Thomas A Darde

Estelle Lecluze

Aurélie Lardenois

Isabelle Stévant

Nathan Alary

Frank Tüttelmann

Olivier Collin

Serge Nef

Bernard Jégou

Antoine D Rolland

Frédéric Chalmel
email: frederic.chalmel@inserm.fr

The ReproGenomics Viewer: a multi-omics and cross-species resource compatible with single-cell studies for the reproductive science community

Motivation:

Recent advances in transcriptomics have enabled unprecedented insight into gene expression analysis at a single-cell resolution. While it is anticipated that the number of publications based on such technologies will increase in the next decade, there is currently no public resource to centralize and enable scientists to explore single-cell datasets published in the field of reproductive biology.

Results: Here, we present a major update of the ReproGenomics Viewer (RGV), a cross-species and cross-technology web-based resource of manually-curated sequencing datasets related to reproduction. The redesign of RGV's architecture is accompanied by significant growth of the database content including several landmark single-cell RNA sequencing datasets. The implementation of additional tools enables users to visualize and browse the complex, high-dimensional data now being generated in the reproductive field.

Introduction

Advances in ultra-high-throughput sequencing technologies, including increased accuracy, diverse applications, and decreased overall cost have significantly contributed to their democratization [START_REF] Sims | Sequencing depth and coverage: key considerations in genomic analyses[END_REF]. As these techniques became more popular, a myriad of datasets were published, and their corresponding raw sequence read data made available from public repositories, such as the Sequence Read Archive [START_REF] Kodama | The Sequence Read Archive: explosive growth of sequencing data[END_REF]. While several databases devoted to reproductive biology were developed to provide access to relevant microarray-based transcriptomics data [START_REF] Lee | GonadSAGE: a comprehensive SAGE database for transcript discovery on male embryonic gonad development. A c c e p t e d m a n u s c r i t p t Article short title[END_REF][START_REF] Hsueh | Ovarian Kaleidoscope database: ten years and beyond[END_REF][START_REF] Luk | GermlncRNA: a unique catalogue of long non-coding RNAs and associated regulations in male germ cell development[END_REF][START_REF] Hua | Follicle Online: an integrated database of follicle assembly, development and ovulation[END_REF][START_REF] Lardenois | GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle[END_REF][START_REF] Schuster | SpermBase: A Database for Sperm-Borne RNA Contents[END_REF][START_REF] Lee | GermSAGE: a comprehensive SAGE database for transcript discovery on male germ cell development[END_REF][START_REF] Zhang | SpermatogenesisOnline 1.0: a resource for spermatogenesis based on manual literature curation and genome-wide data mining[END_REF], until recently no resources were able to manage ultra-high-throughput sequencing data. To deal with these data, in 2015 we introduced the ReproGenomics Viewer (RGV) [START_REF] Darde | The ReproGenomics Viewer: an integrative cross-species toolbox for the reproductive science community[END_REF], a web-based genomic resource for researchers of the reproductive science community. Its aim was to centralize and offer easy access to the published sequencing datasets (e.g., RNA-seq, ChIP-seq, and MNaseseq) that have accumulated in this field by overcoming the standard technical issues regarding data format, technology, and cross-species comparison. The system was based on implementation of a 'JBrowse genome browser' [START_REF] Buels | JBrowse: a dynamic web platform for genome visualization and analysis[END_REF]. It also included unique features such as the conversion of genome coordinates between species for the direct comparison of data acquired in different organisms. The Repro-Genomics Viewer was thus not only a multi-but also a cross-species resource for comparing genomics data.

More recently, the rapid emergence of novel technologies enabling the study of biological systems at a single-cell resolution [START_REF] Linnarsson | Single-cell genomics: coming of age[END_REF] has again presented serious concerns for the storage, mining, and visualization of such complex datasets [START_REF] Raja | A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries[END_REF]. Several dedicated databases and webservers have been set up to host [START_REF] Cao | scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells[END_REF][START_REF] Abugessaisa | SCPortalen: human and mouse single-cell centric database[END_REF] and to explore [START_REF] Lang | SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data[END_REF][START_REF] Detomaso | FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data[END_REF][START_REF] Zhu | Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists[END_REF][START_REF] Weinreb | SPRING: a kinetic interface for visualizing high dimensional single-cell expression data[END_REF] single-cell RNA-sequencing (scRNA-seq) datasets.

In response to the incoming wave of studies at a single-cell resolution in reproductive biology, we would like to introduce the new release of RGV. Single-cell transcriptome profiling has already contributed substantially to deepening our understanding of reproductive processes in mammals, including gonad development [START_REF] Stévant | Deciphering Cell Lineage Specification during Male Sex Determination with Single-Cell RNA Sequencing[END_REF][START_REF] Han | Mapping the Mouse Cell Atlas by Microwell-Seq[END_REF], spermatogonial stem cell biology [START_REF] Li | Single-Cell RNA-Seq Analysis Maps Development of Human Germline Cells and Gonadal Niche Interactions[END_REF][START_REF] Guo | DNA methylation and chromatin accessibility profiling of mouse and human fetal germ cells[END_REF], and spermatogenesis [START_REF] Lukassen | Characterization of germ cell differentiation in the male mouse through single-cell RNA sequencing[END_REF]. The novel architecture of RGV has been totally redesigned and now offers several visualization tools for browsing complex high-dimensional sequencing data including single-cell genomic data. The database content has also grown dramatically and now covers 11 biological topics related to reproduction in 9 species, including 5 landmark scRNA-seq studies. To the best of our knowledge, RGV provides the most comprehensive resource of manually-curated ultra-high-throughput sequencing data in the field of reproduction currently available and thus enables researchers to explore this invaluable source of information.

