper.mirna63Sys_random ( dbutils DBManager (p. 9) object
	Network all, Part 1 of pipeline for DNA repair screen in mixed network
		NetworkManager nutils,
	Parameters	PathwayManager putils,
		CentralityManager outils,
	all	DBManager dbutils, Network (p. 14)
	nutils	RandomManager rand ) throws IOException NetworkManager (p. 16) object
	Part 'p-values' of pipeline for miRNA muscle differentiation screen in mixed network putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object 2
	static void masterPATH.Wrapper.mirna63Sys_part3_3 ( Parameters dbutils DBManager (p. 9) object
	all	Network all, Network (p. 14)
	nutils	NetworkManager nutils, NetworkManager (p. 16) object
	putils	PathwayManager putils, CentralityManager outils, PathwayManager (p. 24) object
	outils	DBManager dbutils ) throws IOException [static] CentralityManager (p. 3) object
	dbutils DBManager (p. 9) object 2.12.2.32 ogg1_part1_ppi()
	Part 3 + filter of pipeline for miRNA muscle differentiation screen in mixed network
	void masterPATH.Wrapper.ogg1_part1_ppi (
	Parameters	Network all,
	all	Network (p. 14) NetworkManager nutils,
	nutils	NetworkManager (p. 16) object PathwayManager putils,
	putils 2.12.2.30 ogg1_add_connections() PathwayManager (p. 24) object
	outils	CentralityManager (p. 3) object
	Network masterPATH.Wrapper.ogg1_add_connections ( dbutils DBManager (p. 9) object Network all,
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CentralityManager outils, DBManager dbutils ) throws IOException

Part 1 of pipeline for DNA repair screen in ppi network

.12.2.34 ogg1_part2_ppi()

  DBManager dbutils ) throws IOException Part 4 of pipeline for DNA repair screen in ppi network RandomManager rand ) throws IOException Part 'random network' of pipeline for DNA repair screen in ppi network RandomManager rand ) throws IOException Part 'pvalues' of pipeline for DNA repair screen in ppi network

