Christophe Malabat

Cosmin Saveanu
email: cosmin.saveanu@pasteur.fr

Identification of links between cellular pathways by Genetic Interaction Mapping (GIM)

Keywords: Genetic screen, Gene deletion, Growth rate, Barcode microarray, Data analysis

Introduction

Tests of the effect on growth rate for a large number of combinations of gene deletions became possible in the last decade through technical advances that arose after the completion of S. cerevisiae genome sequencing. An essential development in this respect has been the creation of a collection of systematic deletion for all the predicted yeast genes (1, 2). Each cell of this collection bears specific short 20 nucleotide long sequences that uniquely identify the affected locus. These "molecular barcodes" allow the analysis of thousands of strains in parallel, to estimate growth of any single strain in a complex mixed population. The second most important advance in the field has been the invention of methods that would allow efficient production of thousands of double mutant strains. Two strategies were employed to this end. One, called SLAM (Synthetic Lethality Analysis with Microarrays), was based on massive transformation of cells from the collection with DNA fragments that ensure the replacement of a gene of interest with a selection cassette (3, 4). The other, which was employed in several laboratories under various names, is based on efficient mass mating (5) of haploid yeast cells, bringing together two mutations of interest. The development of a method for efficient selection of double mutant haploid strains issued through meiosis from these diploids was crucial to the ability to obtain and test a large number of mutation combinations in parallel.

Both Synthetic Genetic Array analysis (SGA, 6, 7) and Genetic Interaction Mapping (GIM, 8) use this approach. Quantitative tests of the extent by which two concomitant mutations affect growth rate in comparison with each single mutation present in a yeast strain, were pioneered in Epistatic Miniarray (E-MAP) studies (9), and used in SGA (10) and GIM (8) screens. The GIM method, presented here, combines the efficient generation of haploid double mutant strains through mating and sporulation with a direct quantitative test of strains growth based on DNA microarrays. Genetic screens of the type described above can be extended to other collections of mutant strains. For example, substituting the normal 3' UTR region of a gene with a long 3' UTR destabilize mRNA through nonsensemediated mRNA decay (NMD, 11). The reduction of the amounts of proteins allowed testing of phenotypes for some of the yeast essential genes (9). Our recent analysis of a collection of such strains indicate, however, that this strategy is biased and cannot guarantee that the obtained strains show a phenotype, since NMD shows preference for mRNAs with short coding sequences [START_REF] Decourty | Long open reading frame transcripts escape nonsense-mediated mRNA decay in yeast[END_REF]. The study of phenotypes for essential gene mutants can also be done with collections of thermosensitive yeast strains (10).

The details provided in the present chapter should be helpful to set up and perform reproducible GIM screens and identify either direct genetic interactions or specific interaction profiles that indicate which cellular pathways are required for the cells to adapt to the absence of a given gene. Our experience with large scale quantitative data indicate that crucial to the identification of patterns or interesting correlation is easy visual data representation. Several software solutions answer this question including Mayday (13), geWorkBench (14), or Perseus (15).

In the end, GIM screen results are only as good as the capacity of the experimenter to deduce useful information from them. In most of the cases, the screens are only the starting point for projects that need years of focused work until completion. Thus, such screens are most of the time a discovery tool that provides first hints for focused analyses in functional genomics.

The preparation of the query strain as well as the relative timing of a GIM screen is indicated in Fig. 1. We perform between 4 and 16 parallel screens in parallel and the limiting step is the selection and culture of the pool of haploid double mutants.

Materials

2.1 Growth media, antibiotics and molecular biology reagents 1. YPD medium: 2% D-glucose, 1% Difco yeast extract, 1% Difco Bactopeptone.

Autoclave at 110°C for 20 minutes.

2. GNA medium: 5% D-glucose, 3% Difco nutrient broth, 1% Difco yeast extract.

Autoclave at 110°C for 20 minutes. Prepare fresh before use.

3. Sporulation medium: 1% potassium acetate (10% stock), 0.005% zinc acetate (0.5% stock), uracile (2 mg/100ml, 0.2% stock), histidine (2 mg/100ml, 0.4% stock) and leucine (6 mg/100 ml, 1.2% stock in dH 2 O). Resuspend the stocks in dH 2 O and filter sterilize. 4. Antibiotics: 0.2 mg/ml Hygromycin B (stock 50 mg/ml); 20 µg/ml Nourseothricin, clonNat (Werner Bioagents, ref 5.1000, 200 mg/ml stock); 0.2 mg/ml G418 sulphate (100 mg/ml stock).

5. Synthetic complete medium without uracile: 6.7 g/L Yeast Nitrogen base without aminoacids, with ammonium sulfate, 2% glucose and 0.2% amino acids mix. The mix of amino acids is composed of alanine, arginine, aspartic acid, asparagine, cysteine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophane, tyrosine and valine, in equal weight proportion, with the exception of leucine, for which twice as much is added. In the same mix, add for 1 g of amino acid 0.5 g of adenine. Autoclave or filter sterilize. For plates 2% agar is used in the final medium composition.

6. Culture plates (96 deep wells, 2 ml), 96 long pins replicator.

7. Gaz permeable and aluminium seal for 96 well plates.

8. NEB4 buffer: 50 mM potassium acetate, 20 mM Tris-acetate, pH 7.9 at 25°C, 10 mM magnesium acetate, 1 mM dithiothreitol. 9. AscI and EcoRI restriction enzymes. pGID3 plasmid. 12. Scanner (GenePix 4000B) and scanning software (GenePix Pro 6, Molecular Devices, LLC). [START_REF] Battke | Mayday -integrative analytics for expression data[END_REF]. System for normalization and data analysis (R from cran.r-project.org/ with the ggplot2 package installed).