Methods

Data curation and processing

As previously described [START_REF] Darde | The ReproGenomics Viewer: an integrative cross-species toolbox for the reproductive science community[END_REF], the backbone of the Rep-roGenomics Viewer relies on a six-step pipeline to consistently download, curate, organize and process data within the system (Fig. 1, panels A-D). Briefly, i) PubMed, SRA, and GEO identifiers assigned to a given study are manually extracted (Fig. 1, panel A). These database entries are then used to mine other information relevant for describing the datasets, such as the species and the technologies. Manual curation is further required to standardize sample names across studies and to gather specific information about, for example, whether or not the RNA sequencing protocol is directional. Next, ii) raw data (sra files) are downloaded from the NCBI SRA repository. The sequencing reads (fastq files) are then iii) aligned to the appropriate reference genome with STAR v2.0 [START_REF] Dobin | Mapping RNA-seq Reads with STAR[END_REF] (Fig. 1, panel B). After read mapping iv), the deepTools suite [START_REF] Ramírez | deepTools2: a next generation web server for deepsequencing data analysis[END_REF] is used to convert the alignment files (bam files) into standard coverage tracks (bigWig files). During this process, coverage values are scaled by using a constant factor corresponding to the maximum number of mapped reads in all samples of a given dataset, divided by the number of mapped reads in the corresponding sample. The next step involves v) cross-species conversion, during which the genome coordinates of the scaled coverage tracks are converted to the other reference genomes indexed in the RGV system with CrossMap [START_REF] Zhao | CrossMap: a versatile tool for coordinate conversion between genome assemblies[END_REF], based on the pairwise whole genome alignment files (chain files) provided by the UCSC genome browser [START_REF] Kuhn | The UCSC genome browser and associated tools[END_REF] (Fig. 1, panel C). Finally, and only for transcriptomic datasets (Fig. 1, panel D), vi) expression at the gene level is accurately quantified for transcriptome studies by applying the StringTie suite (cuffquant and cuffnorm tools) [START_REF] Pertea | StringTie enables improved reconstruction of a transcriptome from RNA-seq reads[END_REF] on the alignment files (bam files). A principal component analysis is also performed with the FactoMineR package implemented in R [START_REF] Lê | FactoMineR: An R Package for Multivariate Analysis[END_REF] to project samples onto the first two components. are converted into standard coverage (.bw) files with the deepTools suite, with a scaling factor used to normalize the coverage of all samples within a given study. When strand information is available, two .bw files per sample (+/-) are generated, i.e. one for each strand of the chromosomes. C) A cross-species conversion is performed next, with the CrossMap tool and the chain files from the UCSC genome browser. This process allows the visualization of data from any species on a selected reference genome (human, mouse, chicken, and zebrafish in the current version). D) Specifically, for transcriptomic data, expression is quantified at the gene level with the StringTie suite on individual .bam files, by providing the reference annotation GTF file and using the cuffquant and cuffnorm tools. This procedure generates the gene expression matrix (.tsv file) suitable for the violin plot and the scatter plot visualization tools.

Data storage, management, and retrieval

The ReproGenomics Viewer database is based on MongoDB (https://www.mongodb.com), a free, open-source, and cross-platform document-oriented database program. This NoSQL database technology

A c c e p t e d m a n u s c r i t p t

Article short title provides relevant features for RGV, such as flexible storage of massive and rapidly changing types of data, data replication, and JavaScript compatibility.

Web interface

The web interface is implemented using two web frameworks, Pyramid (https://trypyramid.com/) and AngularJS (https://angularjs.org/). Pyramid embeds many features, such as a REST API, a JSON renderer, and compatibility with SMTP servers. AngularJS, on the other hand, is an open JavaScript framework that extends traditional HTML vocabulary; it allows implementation of readable and quickly developable web environments. To handle website traffic and to provide data security, scalability, and deployment, every component of RGV (including website server, MongoDB database, and Elasticsearch server) are hosted on separate individual virtual machines, with Docker (https://www.docker.com) as a container system.

Visualization tools

The ReproGenomics Viewer provides three ways to display datasets, including a genome browser and two gene-level visualization tools: the violin plot and the scatter plot.

The genome browser offers a full-featured and highly flexible display for genomic annotations and data via a high-performance, dynamic web interface powered by the implementation of a JBrowse web server [START_REF] Buels | JBrowse: a dynamic web platform for genome visualization and analysis[END_REF]. This system is fully compatible with a large spectrum of data types (fasta, gff, bam, bedGraph, wig, bigWig). The modular design of Jbrowse made it possible to implement three plugins to add new features to the system: i) multibigwig (https://github.com/elsiklab/multibigwig), which allows users to plot multiple samples from the same dataset on a single track, thereby facilitating data visualization;

ii) Bookmarks-JBrowse (https://github.com/awilkey/bookmarks-jbrowse/), which enables them to save selected tracks and options into a list of bookmarks and to add descriptions and/or comments;

iii) Screen Shot (https://github.com/bhofmei/jbplugin-screenshot), which lets users save a high-quality screenshot by using a dedicated dialog box with options including file format, size, quality, and track configuration.

The two gene-level visualization tools were built with Plotly.js, a high-level, declarative charting library based on the famous web graphical library D3.js. Like box plots, the violin plot is a method for representing the distribution of quantitative data (here, gene expression) across several groups of samples by using quartiles, but it also features a kernel density estimation (probability density) of the underlying distribution. The scatter plot enables exploration of complex datasets and the efficient visualization of a single gene's expression pattern following the projection of all samples in a lower-dimensional space.