	NetworkManager nutils, masterPATH::PathwayManager, 26 Parameters DBManager, 10	masterPATH::NetworkManager, 19 find_hitgenes_on_paths, 30
	PathwayManager putils, CentralityManager outils, compare_two_paths_files all loadHIPPIE, 10 Network (p. 14) nutils masterPATH::PathwayManager, 26 loadHPRD, 10 NetworkManager (p. 16) object create_cyto_for_paths loadHUGO, 10 2.12.2.35 ogg1_part3() putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object masterPATH::PathwayManager, 27 loadKEGG, 11 Index create_cyto_for_pathways loadSignalink, 11	load_screening_data find_miRNAs_on_paths, 30 masterPATH::CentralityManager, 8 find_miRNAs_on_pathways, 31 loadHIPPIE find_the_shortest_paths, 31 masterPATH::DBManager, 10 find_the_strongest_pathways, 32 loadHPRD masterPATH::RandomManager
	void masterPATH.Wrapper.ogg1_part3 ( Network all, Parameters masterPATH::PathwayManager, 27 loadSignor, 11 dbutils DBManager (p. 9) object create_random_network loadTransmir, 11	masterPATH::DBManager, 10 calculate_p_values_nodes_random_networks, 33 loadHUGO calculate_p_values_nodes_random_networks_ppi,
	NetworkManager nutils, PathwayManager putils, CentralityManager outils, masterPATH::RandomManager, 37 loadmirTarBase, 11 Network (p. 14) nutils all add_aggregated_pvalues_to_pathways loadtFacts, 11 NetworkManager (p. 16) object masterPATH::PathwayManager, 25 DBManager masterPATH::Interaction	masterPATH::DBManager, 10 34 masterPATH::NetworkTopology, 20 loadKEGG calculate_p_values_paths_random_networks, 34 calculate_average_clustering_coefficient_ppi masterPATH::DBManager, 11 calculate_p_values_phenotype_label_permutation,
	DBManager dbutils ) throws IOException PathwayManager (p. 24) object add_connectivity_to_pathways putils masterPATH::DBManager, 10 loadNetwork2 masterPATH::NetworkTopology, 20 Interaction, 12 35
	void masterPATH.Wrapper.ogg1_part2_ppi ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils ) throws IOException Part 2 of pipeline for DNA repair screen in ppi network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object Part 3 of pipeline for DNA repair screen in mixed network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.36 ogg1_part4() void masterPATH.Wrapper.ogg1_part4 ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils ) throws IOException Part 4 of pipeline for DNA repair screen in mixed network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object build_map_of_neighbors Interaction filterPathways, 28 2.12.2.37 ogg1_part4_ppi() outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.38 ogg1_random() void masterPATH.Wrapper.ogg1_random ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils, RandomManager rand ) throws IOException masterPATH::Wrapper, 50 return_permutation_distribution, 8 Part 'p-values' of pipeline for DNA repair screen in mixed network Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object 2.12.2.39 ogg1_random2_ppi() void masterPATH.Wrapper.ogg1_random2_ppi ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils, 2.12.2.40 ogg1_random_ppi() void masterPATH.Wrapper.ogg1_random_ppi ( Network all, NetworkManager nutils, PathwayManager putils, CentralityManager outils, DBManager dbutils, Parameters all Network (p. 14) nutils NetworkManager (p. 16) object putils PathwayManager (p. 24) object outils CentralityManager (p. 3) object dbutils DBManager (p. 9) object The documentation for this class was generated from the following file: • Wrapper.java masterPATH::PathwayManager, 25 add_experimental_scores_and_pvalues_to_nodes masterPATH::CentralityManager, 4 add_experimnetal_scores_and_aggregated_pvalues← _to_paths masterPATH::CentralityManager, 4 add_hgnc_symbols_to_paths masterPATH::CentralityManager, 5 add_missing_genes_and_products masterPATH::NetworkManager, 16 adenocarcinoma_part1 masterPATH::Wrapper, 40 adenocarcinoma_part1_ppi masterPATH::Wrapper, 41 adenocarcinoma_part2 masterPATH::Wrapper, 41 adenocarcinoma_part2_ppi masterPATH::Wrapper, 41 adenocarcinoma_random masterPATH::Wrapper, 42 adenocarcinoma_random_ppi masterPATH::Wrapper, 42 arp_add_connections masterPATH::Wrapper, 43 arp_filter masterPATH::Wrapper, 43 arp_part1 masterPATH::Wrapper, 43 arp_part1_LIMCH1 masterPATH::Wrapper, 44 arp_part1_ppi masterPATH::Wrapper, 44 arp_part2 masterPATH::Wrapper, 46 arp_part2_ppi arp_part3 masterPATH::Wrapper, 47 load_screening_data, 8 filter_pathways_by_connectivity, 27 case2_random2 masterPATH::Wrapper, 47 masterPATH::NetworkTopology, 22 find_unique_paths_for_list_of_pathways_ppi, 8 create_cyto_for_pathways, 27 masterPATH::Wrapper, 50 arp_random_ppi case2_random get_subnetwork_statistics find_unique_paths_for_list_of_pathways, 7 create_cyto_for_paths, 27 masterPATH::Wrapper, 47 masterPATH::Wrapper, 50 masterPATH::NetworkTopology, 22 filter_paths, 7 compare_two_paths_files, 26 arp_random case2_part3 get_degree_distribution_ppi compare_two_lists_of_nodes, 7 compare_two_nodes_files, 