Microarray reagents

Methods

Obtention of a pool of MATa mutant cells from the collection

This is probably one of the most time consuming steps when initiating the first series of GIM screens in a laboratory. It requires manipulation of 74 plates with 96 wells under sterile conditions (see Note 2). The pools of mutants can be obtained in large amounts and aliquoted, to be used by several laboratories, as a common resource.

1. Thaw several plates (usually no more than 12, for ease of manipulation) from the collection on the bench until the medium is half-molten. Carefully pick cells from the plate by using a 96 pin replicator. Pay attention that no droplets of medium are transported from one well to another. Transfer the cells to two different 96 well plates that are already filled with YPD medium. A standard 96 well plate will serve as a duplicate of the original collection, while the second one, with deep wells that contain up to 2 ml of YPD, will constitute the culture of individual strains to constitute the pool.

2. Add a fresh aluminium sticking cover on the original plate and return it to -80°C (see Note 3).

3. Leave the cells in the new plates to grow for 48-72 hours at 30°C, under a gaz permeable seal. No continuous agitation is required.

4. Verify that the cells have grown in all the wells that contained cells in the original plate and are recorded as such in the spreadsheet that accompanies the collection. Transfer the cells to a single flask by using a multichannel pipetting device. Vortex briefly, centrifuge at 3200 g for 10 minutes at room temperature. Resuspend the cells in YPD containing 25% (w/v) glycerol and take an OD 600nm reading. Store the pool of cells in aliquots of different sizes that will be directly used in the GIM screens.

3.2 Generation of a "query" mutant strain for the screen 1. Thaw one or several haploid BY4742 strains from the collection that carry the G418 resistance cassette as a replacement of the coding sequence for the genes of interest. Carry out the maximum number of validations required to be sure the strain lacks the gene of interest and shows no additional mutations (see Note 4).

2. Prepare enough pGID3 plasmid to be able to transform as many strains as you wish (1 µg digested plasmid/strain). For a single strain, place 1 μg pGID3 plasmid in 10 µl of NEB4 buffer containing 5 U of AscI and 5 U of EcoRI.

Digest for 1 hour at 37°C. Test digestion efficiency by electrophoresis on 1 % agarose gel. Two bands corresponding to the digestion fragments (2066 nt and 2484 nt) should be obvious.

3. Prepare for each strain 10 ml of culture in YPD medium, leave to grow overnight. In the next morning, the culture should be saturated (OD 600nm between 10 and 15). Dilute culture in 50 ml fresh YPD to an OD 600nm of 0.15.

Proceed with a DMSO enhanced yeast transformation protocol (16) with 1 µg of digested pGID3. Spread transformed cells on YPD plates containing nurseothricin to select for the recombination event. Typical yields are 50 to 500 colonies, visible after 2 days of plate incubation at 30°C. Streak single colony cells on YPD plates containing G418 and on YPD plates containing nurseothricin. Store cells that have lost G418 resistance and became resistant to nurseothricin at -80°C.

Obtention of a pool of double mutant strains

1. Transform previously obtained strains, that bear the pMFα2-Nat R cassette as a replacement for the coding sequence of the gene of interest, with 0.3 µg pGID1 plasmid, containing two different markers: URA3 and Hygromycin resistance (see NOTE 5). Select transformants on SC-URA plates (optionally, these strains can be also stored at -80°C, to speed up the time until a screen starts).

2. The evening before starting the screen, prepare a pre-culture of the query strain in 10 ml YPD medium containing hygromycin. The next morning, dilute the cells to an OD 600nm of 0.2 in 25 ml GNA + hygromycin medium and leave to grow until the OD 600nm reaches 0.8. Thaw an aliquot of the pool of mutants in 25 ml GNA medium and incubate for 30 minutes at 30°C with agitation; the expected OD 600nm is 0.4 (see Note 6).

3. Combine the two cultures in a 50 ml tube, centrifuge 10 min. at 3200 g at room temperature, resuspend the cells in 0.5 ml GNA medium and spread the suspension on a 90 mm diameter GNA plate. Leave at 30°C for 5 hours to allow mating. [START_REF] Pan | A robust toolkit for functional profiling of the yeast genome[END_REF]. Recover (scrape) the cells from the plate in 1 ml GNA medium containing hygromycin and G418 and resuspend them in 100 ml of the same medium.

Incubate for 18 hours, usually overnight, at 30°C with agitation.

5. Take an OD 600nm reading for the overnight culture (dilute 1/50 in GNA medium) and recover an amount of culture that, when diluted in 150 ml would yield an OD 600nm of 0.8 (typically 6-10 ml). Centrifuge at room temperature in a 50 ml Falcon tube for 10 minutes at 3 200 g. 8. Recover the cells from 100 ml of sporulation flask by centrifugation at 3200 g, 10 minutes, RT. Resuspend the pellet in 1 l YPD in a large 2 liter culture flask and incubate with agitation at 30°C for 5.5 hours. 9. Add antibiotics (G418 and nourseothricin) to the YPD medium and the cells and leave with agitation at 30°C. [START_REF] Costanzo | The genetic landscape of a cell[END_REF]. Take repeated readings of OD 600nm until the values reach a value of 2. Dilute the cells in 1 l of fresh YPD medium with antibiotics (G418 and nurseothricin) to a DO 600nm of 0.1. Estimate the number of generations from the slope of the growth curve of the newly diluted culture to know at which moment the average number of generations is 18 (in general 45-60 hours, see Note 8).

11. Recover the equivalent of 10 OD 600nm culture for each screen and the corresponding reference pool of cells. Pellet the cells by centrifugation at 3200 g, 10 min, RT, and resuspend in 1 ml ice cold H 2 0, then split in two aliquots in 1.5 ml Eppendorf tubes. Pellet the cells by 20 seconds of 13 000 g centrifugation at 4°C, remove supernatant and freeze the cells at -80°C until further processing.