Results

Overview and current content

The ReproGenomics Viewer website has a minimalist and userfriendly web interface. Its responsive design meets all modern web standards and enables users to display RGV on all their devices (desktops, tablets, and smartphones). Briefly, the home page and the top navigation bar offer rapid access to the visualization tools that allow scientists to explore all sequencing datasets listed in the 'Studies' tab. In the past few months, the RGV database content has grown qualitatively and quantitatively through the numerous requests from researchers of the reproductive science community. As of July 2018, the system embeds 2'970 samples from 79 published studies that were carefully curated before data processing and integration (Supplementary Table 1). Altogether these studies cover 11 biological topics related to the biology of reproduction in 9 species and involve 14 "-omics" technologies, including ATAC-seq and single-cell RNAseq (Table 1).

The genome browser tool: interactive exploration of large genomic datasets

The genome browser from RGV offers fluid and intuitive genomic navigation among chromosomes of nine species and thus allows multiple users to process data simultaneously. All datasets are formatted and configured for visual display at varying levels of resolution; users can pan and zoom efficiently over a genomic sequence region and simply turn genomics tracks on or off. The background of each track name is color-coded so that samples belonging to the same experimental condition share a similar color.

The full set of available tracks is listed in the faceted track selector tool that enables dynamic queries of RGV's large datasets by the successive application of a series of intersecting filters. This is made possible by the indexation of all study metadata into a CSV file, which facilitates access to information related to biological topics, tissues, age, and technologies (Fig. 2, panel A).

As previously described, one key feature of the RGV data processing workflow is the use of pair-wise alignment files provided by UCSC to perform cross-species conversion of genome coordinates. This allows the direct comparison of experiments in different organisms and thus makes the genome browser not only a multi-species but also a cross-species tool for comparing reproductive genomics data. In addition, the improved processing workflow considers the directionality of the library preparation kits; it can therefore display genomic information associated with the forward and reverse strands on two independent tracks per sample (one for each strand) when applicable (Fig. 2,panel B).

Importantly, the genome browser of RGV also integrates genetic variants known to be associated with reproductive disorders and fertility issues from the ClinVar database [START_REF] Landrum | ClinVar: improving access to variant interpretations and supporting evidence[END_REF], as well as the genomic distribution of sequence conservation scores (PhastCons score) provided by the UCSC genome browser [START_REF] Kuhn | The UCSC genome browser and associated tools[END_REF].

It is also noteworthy that users can directly upload their individual coverage (bigWig) and annotation (gtf, gff3, and vcf) files into the genome browser by using the option 'Open' in the 'File' tab. These uploaded data are accessible only during the user session and remain totally private. [START_REF] Jégou | Meiotic Genes Are Enriched in Regions of Reduced Archaic Ancestry[END_REF], showing the expression of ACR from the plus strand ("+" tracks) in spermatocytes and spermatids, and that of the LOC105373100 lncRNA (Gene ID: 105373100) from the minus strand ("-" tracks) in spermatids. To illustrate the cross-species and crosstechnology capabilities of RGV, three studies in rats and mice were also selected. The RNA-seq data of rat testicular cells demonstrate the conserved expression of ACR in spermatocytes and spermatids [START_REF] Chalmel | High-Resolution Profiling of Novel Transcribed Regions During Rat Spermatogenesis1[END_REF], while ChIP-seq data for A-MYB, H3K4me3, and RNA polymerase II in the adult mouse testis [START_REF] Li | An ancient transcription factor initiates the burst of piRNA production during early meiosis in mouse testes[END_REF] and ATAC-seq data in wild-type and Scml2 -/-mouse spermatogonia, spermatocytes, and spermatids [START_REF] Maezawa | Dynamic reorganization of open chromatin underlies diverse transcriptomes during spermatogenesis[END_REF] suggest that A-MYB plays a role, independent of SCML2, in regulating ACR expression in meiotic and post-meiotic germ cells by binding to its proximal promoter. coordinates as originally published and can be color-coded according to different classes of cells defined by the authors (cell types, developmental stages, etc). B) The expression levels of selected genes (Sox9 in this example) can then be displayed within each cell, which are color-coded according to the scale bar. C) Finally, the expression of candidate genes (Sox9 again in this example) within classes of cells (cell types, developmental stages, etc) can be summarized with violin plots; kernel density estimation makes it possible to display the full distribution of expression values.

Gene-level visualization tools: towards shifting paradigms in reproductive biology with single-cell sequencing

The current release of RGV embeds two new visualization tools: the violin plot and the scatter plot. Both are part of the current graphical arsenal used by the "single-cell" community for visualizing the structure of high-dimensional data [START_REF] Lang | SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data[END_REF][START_REF] Detomaso | FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data[END_REF][START_REF] Cao | scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells[END_REF][START_REF] Weinreb | SPRING: a kinetic interface for visualizing high dimensional single-cell expression data[END_REF].

These tools allow users to display quantitative (e.g. gene expression or chromatin accessibility) or qualitative (e.g. copy number variations) information in extremely large datasets that may contain several thousand samples or cells.