26 case2_part4 masterPATH::NetworkTopology, 22 calculate_paths_degree, 6 add_connectivity_to_pathways, 25 masterPATH::Wrapper, 49 get_degree_distribution_direct calculate_centrality_scores_for_paths_ppi, 6 add_aggregated_pvalues_to_pathways, 25 masterPATH::Wrapper, 46 calculate_centrality_scores_for_nodes masterPATH::CentralityManager, 5 calculate_centrality_scores_for_paths masterPATH::CentralityManager, 5 calculate_centrality_scores_for_paths_ppi masterPATH::CentralityManager, 6 calculate_diameter_direct masterPATH::NetworkTopology, 20 calculate_diameter_ppi masterPATH::NetworkTopology, 21 calculate_number_of_connected_components_direct masterPATH::NetworkTopology, 21 calculate_number_of_connected_components_ppi masterPATH::NetworkTopology, 21 calculate_p_values_nodes_random_networks masterPATH::RandomManager, 33 calculate_p_values_nodes_random_networks_ppi masterPATH::RandomManager, 34 calculate_p_values_paths_random_networks masterPATH::RandomManager, 34 calculate_p_values_phenotype_label_permutation masterPATH::RandomManager, 35 calculate_p_values_phenotype_label_permutation_← nodes masterPATH::RandomManager, 36 calculate_p_values_phenotype_label_permutation_ppi masterPATH::RandomManager, 36 calculate_paths_degree masterPATH::CentralityManager, 6 case2_part1 masterPATH::Wrapper, 48 case2_part2 masterPATH::Wrapper, 48 case2_part2_2 masterPATH::Wrapper, 49 masterPATH::Network, 14 calculate_p_values_phenotype_label_permutation← print, 13 filter_paths loadNetworkfromfile _nodes, 36 masterPATH::MasterPATH masterPATH::CentralityManager, 7 masterPATH::Network, 15 calculate_p_values_phenotype_label_permutation← main, 13 filter_pathways_by_connectivity loadSignalink _ppi, 36 masterPATH::Network masterPATH::PathwayManager, 27 masterPATH::DBManager, 11 create_random_network, 37 loadNetwork2, 14 filterPathways loadSignor loadNetworkfromfile, 15 permute_phenotype_label, 37 masterPATH::PathwayManager, 28 masterPATH::PathwayManager, 30 masterPATH::PathwayManager, 30 find_miRNAs_on_paths masterPATH::PathwayManager, 30 find_miRNAs_on_pathways masterPATH::PathwayManager, 31 find_pathway_BF_algorithm masterPATH::NetworkManager, 17 calculate_centrality_scores_for_paths, 5 masterPATH::PathwayManager masterPATH::CentralityManager, 8 calculate_centrality_scores_for_nodes, 5 print, 24 find_unique_paths_for_list_of_pathways_ppi add_hgnc_symbols_to_paths, 5 Node, 23 masterPATH::CentralityManager, 7 pvalues_to_paths, 4 masterPATH::Node find_unique_paths_for_list_of_pathways add_experimnetal_scores_and_aggregated_← get_subnetwork_statistics, 22 masterPATH::PathwayManager, 32 nodes, 4 get_degree_distribution_ppi, 22 find_the_strongest_pathways add_experimental_scores_and_pvalues_to_← get_degree_distribution_direct, 22 masterPATH::PathwayManager, 31 masterPATH::CentralityManager ppi, 21 find_the_shortest_paths masterPATH.Wrapper, 38 calculate_number_of_connected_components_← masterPATH::NetworkManager, 18 masterPATH.RandomManager, 32 direct, 21 find_pathway_for_list_BF_algorithm_ppi masterPATH.PathwayManager, 24 calculate_number_of_connected_components_← masterPATH::NetworkManager, 18 masterPATH.Node, 23 calculate_diameter_ppi, 21 find_pathway_for_list_BF_algorithm masterPATH.NetworkTopology, 19 calculate_diameter_direct, 20 masterPATH::NetworkManager, 17 masterPATH.NetworkManager, 16 calculate_average_clustering_coefficient_ppi, 20 find_pathway_BF_algorithm_ppi masterPATH.Network, 14 20 masterPATH.MasterPATH, 13 calculate_average_clustering_coefficient_direct, masterPATH.Interaction, 12 masterPATH::NetworkTopology masterPATH.FoldersPaths, 12 merge_list_of_networks, 19 masterPATH.DBManager, 9 load_hitlist_and_finalimpl, 19 masterPATH.CentralityManager, 3 find_pathway_for_list_BF_algorithm_ppi, 18 masterPATH::MasterPATH, 13 find_pathway_for_list_BF_algorithm, 18 main find_pathway_BF_algorithm_ppi, 17 find_hitgenes_on_paths loadtFacts find_pathway_BF_algorithm, 17 masterPATH::DBManager, 11 build_map_of_neighbors, 16 filterPathways_by_length masterPATH::DBManager, 11 add_missing_genes_and_products, 16 masterPATH::PathwayManager, 29 loadmirTarBase masterPATH::NetworkManager filterPathways_by_centrality_score masterPATH::DBManager, 11 saveNetworktofile, 15 masterPATH::PathwayManager, 29 loadTransmir removeAll, 15 filterPathways_2 masterPATH::DBManager, 11 Network, 14 masterPATH::Wrapper
	outils masterPATH::NetworkManager, 16 CentralityManager (p. 3) object masterPATH::Interaction, 12 filterPathways_2, 29	compare_two_lists_of_nodes return_symmetric_path, 9
	filterPathways_by_centrality_score, 29 dbutils DBManager (p. 9) object calculate_average_clustering_coefficient_direct load_hitlist_and_finalimpl filterPathways_by_length, 30	masterPATH::CentralityManager, 7 returnPathType, 9 compare_two_nodes_files masterPATH::DBManager
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void masterPATH.Wrapper.ogg1_part4_ppi ( Network all,