Growth speed estimation from barcode microarray data

1. Resuspend the cell pellet recovered in the previous step in 0.2 ml extraction buffer and add approximately 0.2 ml acid-washed glass beads (0.4-0.6 mm diameter, Sigma) and 0.2 ml phenol-chloroform-isoamyl alcohol mix (25:24:1, pH 8). Vortex vigorously for 7 minutes under a chemical hood.

2. Add 0.2 ml TE buffer and vortex for 5 seconds. Centrifuge at 13 000 g for 10 minutes, at RT. Transfer the supernatant in a 1.5 ml Eppendorf tube that already contains 1 ml pure ethanol. Mix by turning the tube upside down several times.

3. Centrifuge at 13 000 g, at RT, for 5 minutes. Wash the white small pellet that sticks to the bottom of the tube with 0.5 ml of 70% ethanol. Centrifuge at 13 000 g for 5 minutes at RT. Throw the supernatant, leave the pellet at room temperature to dry out, between 5 and 10 minutes and resuspend the dry pellet in 30 μl of TE (optionally add 10 ng/ml RNase A). Carefully layer the microarray slide over the sample, oligonucleotide side down (can be detected by breathing on the sides of the slide) and close the hybridizaton chamber. Pay attention that a single air bubble is formed in the chamber. Place the chamber on a rotating wheel (see Note 12) in a 24 °C incubator for at least 12 hours. 10. Prepare several dilutions of SSPE buffer (6x, 2x, 0.2x) containing 0.05% Triton X100. Pre-heat 50 ml SSPE 6X, 0.05% Triton X100 at 30°C. Open the chamber and transfer the two slides together in a Falcon tube with the wash 6X SSPE buffer. Remove the slide with the gaskets and transfer the microarray slide to a second bath of 6x SSPE, 0.05% Triton X100. Leave for 5 minutes with occasional agitation, then move the slide to the 2X SSPE solution, 0.2X SSPE solution and finally to a 0.2X SSPE solution containing no Triton X100. Incubate at each step for 5 minutes (RT).

11. After the last wash, slowly remove the slide from the liquid, dry its lower edge on a piece of Whatman paper, and plunge in stabilization solution for 10 seconds (under a chemical hood). Slowly remove the slide from the liquid so that no droplets remain; droplets can also be removed by a flux of dry air.

12. Scan the slide on a GenePix 4000B or equivalent scanner that is capable of 5 µm resolution. Analyze the scan images using an associated file that describes the position and annotation of each spot (gal file for GenePix Pro 6 or 7). The results are exported in the gpr format, a text file that contains columns separated by the tab character.

Microarray data analysis

1. We provide here an example of R session commands used to normalize data post-acquisition and to be able to do diagnostic plots. The input text file is the gpr result of image analysis with GenePix Pro.

2. Define a function allowing to extract the ORF names from the name of the probes (to be changed depending on how probes are identified). An example of identifier is YDR439W-U, where U shows that the barcode is the one located in the upstream position of the KanMX4 cassette. The "namesextract" function uses regular expressions to identify and grab ORF information from the identifier. 10. Find the row numbers for cases when signal higher than background: indexU <-intersect(goodx, which(to_norm$UD == "U")) indexD <-intersect(goodx, which(to_norm$UD == "D"))

11. Simple normalization, by median substraction, will not work well in this case, since the dependence between signal intensity and ratio changes is not linear. Lowess, or loess is a more versatile method. First, define the function, based on the internal "loess" function: 18. The various computations can be better organized in a series of functions to be sequentially applied to the data. Interactive R sessions are best done with RStudio (www.rstudio.com/). For further data processing or analysis, the results can be exported to a comma separated text file: write.csv(aggregated_ORFgp, "aggregated_ORFgp.csv", row.names = F)

Visualization of series of GIM results

1. To be able to visualize the data from several GIM screens at the same time, build a tabulated file that has a format accepted by Cluster 3 (http://bonsai.hgc.jp/~mdehoon/software/cluster/). It includes a first column with unique identifiers for the genes or mutations followed by an optional, column with annotations (gene name, SGD description line) labelled "NAME" and columns corresponding to normalized numerical values (usually log 2 transformed) for different GIM screens. Cluster 3 has a graphical user interface that allows to keep only rows for which values were obtained in at least 70% of the experiments. Proceed with hierarchical clustering and test several methods. We obtained good functionally relevant grouping of data with 'Correlation (uncentered)' as the similarity metric and with 'Average linkage' as clustering technique.

2. Open the clustering result (.cdt file) with Java TreeView (http://jtreeview.sourceforge.net/) and use either the tabular color-coded view of the cluster or scatterplots (Analysis>Scatterplot) to identify interesting correlations between genetic interaction profiles or similarity in response of the tested mutants as measured in several different GIM screens.

3. More involved data analysis depends on the integration of data from several different sources (SGA, GIM, protein-protein interactions) and gene ontology (GO) enrichment analysis in groups of related genes (by using, for example, the generic GO term finder, http://go.princeton.edu/cgi-bin/GOTermFinder).

Notes

1. Agilent microarrays for barcodes, unlike those used for transcriptome studies, can be stripped and reused, since hybridization is done on a relatively short, 20 nt, region (instead of 60 nt, as usual for expression microarrays). If slides have been previously treated with the Agilent stabilization and drying solution, they should be kept for 4-5 hours in a Tris HCl 10mM, EDTA 1 mM, pH 8 solution at 45°C to remove the protective coating. Stripping is performed by incubating the slides twice for 5 minutes in a solution of 1% SDS, 5 mM Tris HCl pH 8, 0.5 mM EDTA that was brought to 95°C. The stripped slides should be re-scanned to verify that no signal is present after the procedure.