For this purpose, the scatter plot (and the violin plot to a lesser extent) relies especially on dimensionality reduction methods such as the tdistributed Stochastic Neighbor Embedding approach (t-SNE) [START_REF] Van Der Maaten | Visualizing Data using t-SNE[END_REF]. As of July 2018, five scRNA-seq datasets relevant to male and female gonad development [START_REF] Han | Mapping the Mouse Cell Atlas by Microwell-Seq[END_REF][START_REF] Stévant | Deciphering Cell Lineage Specification during Male Sex Determination with Single-Cell RNA Sequencing[END_REF], primordial germ and adult spermatogonial stem cells [START_REF] Li | Single-Cell RNA-Seq Analysis Maps Development of Human Germline Cells and Gonadal Niche Interactions[END_REF][START_REF] Guo | DNA methylation and chromatin accessibility profiling of mouse and human fetal germ cells[END_REF], and spermatogenesis [START_REF] Lukassen | Characterization of germ cell differentiation in the male mouse through single-cell RNA sequencing[END_REF] have already been indexed in RGV, and other unpublished studies are underways in the process of being submitted (Stévant et al., in preparation; Lardenois et al., in preparation). Of note is that although both the scatter plot and the violin plot were initially designed for singlecell studies, they are also fully compatible with and available for all "bulk" RNA-seq studies hosted in RGV.

Studies of interest can be selected through a dataset selector in the form of a table to which users can apply successive criteria (Fig. 3,panel A). In the scatter plots, the spatial distribution of all cells in a selected study is, by default, color-coded according to the subpopulation classes to which they belong (as published in the corresponding study) (Fig. 3, panel B). Users are then invited to select one or more genes of interest so that each individual cell is color-coded according to its relative expression level (Fig. 3, panel C). This feature could thus help users to identify novel markers specific for particular cell subpopulations, or simply to query their favorite genes.

RGV documentation and data download

The website provides a 'Tutorial' section containing several media types to allow researchers to become rapidly able to use the Repro-Genomics Viewer. Moreover, all scripts and data generated or used are freely available through the 'Download' page and the GitHub repository (https://github.com/fchalmel/RGV). The genome browser tool implemented in RGV lets users download each available individual track by choosing a track of interest and then clicking on 'Save track data'.

Conclusion and perspectives

We report a new version of the ReproGenomics Viewer resource intended to contribute to the reproductive science community by facilitating the centralization and harmonization of the sequencing data we have together accumulated. The system includes a unique workflow for sequencing data management, curation, processing, organization, and comparison across species. With the recent democratization of singlecell genomics, the number of these studies will increase rapidly in the near future. To deal efficiently with this new information, we have complemented existing features with two novel visualization tools, the scatter plot and the violin plot, which enable a more in-depth investigation of these highly complex datasets. This logical transition from "bulk" to single-cell approaches is also accompanied by additional major changes to the system, including a new user-friendly and powerful project selector that makes dataset retrieval and mining easier. Since the original publication [START_REF] Darde | The ReproGenomics Viewer: an integrative cross-species toolbox for the reproductive science community[END_REF], the database content has also increased dramatically -there are now 12 times as many samples as in 2015, covering four times as many biological topics related to reproductive biology. To the best of our knowledge, the ReproGenomics Viewer A c c e p t e d m a n u s c r i t p t is the most comprehensive and the only cross-species resource of sequencing data in this field.

We plan to keep improving the features and possibilities already offered by the ReproGenomics Viewer. For instance, in the short term, a lasso select tool will be added to the scatter plot visualization tool to generate additional plots based on manually-defined cell subpopulations. A false-color heatmap visualization tool will also be implemented to display the expression pattern of several selected genes within the same graphical representation. In the mid-term, we will allow the creation of private sessions so that users can upload and display their own processed data in the RGV system. Finally, in the long term, we intend to develop a social community toolbox in RGV that we hope will facilitate and stimulate collaborative work in our research field.

It goes without saying that we also intend to gather even more datasets from a wide range of species to cover additional biological topics relevant to reproductive biology. In our continuous effort to help researchers make the most of the technological breakthroughs in the life sciences, we also intend to make RGV compatible with other technologies, such as proteomics and bisulfite sequencing experiments. Other genetic information related to reproductive disorders (such as GWAS and Quantitative trait loci information from diverse sources and diverse model organisms) will also be added to the genome browser tool, as we already plan to integrate data from the GWAS catalog database [START_REF] Welter | The NHGRI GWAS Catalog, a curated resource of SNPtrait associations[END_REF].

To keep the resource up-to-date, we strongly encourage scientists to request new datasets and types of data to be added to the system. Indeed, in view of the community's demand for direct and easy access to processed data, we think that making raw data available to the community may be insufficient. We also encourage scientists to upload their own reprogenomic datasets to RGV before submission for publication. This could create a win-win situation for all parties, since submitted studies would benefit from enhanced visibility, while the community would take advantage of a continually updated resource. In this context we would like to draw attention to an important issue, which does not concern only RGV, but all (omics) databases in life sciences. Researchers often cite the studies hosted by the databases, but they forget to cite the databases themselves. These citations, however, are essential for the teams that develop and maintain such resources.

The ReproGenomics Viewer is a valuable resource for and supported by researchers in the field of reproductive biology. This system may help scientists and clinicians to overcome the data format and species issues so that they can compare their own datasets with relevant published studies. The very positive feedback of scientists, at international conferences notably, encourages us to maintain our effort and to improve RGV further in terms of both its content and its functionalities. The modular design of the RGV could be applied to other biological domains studied in several model organisms. In that sense, we believe that the Repro-Genomics Viewer resource can serve as a reference database in the life sciences.

Fig. 1 .