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adenocarcinoma_part1, 40 adenocarcinoma_part1_ppi, 41 adenocarcinoma_part2, 41 adenocarcinoma_part2_ppi, 41 adenocarcinoma_random, 42 adenocarcinoma_random_ppi, 42 arp_add_connections, 43 arp_filter, 43 arp_part1, 43 arp_part1_LIMCH1, 44 arp_part1_ppi, 44 arp_part2, 46 arp_part2_ppi, 46 arp_part3, 47 arp_random, 47 arp_random_ppi, 47 case2_part1, 48 case2_part2, 48 case2_part2_2, 49 case2_part3, 49 case2_part4, 50 case2_random, 50 case2_random2, 50 mirna63Sys_part1, 51 mirna63Sys_part2, 51 mirna63Sys_part3, 52 mirna63Sys_part3_3, 52 mirna63Sys_part4, 53 mirna63Sys_random, 53 ogg1_add_connections, 53 ogg1_part1, 54 ogg1_part1_ppi, 54 ogg1_part2, 55 ogg1_part2_ppi, 55 ogg1_part3, 56 ogg1_part4, 56 ogg1_part4_ppi, 56 ogg1_random, 57 ogg1_random2_ppi, 57

Acknowledgements I would like to thank my supervisors Dr. Annick Harel-Bellan and Dr. Nadya Morozova; my Thesis Advisory Committee members Dr. Anna Campalans and Dr. Andrey Zinoviev; Dr. Guillaume Pinna and Dr. Anna Poleskaya for participating in the analysis of the experimental data and providing valuable comments on the text.

hsa-mir-484 hsa-mir-485-3p hsa-mir-501-3p hsa-mir-512-3p hsa-mir-532-3p hsa-mir-541 hsa-mir-600 hsa-mir-625 hsa-mir-636 hsa-mir-663 hsa-mir-664 hsa-mir-766 hsa-mir-770-5p hsa-mir-93 hsa-let-7b hsa-mir-1224-3p hsa-mir-1228 hsa-mir-1249 hsa-mir-125a-5p hsa-mir-1260 hsa-mir-1280 hsa-mir-129-3p hsa-mir-1296 hsa-mir-133a hsa-mir-133b hsa-mir-150 hsa-mir-197 hsa-mir-204 hsa-mir-328 hsa-mir-342-3p hsa-mir-346 hsa-mir-361-3p hsa-mir-483-3p hsa-mir-486-3p hsa-mir-574-3p hsa-mir-629 hsa-mir-885-5p hsa-mir-193b hsa-mir-369-3p hsa-mir-381 hsa-mir-886-5p hsa-mir-940 hsa-mir-98 hsa-mir-631

Table 2-16. DNA repair loss-of-function screening: top 50 paths with the highest centrality scores.