2. The collection of yeast mutants has been distributed to many laboratories in the yeast community under the form of 96 well plates. Single strains should be checked thoroughly for the absence of the deleted gene and for the presence of the KanMX4 cassette at the appropriate locus. Crosscontamination is not a problem when working with pools of mutants, where we can follow a deleted locus via the molecular barcodes, with the exception of slow growing mutants that could be "invaded" readily by faster growing contaminating strains.

3. Thawing plates leads to viability loss; however since there is a large excess of viable cells in a typical well of the collection plates, re-freezing plates allows an economy of plates and manipulation time to be made.

Many viable yeast deletion strains have been previously shown to accumulate

secondary mutations that alleviate the growth defect of the original strain (17). The extent of this phenomenon is remarkable, as it was estimated that more than half of the strains from the collection are heterogeneous mixtures of cells bearing different genome variants as a result of the original gene deletion (18).

5.

The presence of two markers on the plasmid used for diploid selection eases selection of transformed yeast cells on SC-URA plates, while culture and selection of diploids is done in rich medium with hygromycine.

6. For a single screen we use an estimated number of 3*10 8 mutant cells. Since only a fraction of the cells stored at -80°C is viable and the mating efficiency is variable, we estimate the number of diploids obtained to be around 5*10 7 , which corresponds to around 10,000 heterozygous double mutants per locus, assuming an uniform distribution for the number of cells for each mutant in the pool of cells. These relatively large numbers tend to minimize betweenscreen variation. Another step in which the number of cells is critical is sporulation, since this step is particularly inefficient in cells derived from S288c strain (19).

7. If some of the very rich GNA medium gets carried in the sporulation medium, an important decrease in the sporulation efficiency can be observed.

Thorough wash of diploid cells is thus mandatory for the efficiency of this step.

8. Growth rate estimates are useful to ensure that the query screen pool of double mutants and the reference pool of double mutants grow for a comparable number of generations. If, for example, the doubling time of a pool of mutants is 2.5 hours, cells should be recovered after 45 hours from the time the antibiotics have been added to the culture. Such timing can be difficult to follow, and an automatic system for cell culture can be set up in such a way that several screens are performed in parallel. The system involves the use of a turbidostat, for which readings of absorbance (proportional with the turbidity and the total number of cells) are done periodically, and defined amounts of fresh media are automatically added to ensure continuous dilution of the culture. 9. We use two separate PCR reactions for barcode amplification and labeling to avoid the massive expansion of the barcode regions from the query strain deletion that is present in all the cells of the population. The KU and KD oligonucleotides are complementary to regions of the KanMX4 cassette that have no equivalent in the MFα2-NAT cassette. While we used to gel purify the products of the first PCR reaction, a simple dilution before the second PCR reaction is sufficient to yield excellent hybridization results. It is important, especially for the second PCR reaction, to set up control PCR reactions, without template DNA. Contamination with PCR products from previous screens is a common occurrence. [START_REF] Costanzo | The genetic landscape of a cell[END_REF]. In general we use the deletion of YEL068C as a reference strain. We observed no specific effects of this deletion on cell growth in previous GIM screens or under various stress conditions (20). We have also used successfully mixes of genomic DNA obtained from 16 different GIM screens as a base for comparison with a given gene deletion screen.

11. Oligonucleotides used at this step in great excess hybridize with the universal sequences found on both sides of the barcode region, so that only that region remains available for hybridization with the complementary regions on the barcode microarray.

12. In the absence of a specific rotating system for Agilent hybridization chambers, we adapted a rotating wheel by carving slots of the right size in a poly-styrene foam pad. The hybridization chambers are placed and secured with elastic bands and the rotating wheel is left in a 24°C incubator overnight. The characteristic exclusion peak is marked with a blue vertical line and corresponds to the position of the query gene locus.

1 . 3 .

 13 Oligonucleotides: U1 5'-GAT GTC CAC GAG GTC TCT; KU 5'-AAG AAG AAC CTC AGT GGC; D1 5'-CGG TGT CGG TCT CGT AG; KD 5'-GGA TCT TGC CAT CCT ATG; U2block 5'-CGT ACG CTG CAG GTC GAC; D2block 5'-ATC GAT GAA TTC GAG CTC; U2-Cy3/5 5'-Cy3/5-GTC GAC CTG CAG CGT ACG; D2-Cy3/5 5'-Cy3/5-CGA GCT CGA ATT CAT CGA T; 2. Custom Agilent microarray (8x15k version for barcodes, Agilent-026035 Scer_barcode_v2_200911, platform GPL18088 at the GEO database, www.ncbi.nlm.nih.gov/geo/) (see Note 1). Extraction buffer: 2% Triton X100, 1% SDS, 0.1M NaCl, 10 mM Tris-Hcl pH 8, 1 mM EDTA.

4 .

 4 TE buffer: 10 mM TrisHCl pH8, 1 mM EDTA.

5 .

 5 Acid washed glass beads of 0.4-0.6 mm diameter.6. Phenol-chloroform-isoamyl alcohol mix (25:24:1, pH 8).

7 .

 7 Linear acrylamide, 5 mg/ml. 8. DIG Easy Hyb solution (Roche Applied Science 1.603.558). 9. 20X SSPE buffer: 3 M NaCl, 20 mM EDTA, 0.2M NaH 2 PO 4 , pH 7.4. 10. Agilent hybridization chamber and gasket slides for 8x15k microarrays. 11. Agilent stabilization and drying solution, required to protect Cy5 fluorescence from fading under the action of ambient O 3 .

6 .

 6 Thoroughly resuspend the pellet in 25 ml sporulation medium. Centrifuge a second time and resuspend the cells in another 25 ml sporulation medium (see Note 7). Resuspend the cells in 150 ml sporulation medium in a 1 liter culture flask. 7. Leave the cells at 25°C for 5 days with continuous agitation and shift the cultures to 30°C for an additional 3 days. At the end of this sporulation period check by light microscopy that specific tetrads are a sizable proportion of the cells.