 1 Fig. 1. The RGV data processing pipeline. A) A schematic diagram of the strategy used to organize and process each individual sample in the RGV system from the published datasets. The information used to structure and organize the data -the Pubmed, GEO, and SRA IDs -are manually extracted from the publication first before automatic extraction and manual curation (species name, biological topic, technology, samples, etc). Raw data (.sra files) are next downloaded from the NCBI SRA archive and processed (see below, B-D), leading to the generation of two types of files: first, an indexed binary (bigWig or .bw) file for each sample, to enable fast remote access to the data in the genome browser; second, a tabulation-separated value (.tsv) file for each transcriptomic study, used to summarize gene expression levels in the violin and scatter plot visualization tools. B-D) A schematic diagram of the RGV data processing workflow. B) After download, the SRA Toolkit is used to convert raw data (.sra) files into .fastq files, which are further mapped on their corresponding genome sequences with STAR v2.0. Next, alignment (.bam) files

Fig. 2 .

 2 Fig. 2. The RGV genome browser. A) JBrowse faceted track selector. The track(s) to be displayed can be selected by applying successive filters to the track metadata. Data can be searched using (1) the topics-related panel and/or can be queried with (2) the text search engine. (3) Studies and data matching the search criteria are displayed in the track list panel for further selection. B) Genome browser overview. A screen capture from the RGV genome browser, showing the genomic environment of the human acrosin gene (ACR; Gene ID: 49), is presented together with the RefSeq annotation (green and orange boxes correspond to untranslated and coding exons, respectively, and the dark line represents introns) in the human genome (hg38). The RNA-seq analysis of human testicular cells from Jégou et al. was selected[START_REF] Jégou | Meiotic Genes Are Enriched in Regions of Reduced Archaic Ancestry[END_REF], showing the expression of ACR from the plus strand ("+" tracks) in

Fig. 3 .

 3 Fig. 3. The RGV visualization tools. Large datasets that may contain hundreds to thousands of cells or samples can be browsed and investigated through both the scatter plot (A and B) and the violin plot (C) visualization tools, which are also compatible with the bulk transcriptomic datasets hosted in RGV. The single-cell RNA sequencing analysis of mouse sf1+ fetal testicular cells is taken as an example (Stévant et al., 2018). A) Cells are displayed according to the T-distributed Stochastic Neighbor Embedding (t-SNE)

Table 1 . RGV content evolution.

 1

	2015	2018

Acknowledgements

The ReproGenomics Viewer database is supported, built, and maintained by the Research Institute for Environmental and Occupational Health (IRSET), the French School of Public Health (EHESP), and the GenOuest BioInformatics core facility.

We thank the Institut national de la santé et de la recherche médicale (Inserm) and the Université de Rennes 1 for supporting this work.

Funding

This work was supported by the French agency for food and safety [ANSES n° EST-13-081 and n° EST-17-256 to F.C.]; the French National Research Agency [ANR n° 16-CE14-0017-02 and n°18-CE14-0038-02 to F.C]; the Fondation pour la recherche médicale [FRM n° DBI20131228558 to F.C.], the Swiss National Science Foundation [SNF n° CRS115_171007 to B.J.], the DFG Clinical Research Unit 'Male Germ Cells: from Genes to Function' [CRU 326 to F.T.], the French National Institute of Health and Medical Research (Inserm), the University of Rennes 1 and the French School of Public Health (EHESP).

http://rgv.genouest.org. The website is implemented in Python, JavaScript, and MongoDB, and is compatible with all major browsers. Source codes can be downloaded from https://github.com/fchalmel/RGV.

Conflict of Interest: none declared.


Anas El Ankouri
email: anas.elankouri@orange.com

Anet Luiz

Ali Neto

Sylvain Sanhaji

Hugues Barthomeuf

Bertrand Le Bras

Abdelatif Le Guyader

Minqi Chagdali

N Wang

K Genay

Grzybowski

Experimental Demonstration of Real-time PDCP-RLC V-RAN Split Transmission over Fixed XGS-PON access

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

Introduction

Mobile networks have greatly evolved from the couple of hundreds of kbit/s in 2G to the hundreds of Mbit/s in 4G. Right now, the 5G is on its way with expected bit-rates in the range of Gbits/s [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] . A great deal of efforts is being put in to allow the increase in capacity of the air interface while introducing the least possible changes to the legacy fibre infrastructures connecting the antenna sites to the central offices.

The bit-rate increase foreseen in 5G is fomenting the rise of interesting applications, amongst which providing fixed access connectivity to users in rural areas. In this context, Fixed Wireless Access (FWA) [2] is often presented as a competing technology to low bitrate fixed access solutions such as Asymmetric Digital Subscriber Line (ADSL). In a nutshell, FWA allows providing broadband internet to house-holds through a mobile network such as 4G, as depicted in Fig. 1a, thus avoiding costly greenfield fibre deployments. The main challenge in this case is that fixed access prone services such as video streaming and online gaming impose higher constraints on the needed throughputs between the antenna sites and mobile evolved packet core (EPC) compared to standard mobile usages such as emailing and social media communication.

As opposed to this use-case, another interesting application can be considered with the 5G, this time for dense or ultra-dense areas. Indeed, the mobile traffic can be offloaded through available high capacity passive optical networks (PON), as seen in Fig. 1b. This would not only allow leveraging on already deployed fibre to the premise (FTTx) infrastructures but also alleviating the throughputs in the radio access network (RAN) while offering, through simple handover, seamless connectivity for the users on the transitions between typical mobile and fixed access contexts. This solution would also provide the unification of the wireless interfaces, from currently two (mobile and WiFi) to only one (5G), with higher capabilities in terms of air interference management. Moreover, large bandwidths could be targeted in the unlicensed or shared spectrum ranges [START_REF]What can we do with 5G NR Spectrum Sharing that isn't possible today?[END_REF] . In Fig. 1b, the customer premises equipment (CPE) embeds both fixed access and functional split based mobile functions. The RU+DU block carries layer 1 and lower layer 2 functionalities whereas a virtualized central unit (vCU) hosts higher layer 2 and layer 3.

