Evolutionary conservation of unusual N-glycosylation sites in the human
glycosyltransferase B4GALNT2

Virginie COGEZ¹, Anaïs BARRAY², Jérôme DE RUYCK¹, Sophie GROUX-DEGROOTE¹ and Anne HARDUIN-LEPERS¹

1. Univ.Lille, CNRS, UMR 8576, Structural and Functional Glycobiology Unit, 59655 Villeneuve d'Ascq, France

2. Bilille, CRISTAL (UMR CNRS 9189 Univ. Lille), 59655 Villeneuve d'Ascq, France

Corresponding Author: virginie.cogez@univ-lille1.fr

Post translational modifications (PTM) enhance the functional diversity of proteins. Among these PTM, glycosylation represents one of the most abundant and complex modification of proteins and N-glycosylation is determinant for cell survival. For 96% of N-glycosylproteins, glycosylation occurs at the canonical motif [N]-!P-[S/T] known as sequon [1]. In the laboratory, we study the evolutionary relationships and molecular evolution of glycosyltransferases that are Golgi enzymes implicated in the last steps of glycosylation [2] like B4GALNT2 implicated in the synthesis of blood group antigen Sda [3]. This enzyme is a glycoprotein and our working hypothesis is that the gain or loss of an ancestrally conserved N-glycosylation site in this enzyme might have resulted in the evolution of protein structure, subcellular localization and functional modifications with impact on the phenotype [4-7]. As a first towards answering this question, we identified over 150 B4GALNT-related sequences (37 B4GALNT1 paralogues and 85 B4GALNT2 orthologues) from public databases (NCBI, Ensembl, CAZy) using a BLAST approach. We carried out sequence-based analysis using various multiple sequence alignment algorithms (MUSCLE, ClustalW, MAFFT, T-Coffee, ClustalW) to delineate informative regions and define conserved sequence motifs. Furthermore, we used NetNGlyc 1.0 server [8] and GlycoMine [9] to predict potential N-glycosylation sites. We found differentially conserved N-glycosylation sites in the homologous B4GALNT sequences: a canonical sequon, [N]-!P-[S/T], present in 11 mammalian B4GALNT2 sequences, which has disappeared in the human B4GALNT2 sequence [10]. This N-glycosylation site is also present in some ancestral B4GALNT sequences and has disappeared in the paralogous B4GALNT1 sequences suggesting no major role for this N-glycan. Interestingly, two atypical N-glycosylation sites [N]-X-[C] and [N]-[G] were predicted in mammalian B4GALNT2 sequences. Despite the fact that the [N]-[G] site is present in almost all the mammalian B4GALNT2 sequences, we found using PNGase F, an enzyme that remove N-Glycans, that the human B4GALNT2 enzyme is not glycosylated at this position, but at a second unusual [N]-X-[C] glycosylation site. This latter was found to be highly conserved in nearly all B4GALNT sequences except the teleostean B4GALNT2 sequences further suggesting an important functional role, which is currently investigated.

References

[1] Zielinska DF, Gnad F, Wiśniewski JR, Mann M. *Cell*. May 28;141(5):897-907, 2010

[2] Harduin-Lepers A. *Glycobiology Insights* 2, 29-61, 2010.

[3] Dall'Olio F, Malagolini N, Chiricolo M, Trinchera M, Harduin-Lepers A. *Biochim Biophys Acta*, 1840(1):443-53, 2014.

[4] Ruggiero FM, Vilcaes AA, Iglesias-Bartolomé R, Daniotti JL. *Biochem J*. Jul 1;469(1):83-95, 2015.

[5] Yi L, Bozkurt G, Li Q, Lo S, Menon AK, Wu H. *Sci Rep*. Apr 4;8:45912, 2017.

[6] Mühlenhoff M, Manegold A, Windfuhr M, Gotza B, Gerardy-Schahn R. *J Biol Chem*. Sep 7:276(36):34066-73, 2001.

[7] Zhuo Y, Yang JY, Moremen KW, Prestegard JH. *J Biol Chem*, 291(38):20085-95, 2016.

[8] R. Gupta, and S. Brunak. *Pacific Symposium on Biocomputing* 7:310-322, 2002

[9] Li, F, Li, C, Wang, M, Webb, GI, Zhang, Y, Whisstock, JC, Song J. *Bioinformatics* 31: 1411-1419, 2015

[10] Montiel, L, Krzewinski-Recchi, MA, Delannoy, P, Harduin-Lepers, A. *Biochem. J.* 373, 369-379. 2003
# Comparison of statistical methods of inference of cooccurrence networks within microbial ecosystems from metagenomics data

Julie LAO¹, Mahendra MARIADASSOU¹ and Sophie SCHBATH¹

¹ Institut National de la Recherche Agronomique, UR 1404 MaIAGE, F-78352, Jouy-en-Josas, France

Corresponding Author: julie.lao@inra.fr

Metagenomics consists of experimentally characterizing a microbial ecosystem as a whole without prior isolation of the different microorganisms composing it. Many microorganisms are not culturable and separate analyses of each microorganism result in a warped understanding of the ecosystem, as they overlook close relationships between these microorganisms (mutualism, parasitism). Metagenomics enable us to apprehend a microbial ecosystem globally.

Metagenomics data raise many methodological questions, as studies are increasingly moving beyond the mere constitution of a catalogue of species or genes and towards more complex analyses accounting for spatial data, time series and covariates. In particular, it is not clear how best to perform interaction studies and, more precisely, how to detect associations within the ecosystem [1].

In recent years, several statistical methods were developed to detect significant cooccurrences between species, in different ecosystems and under different experimental conditions. These methods assume that cooccurences are indicative of biological interactions between species [1] and interactions are thus revealed by reconstructing the cooccurrence network. SparCC [2], REBACCA [3] and SPIEC-EASI [4] are recently developed tools for the problem of network reconstruction in microbial ecology.