4 . 5 .

 45 Use 1 μl of the previously prepared DNA extract as template in each of two PCR reactions that are set up using oligonucleotides U1-KU (uptag PCR 1, UPCR1) and D1-KD (downtag PCR 1, DPCR1) (see Fig. 1a and Note 9). Set up standard 50 µl reactions for each PCR reaction (25 cycles, 94°C denaturation 30 sec., 50°C annealing 30 sec., 72°C elongation 30 sec). Verify the size and abundance of PCR products by electrophoresis on a 1% agarose gel. Prepare four different PCR reactions for each comparison between a screen using a query mutation and a screen performed under identical conditions with a reference mutation (see Note 10). Set up standard 50 µl PCR reaction with 0.5 µl UPCR1 or DPCR1 in the following combinations with oligonucleotides: UPCR1 query with oligonucleotides U1 and U2-Cy3, UPCR1 reference with oligonucleotides U1 and U2-Cy5, DPCR1 query with oligonucleotides D1 and D2-Cy3, DPCR1 reference with oligonucleotides D1 and D2-Cy5. Use 15 cycles of amplification (94°C 15 sec, 55°C 15 sec, 72°C 15 sec). Test the reactions by electrophoresis using a 3% agarose gel and a loading buffer that only contains xylene cyanol. The expected size of PCR products is 60 nt. 6. Mix the four PCR products obtained in the previous step. Add a pre-mix containing 5µl each of oligonucleotides U1, D1, U2block and D2block (100 µM each, see Note 11) and 2 µl linear poly-acrylamide (5 mg/ml), as carrier. Add 22.5 µl sodium acetate (3M, pH=5.2), mix well and precipitate with 550 µl pure ethanol. Leave at -20°C for at least one hour. 7. Centrifuge 30 minutes at 15 000 g, 4°C. Pay attention to the purple pellet when removing the supernatant. From now on, avoid direct exposure of the pellet or solution to light. Wash the pellet once with 70% ethanol. Recentrifuge 5 minutes at 15 000 g, 4°C. Remove the ethanol and leave the tube on the bench for 5 minutes to dry. 8. Add 50 µl DIG Easy Hyb buffer to the pellet. Mix by repeated pipetting. Dilute 20 µl of the sample with 100 µl DIG Easy Hyb and keep the rest at -20°C, for the case when the hybridization needs to be repeated. Heat for 2 minutes at 95°C and then switch to ice for 5 minutes. Keep the sample at room temperature until hybridization. 9. Add the cover slide, with the gaskets, to the lower part of the hybridization chamber. Pipet carefully 47 µl of sample in the middle of a gasket square.

} 3 .

 3 namesextract <-function(IDs) { orfnms1 <-gsub("(Y......-[ABC]?).*", "\\1", IDs) orfnms2 <-gsub("-$", "", orfnms1) return(orfnms2) Read the tab delimited file in a data frame, choose columns and create a column with the ORF name extracted from the barcode identifiers. Replace the file name with your actual gpr file name. A setwd() command indicating the "working directory" is useful at the beginning of the R session. gprfname <-"252603510022_8_G100526_12vsownmix.gpr" gprdata <-read.delim(gprfname, skip = 35, header = TRUE, sep = "\t", stringsAsFactors = F) gprdata$ORF <-namesextract(gprdata$ID) 4. Find the columns that will be used in the normalization process by using either string identity (==) or regular expressions: header = names(gprdata) block_c = which(header == "Block") col_c = which(header == "Column") row_c = which(header == "Row") id_c = which(header == "ID") name_c = which(header == "Name") genename_c = which(header == "GeneName") ORF_c = which(header == "ORF") median_ratios_c = which(header == "Median.of.Ratios..635.532.") SNR_c = grep("SNR", header) F_median_c = grep("^F[0-9]{3}.Median$", header) B_median_c = grep("^B[0-9]{3}.Median$", header) 5. Extract only a few columns of interest and do simple filtering and calculations for M, the log transformed ratio of red and green signal, log 2 (Red/Green) and A, the average intensity of the two signals 0.5*log 2 (Red*Green). A is a logaverage, the log value for the geometric mean of the two signals. The plot of ratios against average intensity can reveal intensity dependent artifacts that need correction. F_median <-gprdata[F_median_c] B_median <-gprdata[B_median_c] FB_median <-F_median -B_median FB_median[FB_median < 1] <-1 #adjust missing values to 1 names(FB_median) <-c("signal_R", "signal_G") to_norm = data.frame(gprdata[, c(ORF_c, block_c, col_c, row_c, id_c, name_c,genename_c, SNR_c, median_ratios_c)], FB_median) 6. Filter and calculate vaues that have low signal: minSNR = 5 #minimum acceptable signal to noise ratio goodones <-which(to_norm$signal_R > 1 & to_norm$signal_G > 1) filterSNRidx <-which(to_norm$SNR.635 > minSNR & to_norm$SNR.532 > minSNR) 7. Calculate M and A only on the signals that are worth it, others leave as NA (not available): goodx <-intersect(goodones, filterSNRidx) to_norm$M <-NA to_norm$A <-NA to_norm$M[goodx] <-log2(to_norm$signal_R[goodx]/to_norm$signal_G[goodx]) to_norm$A[goodx] <-0.5 * log2(to_norm$signal_R[goodx] * to_norm$signal_G[goodx]) 8. Split the data by group of barcodes (separate for up and down, since coming from two separate PCR reactions).

  ups <-grep("-U", to_norm$ID) downs <-grep("-D", to_norm$ID) to_norm$UD <-NA to_norm$UD[ups] <-"U" to_norm$UD[downs] <-"D" to_norm$UD <-as.factor(to_norm$UD) 9. Create a diagnostic plot for the two classes of barcodes (Fig. 2a): library(ggplot2) ggplot(to_norm[goodx, ], aes(x = A, y = M, color = UD)) + facet_grid(UD ~ .) + theme_bw() + geom_point(alpha = 0.33) + geom_smooth(method = "loess", size = 0.5, color = "black", alpha = 0.5) + ylim(-3, 6)

} 12 .