We experimentally assess the transmission of 3GPP's option 2 interface [4] through a symmetrical 10G capable PON (XGS-PON) [5] access. This functional split is implemented on top of an Openstack virtualization environment together with the mobile evolved packet core (EPC). We investigate the impacts of dynamic bandwidth allocation (DBA) in the uplink transmissions and assess the robustness of the mobile interface to packet jitter introduced by an Ethernet traffic impairment engine that emulates degradations from the aggregation network.

Experimental setup

Fig. 2 shows our experimental setup, which can be viewed as three distinct parts:

The RAN blocks run on a host server containing the LTE protocol stack and EPC. They are implemented on a CentOS/Openstack virtualization environment on provisioned virtual machines (VM). Each set of services physically separated in an actual mobile network are logically separated in our server. For instance, the EPC, containing a domain name system (DNS) server, mobility management entity (MME), serving and packet data network gateway (SPGW) and the home subscriber server (HSS) communicates with the CU via the S1 (backhaul) interface. The CU is connected to the DU via an Ethernet encapsulated V1 (3GPP PDCP-RLC split) interface which goes out of the server. It then goes through our access and (emulated) aggregation network segments and loops back to our server, in the DU+RU block. The hosts layer 3 and higher layer 2 functions whereas the DU+RU hosts lower layer 2 functionalities. Layer 1 functionalities of the RU and user equipment (UE) are abstracted without loss of generality since our main goal is to evaluate the transmission of a high layer split through an optical transmission system. The UE VM is also implemented and provisioned in the same server.

The aggregation network is emulated with an Ethernet Degradation Engine. It allows us to add packet latency and jitter to an incoming V1 interface. The mobile interface is then transported through a 10Gigabit Ethernet switch and mixed with a 8 Gb/s overloading Ethernet signal which emulates Fixed Access Network (FAN) traffic. The two streams are tagged with different VLAN tags by the switch and then transported together in the same port towards our Fixed Access Network in the downlink. An equivalent scenario is set up for the uplink but with different ports of the switch associated to the different tags so that the dynamic bandwidth allocation procedures can be individually applied to mobile and overloading traffic.

Results and discussion

We start our experimental assessment by investigating the impact of eventual degradations coming from the aggregation network. Fig. 3 shows the effect of the induced packet jitter to our downlink V1 interface, with user datagram protocol (UDP) packets 1200 bytes long. The packet jitter values correspond to the standard deviation of a normally distributed latency, with 2 ms mean value. Three sets of measures are shown, namely, without packet jitter, with 0.1 ms jitter and with 0.66 ms jitter. We can see that for a transmission without added jitter, the PER (Packet Error Rate) is relatively low from 20 to 100 Mbit/s. After that, we have a steep ascent up to 150 Mbit/s. We believe that such limitation is coming from the limited resources in our virtual machines. When adding packet jitter, the overall PER is higher and we observe a steep ascent that begins earlier with respect to the bitrate, with much stronger impact with 0.66 ms.

The second set of measurements consisted on investigating the impacts of DBA in the uplink by assessing the needed response times to provide full assignment of a specific bit-rate to the mobile stream. UDP packets are transmitted via iperf from the UE to the EPC while the overloading traffic is always set to a maximum useful bit-rate of 8.5 Gb/s. We then measured the evolution of the bitrate in the V1 interface of the CU, which is shown in Fig. 4. Two bit-rates are considered, one below the maximum assigned bit-rate (100 Mb/s) and another at the limited assigned capacity (150 Mb/s). Fig. 4 compares the evolution of the bit-rate with and an optical back-to-back (B2B) point-to-point scenario, where the server is looping back directly in itself and thus without TDMA (Fig. 4 a andb). Fig. 4c andd show the upstream bit-rate evolution profile for our XGS-PON system.

Comparing the two setups, we can see that the bandwidth is more rapidly allocated in the B2B case compared to the XGS-PON. For 150 Mbit/s, the B2B setup allocates the required bandwidth in 60 ms to the CU, whereas in the XGS-PON setup, a two-fold increase in the system response is observed, with 120 ms. We can also observe that without congestion (100 Mb/s bit-rates), the XGS-PON and point-topoint set-ups behave similarly, as expected.

Finally, Tab. 1 shows the round trip time (RTT) latencies added by our XGS-PON system over 10 km compared to the point-to-point backto-back scenario. It can be observed that the XGS-PON not only increases the mean latency but also increases the packet jitter by a factor of 10, from 0.047 ms to 0.154 ms, which could impact the mobile transmission, depending on the service.

Conclusions

In this work, we demonstrated the feasibility of FWA for high dense area by transporting a mobile traffic through an emulated aggregation network and a fixed access network using XGS-PON technology. We assessed the impacts of introduced packet jitter to the downlink as well as the degradations induced by DBA profiles with limited assured bit-rates.

Fig. 1 :

 1 Fig. 1: (a) Rural and (b) dense area FWA scenarios.

  The FAN is composed by an WGS-PON optical line termination (OLT), and optical network unit (ONU), fixed attenuation emulating a splitter and 10 km of standard single mode fibre. Since PON operate under time division multiple access (TDMA) to allow sharing of the feeder fibre, a traffic container of type 3 (T-CONT 3) is associated to each ONU port. The DBA profile set on both ports allows for 150 Mb/s of assured traffic with the remainder of the capacity of the channel allocated on a besteffort basis. At the output of our ONU, we have two physically distinct streams transmitted over Ethernet. The streams are received by a second switch, which de-tags the streams before sending them back to the host server.