Centrality score

Hit gene -Hit gene pair pvalue pHIST1H3A-pRBBP7 pSETD1B-pHIST1H3A ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3J-pRBBP7 pSETD1B-pHIST1H3J ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3I-pRBBP7 pSETD1B-pHIST1H3I ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3H-pRBBP7 pSETD1B-pHIST1H3H ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3G-pRBBP7 pSETD1B-pHIST1H3G ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3D-pRBBP7 pSETD1B-pHIST1H3D ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3F-pRBBP7 pSETD1B-pHIST1H3F ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3B-pRBBP7 pSETD1B-pHIST1H3B ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3E-pRBBP7 pSETD1B-pHIST1H3E ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pHIST1H3C-pRBBP7 pSETD1B-pHIST1H3C ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0066 pSETDB1-pHIST1H3E pSETD1B-pHIST1H3E ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3F pSETD1B-pHIST1H3F ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3A pSETD1B-pHIST1H3A ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3I pSETD1B-pHIST1H3I ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3D pSETD1B-pHIST1H3D ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3G pSETD1B-pHIST1H3G ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3C pSETD1B-pHIST1H3C ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3H pSETD1B-pHIST1H3H ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3J pSETD1B-pHIST1H3J ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSETDB1-pHIST1H3B pSETD1B-pHIST1H3B ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0485 pSMC1A-pMCM6 pMCM6-pPSMA1 ;pSMC1A-pPSMA4_pp;pPSMA1-pRAD21_pp;pSMC1 0.0104 pCHAF1A-pHIST1H3D pSETD1B-pHIST1H3D ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3H pSETD1B-pHIST1H3H ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3C pSETD1B-pHIST1H3C ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3E pSETD1B-pHIST1H3E ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3G pSETD1B-pHIST1H3G ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3J pSETD1B-pHIST1H3J ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3A pSETD1B-pHIST1H3A ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3I pSETD1B-pHIST1H3I ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3F pSETD1B-pHIST1H3F ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pCHAF1A-pHIST1H3B pSETD1B-pHIST1H3B ;pSMC3-pSETD1B_pp;pSETD1B-pRNF111_pp;pPSMA 0.0238 pSUMO2-pRAD21 pSUMO2-pPSMA4 ;pSMC1A-pPSMA4_pp;pPSMA1-pRAD21_pp;pRAD2 0.0061 pSUMO2-pSMC3 pSUMO2-pPSMA4 ;pSMC1A-pPSMA4_pp;pSMC3-pPSMA3_pp;pSMC3-0.0067 pHIST1H3A-pRBBP4 pSETD1B-pHIST1H3A ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3J-pRBBP4 pSETD1B-pHIST1H3J ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3H-pRBBP4 pSETD1B-pHIST1H3H ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3D-pRBBP4 pSETD1B-pHIST1H3D ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3C-pRBBP4 pSETD1B-pHIST1H3C ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3G-pRBBP4 pSETD1B-pHIST1H3G ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3E-pRBBP4 pSETD1B-pHIST1H3E ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3B-pRBBP4 pSETD1B-pHIST1H3B ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3I-pRBBP4 pSETD1B-pHIST1H3I ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pHIST1H3F-pRBBP4 pSETD1B-pHIST1H3F ;pDDB1-pSETD1B_pp;pFANCA-pSETD1B_pp;pSMC1 0.0357 pRAD21-pPRKDC pHIST1H3G-pPRKDC pSETD1B-pHIST1H3G ;pSMC3-pSETD1B_pp;pSMC1A-pSETD1B_pp;pRAD2 0.0039 pRAD21-pPRKDC pHIST1H3C-pPRKDC pSETD1B-pHIST1H3C ;pSMC3-pSETD1B_pp;pSMC1A-pSETD1B_pp;pRAD2 0.0039 pRAD21-pPRKDC pHIST1H3B-pPRKDC pSETD1B-pHIST1H3B ;pSMC3-pSETD1B_pp;pSMC1A-pSETD1B_pp;pRAD2 0.0039 pRAD21-pPRKDC pHIST1H3E-pPRKDC pSETD1B-pHIST1H3E ;pSMC3-pSETD1B_pp;pSMC1A-pSETD1B_pp;pRAD2 0.0039 pRAD21-pPRKDC pHIST1H3J-pPRKDC pSETD1B-pHIST1H3J ;pSMC3-pSETD1B_pp;pSMC1A-pSETD1B_pp;pRAD2 0.0039 pRAD21-pPRKDC pHIST1H3H-pPRKDC pSETD1B-pHIST1H3H ;pSMC3-pSETD1B_pp;pSMC1A-pSETD1B_pp;pRAD2 0.0039 pRAD21-pPRKDC pHIST1H3D-pPRKDC pSETD1B-pHIST1H3D ;pSMC3-pSETD1B_pp;pSMC1A-pSETD1B_pp;pRAD2 0.0039