 12 loessnorm <-function(M, A) { loessfit <-loess(M ~ A, span = 0.8) #span is adjustable normM <-M -predict(loessfit) return(normM) Apply loess normalization to the two classes of signal (U/D):normU <-loessnorm(to_norm$M[indexU], to_norm$A[indexU]) normD <-loessnorm(to_norm$M[indexD], to_norm$A[indexD]) to_norm$normML <-NA #create a new column to_norm$normML[indexU] <-normU to_norm$normML[indexD] <-normD13. Verify that the normalization changed the distribution of the values (Fig.

  ggplot(to_norm[goodx, ], aes(x = A, y = normML, color = UD)) + facet_grid(UD ~ .) + theme_bw() + geom_point(alpha = 0.33) + geom_smooth(method = "loess", size = 0.5, color = "black") + ggtitle("Normalized") + ylim(-3, 6) 14. Recalculate the green and red signal values from the normalized ratios (if needed): to_norm$norm_R <-sqrt(2^(2 * to_norm$A + to_norm$normML)) to_norm$norm_G <-sqrt(2^(2 * to_norm$A -to_norm$normML)) 15. Combine the results for each UP and DOWN values, for each ORF and, next, combine the obtained UP and DOWN medians for each ORF. aggregated_UD <-aggregate.data.frame(to_norm[, c("normML", "A")], by = list(ORF = to_norm$ORF, UD = to_norm$UD), median, na.rm = T) aggregated_ORF <-aggregate.data.frame(aggregated_UD[, c("normML", "A")], by = list(ORF = aggregated_UD$ORF),median, na.rm = T) 16. Read a list of genomic positions for the different features of SGD (simple text file, tab delimited) and add this information to the normalized table of values: genopos <-read.delim("ordre_geno.txt", stringsAsFactors = F) aggregated_ORFgp <-merge.data.frame(aggregated_ORF, genopos, by.x = "ORF", by.y = "orf") 17. Estimate the presence of the "exclusion region" in the position that corresponds to the gene of interest, in this case the deleted gene is the 4057 th feature in the list (Fig. 3, see Note 13): ggplot(aggregated_ORFgp, aes(x = ordre_geno, y = -normML)) + theme_bw() + geom_point(alpha = 0.5) + geom_vline(xintercept = 4057, alpha = 0.5, col = "blue")

13 .

 13 The observed peak, centered on the position of the query strain deletion, correspond to gene deletions that are physically close on the chromosome. If the two markers are on the same chromosome, to find both of them in a single cell, a meiotic recombination event must occur. It is possible to use simple probabilistic rules and the Haldane transformation to correct the observed values and estimate genetic linkage distances, as explained in the supplementary information of the initial GIM paper (8). Briefly, we can estimate the recombination frequency r = 1/(1+query/reference), where the ratio query/reference is the one obtained in the GIM screen after normalization. Haldane genetic distance is found as ln(1-2r)/2 and shows a linear correlation with the known physical distance between the markers (Fig.S1Ein the mentioned supplementary information). Once a fit has been made, we correct the observed values for the effect of genetic linkage. The same method has been recently used to estimate changes in crossing over rates at different chromosomal positions, based on the results of large numbers of SGA screens (21). Peaks can be also inspected visually, for single GIM screens and the data having obvious biases can be removed from the final table.

  21. A. Baryshnikova, B. VanderSluis, M. Costanzo, et al. (2013) Global linkage map connects meiotic centromere function to chromosome size in budding yeast, G3 (Bethesda, Md.). 3, 1741-1751.

Figure legends Figure 1 .

 legends1 Figure legends

Figure 2 .

 2 Figure 2. a) Diagnostic MA plot to estimate variations in the observed signal ratios (M, log 2 transformed) with the intensity of the signal (A, average of signals for the two fluorescence channels, log 2 transformed). b) MA plot after loess normalization.

Figure 3 .

 3 Figure 3. Diagnostic plot showing measured double deletion strains growth as a function of the physical position of the tested loci along the yeast chromosomes.

Acknowledgments

The protocol presented here was set up initially by Laurence Decourty, Gwenael Badis-Breard and Alain Jacquier, data management and analysis were performed by Christophe Malabat, and large scale developments were made possible by the ANR funding of the GENO-GIM project (ANR-2008-JCJC-0019-01).


Florent Chuffart

Eddy Caron

Gael Yvert

Natural Functional Diversity of the Yeast Galactose Network

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Natural Func+onal Diversity of the Yeast Galactose Network Florent Chuffart, Eddy Caron and Gael Yvert

The aim of our research is to study the natural intra-species variability of the yeast galactose regulatory network. Our approach consists in studying more that 100 natural strains of yeast (S. cerevisiae).

The Galactose Regulatory Network benefits from both complex and well-characterized [P.J. Bhat, Galactose Regulon of Yeast, 2008] features.

We

  use a Green Fluorescent Protein (GFP) reporter under the control of the Gal1 Promoter and make single-cell acquisitions by flow cytometry. We will put in perspective in vivo experiments with in silico modelling, based on the models of [M. Acar et al., Nature, vol 435, 12 may 2005], [S. A. Ramsey et al., nature genetics, WIREs SysBio, vol 38, num 9, sept 2006], [V. R. Pannala, Vol 2, Jan/Feb 2010] [Hsu C. et al., Nat Commun. 2012 Feb 21;3:682] we will build a simulator and perform and perform dynamic simulations of the galactose response using SPADES. SPADES is high performance computing platform targeting different supercomputer. Parameters of the model will be fit to relevant observations and predictions from simulations will supervise further experiments. Our aim is to run simulations efficiently and to describe which network parameters vary across the species and which ones are stable. Our expectation is to describe and quantify the variability in terms of response distribution, kinetics and dose response. et al.] [S. A. Ramsey et al.]