Fig. 2 :Fig. 3 :

 23 Fig. 2: Experimental setup

1 :Fig. 4 :

 14 Fig. 4: Upstream bitrate evolution for point-to-point B2B (a and b) and uplink XGS-PON (c and d).

Acknowledgements

This work was supported by the European H2020-ICT-2016-2 project 5G-PHOS.

Experimental Demonstration of Real-time PDCP-RLC V-RAN Split Transmission over Fixed XGS-PON access

A. El Ankouri

(1), [START_REF]Fixed Wireless Access: on a massive scale with 5G[END_REF] , L. Anet Neto [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] , A. Sanhaji (1) , S. Barthomeuf [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] , H. Le Bras [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] , B. Le Guyader [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] , A. Chagdali [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] , M. Wang [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] , N. Genay [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] , K. Grzybowski [START_REF] Grzybowski | IMT Vision-Framework and overall objectives of the future development of IMT for 2020 and beyond[END_REF] ,S. Durel (1) , P. Chanclou


Tania Richmond

Annelie Heuser

Benoit Gérard

Benoit Gérard Side

Side-Channel Analysis of Post-Quantum Cryptography


A El Ankouri
email: anas.elankouri@orange.com

L Anet Neto

G Simon

H Le Bras

A Sanhaji

P Chanclou

High-Speed Train Cell-less Network Enabled by XGS-PON and Impacts on vRAN Split Interface Transmission

We successfully demonstrate a transmission of a high layer split mobile interface for cell-less, high-speed train network applications using a commercially available XGS-PON. Operation is also demonstrated for a GbE interface.

Introduction

Providing higher bit-rates all the time and anywhere is a key feature of the coming 5G Radio Access Network (RAN). This includes solutions targeting vehicular transportation, which, of course, should allow for a seamless connectivity for the final users. As far as rail transportation is concerned, GSM-R [START_REF] He | High-Speed Railway Communications: From GSM-R to LTE-R[END_REF] relies entirely on circuit switching and can only be used for voice communication and signaling operations for trains and railways. For the coming mobile generation, the requirements for a successor railway communication technology beyond 3GPP's Future Railway Mobile Communication System (FRMCS) are given in TS 22.289 [2].

In this context, efforts focusing on extending the mobile coverage in rural areas are currently being intensified to improve mobile services in high-speed trains (HST). Highly densely populated urban areas also represent a challenging use-case, where often poorly covered deep-indoor infrastructures such as undergrounds should also be addressed. Whereas signal penetration remains the major issue for deep-indoor scenarios, guaranteeing high data rates can be challenging in HST due to the speeds at which users are moving, generally beyond 300 km/h. Indeed, with fast mobility comes great complexity. The management of the mobile handover as well as the propagation limitations imposed by the Doppler frequency shift [START_REF] Sarkar | Wireless cellular network for high speed (up to 500 km/h) vehicles[END_REF] need thus to be taken into consideration. A way to overcome the challenges of high-speed handover has been recently proposed where a mobile "cell-less" configuration is obtained thanks to an optical transmission system. A successful transmission was demonstrated in [START_REF] Dat | High-Speed and Handover-Free Communications for High-Speed Trains Using Switched WDM Fiber-Wireless System[END_REF] using Analog Radio-over-Fiber (A-RoF) and per-antenna wavelength allocation. Wavelength Division Multiplexing (WDM) handover-free operation was enabled by a fast tunable laser with service interruption times in the order of some µs.

Here, we propose an equivalent cell-less network which is enabled, differently, by a commercially available 10 Gb/s symmetrical Passive Optical Network (XGS-PON) system. In our work, each Trackside Remote Unit (TRU) is connected to an Optical Network Unit (ONU), the latter being connected to the Optical Line Termination (OLT) through a classical point-to-multipoint (PtMP) topology as shown in Fig. 1. The PON will actually behave most the time as a point-to-point (PtP) system, being only temporarily switched to a 1:2 PtMP transmission during the transitions between an ONU and its neighbor as the HST moves along the rail. This solution would notably allow us to keep simple and robust to optical noise on-off keying (OOK) operation with low-cost commercially available off-the-shelf (COTS) small factor plug-and-play optical transceivers (SFP+) with a standardized and soon to be deployed optical access solution. First, we experimentally demonstrate the principle of the proposed solution Then, we evaluate the impacts of the transmission of a real-time high-layer split (HLS) virtual RAN (vRAN) interface [START_REF] Ankouri | Experimental Demonstration of Real-time PDCP-RLC V-RAN Split Transmission over Fixed XGS-PON access[END_REF]. Finally, we address the XGS-PON parameters that would allow transmission at different RF frequencies.

Experimental setup

The experimental setup of our XGS-PON capable of enabling bi-directional fast switching between ONUs is depicted in Fig. 2. The top left-most block is an Ethernet traffic generator that emulates any mobile functional split option. We evaluate a GbE interface. Apart from this generic Ethernet interface, we also assess a real-time packet data convergence protocol/radio link control (PDCP/RLC) functional split corresponding to 3GPPs option 2 (c.f. inset Fig 1). This solution is based on virtualized central units (vCU) running on an Openstack cloud platform (for more details, please refer to [START_REF] Ankouri | Experimental Demonstration of Real-time PDCP-RLC V-RAN Split Transmission over Fixed XGS-PON access[END_REF]). This traffic is fed to an XGS-PON OLT connected to two ONUs through an optical coupler. The ONUs are connected to a probe device that allow us to visualize the bit-rate evolution and switching in time for our PON system.