62

pACVR2B-pACVR1B pACVR1B-pTDGF1 pTDGF1-pMSTN 0.004 SNX6-pSNX6 pSNX6-pEGFR pEGFR-pARF6 0.000 VAV3-pVAV3 pVAV3-pEGFR pEGFR-pARF6 0.000 E2F1-pE2F1 pSTAT1-pE2F1 pKIT-pSTAT1 0.000 KPNA3-pKPNA3 pARRB1-pKPNA3 pARF6-pARRB1 0.001 KPNA3-pKPNA3 pARRB2-pKPNA3 pARF6-pARRB2 0.001 ACVR2B-pACVR2B pACVR2B-pACVR1B pACVR1B-pTDGF1 0.004 pACVR2B-pACVR1B pACVR1B-pTDGF1 pTDGF1-pMSTN 0.004 E2F1-pE2F1 pE2F1-pSP1 pSP1-ACTA1 ACTA1-pACTA1 0.000 E2F1-pE2F1 pMDM2-pE2F1 pTCAP-pMDM2 pTCAP-pMSTN 0.000 E2F1-pE2F1 pTBP-pE2F1 pTBP-ACTA1 ACTA1-pACTA1 0.000 TP53-pTP53 pTP53-hsa-mir-125b hsa-mir-125b-IGF2 IGF2-pIGF2 0.000 E2F1-pE2F1 pEP300-pE2F1 pEP300-ACTA1 ACTA1-pACTA1 0.000 E2F1-pE2F1 pE2F1-hsa-let-7a hsa-let-7a-IGF2 IGF2-pIGF2 0.000 NCOA3-pNCOA3 pNCOA3-pAR pAR-ACTA1 ACTA1-pACTA1 0.001 NCOA3-pNCOA3 pEP300-pNCOA3 pEP300-ACTA1 ACTA1-pACTA1 0.001 E2F1-pE2F1 pE2F1-hsa-let-7a hsa-let-7a-IGF2 0.000 pE2F1-hsa-let-7a hsa-let-7a-IGF2 IGF2-pIGF2 0.000 pMDM2-pE2F1 pTCAP-pMDM2 pTCAP-pMSTN 0.000 E2F1-pE2F1 pE2F1-ARF6 ARF6-pARF6 0.000 TP53-pTP53 pTP53-hsa-mir-125b hsa-mir-125b-IGF2 0.000 pTP53-hsa-mir-125b hsa-mir-125b-IGF2 IGF2-pIGF2 0.000 E2F1-pE2F1 pMDM2-pE2F1 pTCAP-pMDM2 0.000 E2F1-pE2F1 pE2F1-pSP1 pSP1-ACTA1 0.000 pTBP-pE2F1 pTBP-ACTA1 ACTA1-pACTA1 0.000 E2F1-pE2F1 pTBP-pE2F1 pTBP-ACTA1 0.000 pE2F1-pSP1 pSP1-ACTA1 ACTA1-pACTA1 0.000 Path 72 protein TP73 (Figure 2-16 A), and, finally, the protein LRIG1 (Figure 2-16 C) that has a strong negative effect on the expression of epidermal growth factor receptor. These pathways represent interesting new directions to follow, in order to further understand the mechanics of skeletal myogenesis. "Final implementers": RB1, CDKN1A.

The analysis was performed in protein-protein network.

Appendix 2 masterPATH JavaDoc

Below is Java Doc for masterPATH method.

masterPATH Generated by Doxygen 1.8.13

Contents Author

Natalia Rubanova

The documentation for this class was generated from the following file:

• FoldersPaths.java

masterPATH.Interaction Class Reference

Public Member Functions