In 2000, the Grids and Algorithms, or GRAAL, research team of INRIA, located in Ecole Normale Supérieures de Lyon, France, initiated the Distributed Interactive Engineering Toolbox project under the supervision of Frédéric Desprez and Eddy Caron. The project is focused on the development of scalable middleware with initial efforts on distributing the scheduling problem across a hierarchy of agents, at the top of which sits the Master Agent, or MA. At the bottom level of a DIET hierarchy one can find the Service Daemon, or SeD, agents. SeDs are connected to the MA by means of Local Agents, or LAs. Over the last few years, the Cloud phenomenon has been gaining more and more traction in the industry and in research communities because of its qualities, the most interesting of which is its ondemand resource provisioning model and its pay-as-you-go billing approach. We deem these features to be highly interesting for DIET.

From Grid to Cloud

The first step towards the Cloud was to enable DIET to take advantage of ondemand resources. This should be done at the platform level and be transparent to the DIET user. The authors and David Loureiro have targeted the Eucalyptus Cloud platform as it implements the same management interface as Amazon EC2, but unlike the latter it allows for customized deployments on the user 's hardware. The team has implemented the scenario in which the DIET platform sits completely outside of the Eucalyptus platform and treats the Cloud only as provider of compute resources when needed. This opened the path towards new researches around grids and Cloud resource provisioning, data management over these platforms and hybrid scheduling in general.

Cloud application resource scaling

The on-demand provisioning model for resource allocation and the pay-as-yougo billing approach that Cloud systems offer makes possible the creation of more cost-effective approaches for application resource provisioning. A Cloud application can scale its resources up or down to better match its usage and to reduce the number of unused, yet paid for, resources. This leads to smart auto-scaling strategies.

By taking into account research done around self-similarities in web traffic, the authors have developed a resource usage prediction model that identifies similar past resource usage patterns from a historic archive. Once identified, they provide an insight into what the short-term usage of the platform will be. This approach can be used to predict the usage of the most important types of resources of a Cloud client and thus give an insight of what type of virtual machine to instantiate or terminate when the Cloud application is rescaled. We have tested this approach against resource usage traces from one Cloud client and three production grids and obtained encouraging results.

Economy-based resource allocation

The dynamics that Cloud systems bring in combination with the agent-based DIET platform led us towards an economic model for resource provisioning. The ultimate goal is to guarantee resource sharing fairness and avoid starvation. The authors have done this by simulating the dynamics of a tender/contract-net market. In this market contracts are established between platform users (the DIET clients) and resource providers (the Recent Developments in DIET: From Grid to Cloud by Frédéric Desprez, Luis Rodero-Merino, Eddy Caron and Adrian Muresan

The Distributed Interactive Engineering Toolkit, or DIET, project started with the goal of implementing distributed scheduling strategies on compute Grids. In recent times, the Cloud phenomenon has yougo billing approach. This led to a natural step forward in the evolution of DIET, with the inclusion of Cloud platforms in resource provisioning. DIET will be used to test resource provisioning heuristics and to port new applications that mix grids and Clouds. DIET SeDs). Users send requests to the DIET platform for the execution of their tasks and resource providers reply with offers, each containing the cost and duration of the task execution. A userdefined utility function is applied to identify the best offer and the corresponding SeD will run the task.

In this scenario, platform users compete against each other for resource usage while the resource providers compete against each other for profit. Resource prices, which determine the offer costs, fluctuate depending on each provider's resource usage level. Hence, users will tend to choose SeDs with more free resources and so lower prices.

What's next?

Future directions include implementing a complete automatic resource scaling strategy for Cloud clients and testing against real-life situations.

We are also looking forward towards integrating Cloud-specific elements into the DIET scheduler for existing applications. The final goal is to see if deployment on a Cloud platform would yield a better performance and if so then with what scheduling modifications. Utility grids are generating increasingly huge amounts of metering information. Grid operators face rising costs and technical hurdles to aggregate and process data. Can Cloud Computing tools, developed notably by Web companies to deal with large data sets, also be used for power grid management?

As electricity in the current state of technology cannot be stored, consumption in power grids is continuously counterbalanced by production. Both producers and consumers are connected to the grid through metered Access Points (AP). Every month, grid operators have to determine the amount of energy produced or used by each stakeholder, knowing that the sum of produced energy, either out of the grid by power plants or within the grid (eg by wind turbines), equals the sum of consumed energy, from effective use and losses in the grid. Amounts are aggregated in order to obtain amount of "allocated" energy by stakeholder. The volume of data at stake in the allocation computation depends on the size of the grid.Data are produced every 15 minutes, and datasets may be huge. For example, there are roughly 8 million APs in Belgium, each producing 96 metering data outputs per day: allocation for a month would therefore require handling of more than 23 billion records.

Data aggregation is currently based on existing

Relational DataBase Management Systems (RDBMS). However performance of such software is declining with the increasing volume of data to process. Performance can be improved by investing in hardware and sophisticated software setups, like database clusters, but such an investment is not necessarily economical, with the cost of such a setup increasing disproportionately in relation to data processing capacity.

The goal of our research was to overcome the limitations of RDBMS by scaling performance according to growth of aggregated data. Moreover the allocation algorithm is a good candidate for parallelization as sums have to be performed on distinct data sets, ie, per stakeholder. For this reason we investigated the use of programming platforms and frameworks, identified as providing Platform as a Service (PaaS) on Cloud infrastructures, to enable scalability in data storage and processing.

Finally, we plan to study hybrid scheduling strategies mixing static grids and dynamic Clouds for a more efficient resource management of large scale platforms.