The switching between TRUs is emulated by a 10GbE switch. The switching method relies on the aging time of its media access control (MAC) address table which is configured to 20s. Connected simultaneously to both of our ONUs, after 20s the switch transmits the flow from one ONU to the other, similar to what would happen on an actual transition of a train from one TRU to the next one. Finally, an Ethernet packet analyzer is used to evaluate the transmission of the generic GbE interfaces. A distributed unit (DU) and an emulated radio unit (RU) and user equipment (UE) are used to complete the downlink transmission for the PDCP/RLC split scenario. Our setup also supports upstream traffic with standard time-division multiple access (TDMA) operation. It should be noted that the main advantage of a virtualized CU in our approach is that it could facilitate the implementation of longer reach railways. Indeed, the handover of the mobile backhaul interface between zones covered by different PON trees could be easily implemented by simple transfer of vCU virtual machines between regional nodes (see Fig. 1).

In order to demonstrate the feasibility of the cell-less network with an XGS-PON system, the degradations during the transitions from on ONU TRU to another should be minimized. To do so, we explicitly disable antispoofing rules of our PON during a short period of time in which the two ONUs will be transmitting at the same time in the uplink with the same source MAC address. In a real implementation, some sort of white list police would have to be implemented for groups of three consecutive ONUs (previous/current/next) to allow for localized MAC flapping.

Results

The first set of results corresponds to the PDCP-RLC real-time mobile transmission. We transmitted a stream of 1200 bytes user datagram protocol (UDP) packets at a 100 Mbit/s. We assigned to our mobile traffic a dynamic bandwidth allocation (DBA) profile using a type 2 T-CONT with 150 Mbit/s fixed bandwidth to assure that our system could handle eventual bandwidth bursts higher than 100 Mbit/s without having to discard packets. In Fig. 3 (left), at the 17:10:28 time mark, the switch allows for a transition from ONU 1 to ONU 2. The transition period had a value of three seconds in which the communication wasn't interrupted and our system didn't suffer any additional Packet Errors at the reception of our mobile date. We should notice that this time limitation is exclusively due to our railway emulation. On an actual implementation, switching times could possibly be as small as 125µs, which corresponds to the minimum duty cycle and frame duration on a PON system. Fig. 3 (right) we can two cycles of the upstream transmission with a higher power burst corresponding to ONU 1. Fig. 4 shows the results for the generic GbE interface. In Fig. 4 (left), we can see that at 15:09:16, for both uplink and downlink transmissions, the transition from ONU 1 to ONU 2 is done under less than 1 second. During this time interval, both ONUs are transmitting at the same time. The DBA profile in this case was configured to transmit a maximum bandwidth up to 9 Gbit/s with an assured bandwidth of 1 Gbit/s. We didn't stress our system to the limit due to a bandwidth limitation of our probe device, incapable of providing throughput visualization beyond 1 Gbit/s. Fig. 4 (right) shows the two optical bursts of the ONUs.

Conclusions and possible deployment scenarios

In this work, we demonstrated a real-time bi-directional transmission of a generic functional split GbE interface as well as a PDCP-RLC split at 150 Mbit/s in an emulated HST scenario. Our cell-less network is demonstrated using a commercial XGS-PON system allowing operation with low-cost COTS SFP+ transceivers and standard OOK operation. In order to open up the possibilities of such solution, Tab. 1 shows three possible deployment use-cases. For instance, for a 700 MHz carrier, an 1:4 PON compatible with optical budget class N2 could be considered. By setting the distance between adjacent ONUs in the railway to 40 km, we would allow compatibility with the maximum differential distance between ONUs and between ONU and OLT [START_REF]1 Amendment 1, 10-Gigabit-capable symmetric passive optical network (XGS-PON)[END_REF] while guaranteeing a cell radius below 6.5 km (from Friis equation [START_REF] Friis | A Note on a Simple Transmission Formula[END_REF]). This would allow for an uninterrupted communication time of approximately 26 minutes per PON tree considering a speed of 360 km/h. Tab. 1: Three use-cases for cell-less XGS-PON.

Fig. 1 :

 1 Fig. 1: HST XGS-PON "cell-less" network.

Fig. 2 :

 2 Fig. 2: Experimental Setup and the switching between two ONUs with an Ethernet interface emulating a RAN functional split interface [5].Then, we evaluate the impacts of the transmission of a real-time high-layer split (HLS) virtual RAN (vRAN) interface[START_REF] Ankouri | Experimental Demonstration of Real-time PDCP-RLC V-RAN Split Transmission over Fixed XGS-PON access[END_REF]. Finally, we address the XGS-PON parameters that would allow transmission at different RF frequencies.

Fig. 3 :

 3 Fig. 3: ONUs bitrate and PER evaluations (left) and OLT bursts corresponding to the two ONUs in the uplink (right).

Fig. 4 :

 4 Fig. 4: ONUs bit-rate variation for the down and uplinks (left) and uplink bursts (right).

  For an RF link budget of 120dB 2 ΔLONU: distance between adjacent ONUs on a rail track 3 ΔLDIFF., MAX: maximum differential distance between ONUs 4 LMAX: Maximum distance between ONU and OLT 5 Minimum needed optical budget/class6 Uninterrupted communication time per PON (360 km/h)

	Carrier frequency	Max. cell radius (Friis) 1	#ONUs	ΔL ONU | cell radius 2	ΔL DIFF., MAX	3	L MAX 4	OB 5	T 6
	700 MHz	34 km	4	40 km | 20 km	40 km		60 km	31 dB (N2)	26 min
	3.5 GHz	6.5 km	16	5 km | 2.5 km	35 km		37.5 km	29 dB (N1)	13 min
	25 GHz	1 km	64	1 km | 0.5 km	31 km		31.5 km	36 dB (E1)	10 min
	1