Figure 1 :

 1 Figure 1: The cloud-enabled DIET hierarchy.

ERCIM NEWS 83

 83 October 2010 Special Theme: Cloud Computing Addressing Aggregation of Utility Metering by using Cloud -The Power Grid Case Study by Orlando Cassano and Stéphane Mouton

Link:

The DIET project: http://graal.ens-lyon.fr/DIET Please contact: Adrian Muresan Ecole Normale Supérieure de Lyon, France Tel: +33 4 37 28 76 43 E-mail: Adrian.Muresan@ens-lyon.fr The slaves can run tasks in parallel on data physically located on this machine. All computed values will then be combined. This method is called MapReduce and has been originally developed by Google.

Retrieving information

Writing data


Benjamin André
email: benjamin@cozycloud.cc

Philippe Pucheral
email: philippe.pucheral@inria.fr

A ROOT OF TRUST FOR THE PERSONAL CLOUD by Nicolas Anciaux, Benjamin André, Philippe Pucheral and Paul Tran-Van

We are witnessing an exponential accumulation of personal data on central servers: data automatically gathered by administrations, companies and web sites, but also data produced by individuals themselves and stored in the cloud for convenience (e.g., photos, agendas, raw data produced by smart appliances and quantified-self devices). Unfortunately, there are many examples of privacy violations arising from abusive use or attacks, and even the most secured servers are not spared.

The Personal Cloud paradigm has recently emerged as a way to allow individuals to manage under their control the collection, usage and sharing of data in different contexts and for different types and sensitivities of data, as requested by the World Economic Forum 1 [START_REF] Abiteboul | Manage your digital life in your personal info management system[END_REF]. Initiatives like Blue Button and Green Button in the US, MiData in Great Britain and MesInfos in France give body to this paradigm by returning personal data retained by companies and administrations to individuals in a practical way. This user-centric vision illustrates the gravity shift of information management from organisations to individuals. However, at the same time as individuals recover sovereignty of their data, they also inherit the burden of organising this personal data space, and more importantly of protecting it against attacks and loss.

While much research work is tackling the organisation and semantic exploitation of the user's workspace, far less attention has been paid to security issues. Existing security solutions range from encrypting the personal data with an independent tool (e.g., TrueCrypt or BoxCryptor) before storing it on a cloud provider or relying on decentralised storage (such as OwnCloud, SandStorm, SeaFile, Unity or OpenPDS personal cloud platforms), letting the individual decide where to store his data. In both cases, data is usually encrypted with keys, which are in turn encrypted with the user's password. Hence, the password is de facto the root of security and the Achilles heel of these systems since all data are definitely lost if the user forgets his password or can be leaked if the password is compromised. This is a critical point considering that a personal cloud is expected to concentrate the complete digital estate of an individual. The sharing functionality among users is also very limited, implemented either by manually exchanging links to files or by compiling the access control within the encryption (e.g., by encrypting the file key with the recipient's public key) making access control static and cumbersome to manage.

We promote a different approach, called Secure Personal Cloud, where the root of security is made of secure hardware and the access control policies are easily extracted from the personal cloud itself, with minimal user interaction. Our solution is based on the tight integration of a Personal Cloud platform (Cozy) with a trusted component (PlugDB). Cozy is an open-source solution developed by Cozy Cloud. It is able to gather personal data from multiple sources and data providers, to organise it in a document database (CouchDB) and to synchronize it with multiple devices of the same user. PlugDB is a secure database engine embedded in a low-cost tamper-resistant hardware device (i.e., a combination of smartcard technology and Flash storage in a USB form factor token) [START_REF] Anciaux | MILo-DB: a Personal, Secure and Portable Database Machine[END_REF]. It is able to store data/metadata associated to Cozy documents, query them, manage access control rules and encrypt/decrypt data [START_REF] Anciaux | A Scalable Search Engine for Mass Storage Smart Objects[END_REF]. PlugDB is a research prototype from INRIA. Combining these two components allows the architecture depicted in Figure 1 to be set up. This architecture works as follows. Heterogeneous data issued by external sources (e.g., banks, employers, hospitals, commercial web sites) and by personal appliances (e.g., quantified-self devices, smart meters, domestic sensors, cameras) are all transformed into documents. Document metadata is extracted and stored in PlugDB and documents themselves are encrypted by PlugDB before being integrated in the Cozy store. Encryption keys never leave the secure sphere of PlugDB. Once encrypted, documents can be safely stored in the cloud for resiliency reasons or exchanged among users. Hence, PlugDB acts has a doorkeeper protecting the complete digital environment managed by Cozy. At the time an application/user asks for a Cozy document, the request is routed to PlugDB which evaluates the access control policy and decrypts the requested document if the authorisation check succeeds. Thanks to PlugDB tamper-resistance, sticky policies can be safely enforced when documents are shared among individuals. Encryption keys and access/usage control rules are transferred along with the shared document and are enforced at the recipient side, with no way for the recipient user to tamper with the processing on his own platform. The implementation of a proof-of-concept of this architecture is partly supported by the SECSi French PIA (Programme Investissement Avenir) project.

Important scientific, technological, societal and legal issues are raised by such Secure Personal Cloud architecture. Notably, our objective is to enable the execution of distributed queries linking the personal data of several individuals with the guarantee that neither the result of the query, nor the observation of all intermediate steps of the execution discloses any information about a particular individual. In other words, Privacy-by-Design big data treatments can be implemented on personal data. Another important challenge is to ease the declaration and administration of access control policies by the individuals. Our hope is that the Secure Personal Cloud approach will provide a credible alternative to the systematic centralisation of personal data on servers and will pave the way for new privacy-by-design architectures.

Figure 1 :

 1 Figure 1: Secure Personal Cloud Platform.

plugdb/ Cozy Cloud: https://cozy.io/fr/

https://project.inria.fr/