On this poster, we present a benchmark on the main reconstruction methods. Accuracy and running time was assessed on both simulated and real metagenomic data.

## References

[1] Lima-Mendez, G., Faust, K., Henry, N., Decelle, J., Colin, S., Carcillo, F., ... Raes, J. (2015). Determinants of community structure in the global plankton interactome. *Science*, **348**(6237), 1262073. doi:10.1126/science.1262073

[2] Friedman, J., & Alm, E. J. (2012). Inferring Correlation Networks from Genomic Survey Data. *PLOS Computational Biology*, **8**(9), e1002687. doi:10.1371/journal.pcbi.1002687

[3] Ban, Y., An, L., & Jiang, H. (2015). Investigating microbial co-occurrence patterns based on metagenomic compositional data. *Bioinformatics*, **31**(20), 3322-3329. doi:10.1093/bioinformatics/btv364

[4] Kurtz, Z. D., Müller, C. L., Miraldi, E. R., Littman, D. R., Blaser, M. J., & Bonneau, R. A. (2015). Sparse and Compositionally Robust Inference of Microbial Ecological Networks. *PLOS Computational Biology*, **11**(5), e1004226. doi:10.1371/journal.pcbi.1004226
# ShRCAn: a user-friendly R-Shiny application for quantitative metagenomics analysis

Florence THIRION¹, Emmanuelle LE CHATELIER¹, Nicolas PONS¹, Anne-Sophie ALVAREZ¹, Pierre LEONARD², Dusko EHRLICH¹,³

1. MGP MetaGénoPolis, Institut National de Recherche Agronomique, Université Paris-Saclay, 78350 Jouy en Josas, France.

2. DSI Global Services, 92350 Le Plessis-Robinson, France

3. Centre for Host Microbiome Interactions, Dental Institute, King's College London, UK.

Corresponding Author: florence.thirion@inra.fr

## 1. Introduction

In quantitative metagenomics, sequenced reads are usually mapped onto a reference gene catalogue yielding a gene abundance table (or counting matrix). The increasing number of metagenomic studies and reference gene catalogues makes it necessary to develop efficient new tools able to deal with those abundance tables, in order to ensure standardization of analysis as well as making it easier and faster for the user, especially concerning repeated tasks. Here we present ShRCAn, a user-friendly software that takes an abundance table as input to preprocess the data, perform the preliminary analysis, and enable data visualization.

## 2. Materials and methods

ShRCAn (Shiny application for Raw Counting matrix Analysis) is written in the free language R. It uses the package shiny, that helps in building interactive applications in R; the package MetaOMineR (momr)[1] that provides useful functions for analysis of metagenomic data; the package ggplot2 that creates graphics; and optionally the package PARConnector that enables to use the ProActive Parallel Suite, a scheduler which manages high-performance computing to reduce execution time by parallelizing the tasks.

## 3. Results

The workflow starts with preprocessing of the raw matrix, which consists in clustering of samples (to detect contamination), downsizing, RPKM-normalization, and metagenomics species (MGS) matrix generation. Then statistical computations between different classes of samples are performed, including richness comparisons or MGS contrast studies based on discriminant genes. The results are finally displayed as boxplots or barcodes. Furthermore, ShRCAn is designed in such a way that it is always possible to easily add new analytical steps at the end of the workflow in order to improve its capabilities.

## 4. Conclusion

ShRCAn is an interactive shiny application that provides a user-friendly and standardized way to process gene abundance table, from pre-processing to statistical analysis. Automation and speedup of the data preprocessing allows the bioanalyst to have more time in the interpretation of biological results.

## References

[1] Edi Prifti, Emmanuelle Le Chatelier. MetaOMineR: A Quantitative Metagenomics Data Analyses Pipeline 1.1, 2014. Available from https://cran.r-project.org/web/packages/momr/index.html.

[2] Emmanuelle Le Chatelier, Trine Nielsen, Junjie Qin, Edi Prifti, Falk Hildebrand, Gwen Falony et al. Richness of human gut microbiome correlates with metabolic markers. Nature (500):541–546, 2013.

[3] H Bjoern Nielsen, Mathieu Almeida, Agnieszka Sierakowska Juncker, Simon Rasmussen, Junhua Li, Shinichi Sunagawa et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nature Biotechnology (32): 822–828, 2014.
Genetic diversity of *Anaplasma phagocytophilum* among ticks and roe deer in a fragmented agricultural landscape

Amélie CHASTAGNER<fnref n="1" />, <fnref n="2" />, Angélique PION<fnref n="2" />, Hélène VERHEYDEN<fnref n="3" />, Bruno LOUTRET<fnref n="3" />, Bruno CARGNELUTTI<fnref n="3" />, Denis PICOT<fnref n="3" />, Valérie Poux<fnref n="2" />, Émilie BARD<fnref n="2" />, Olivier PLANTARD<fnref n="4" />, Karen D. MCCOY<fnref n="5" />, Agnès LEBLOND<fnref n="6" />, Gwenaël VOURC'H<fnref n="2" />, and Xavier BAILLY<fnref n="2" />

Corresponding Author: xavier.bailly@inra.fr

*Anaplasma phagocytophilum* is a tick-borne pathogen infecting multiple vertebrate host species, including humans. The tick species *Ixodes ricinus* is considered as its main vector in Europe. While the prevalence of *A. phagocytophilum* in ticks is often low, *i.e.* below 5 % in questing *I. ricinus* nymphs, it can reach higher values in host populations. As an illustration, roe deer frequently shows a prevalence of *A. phagocytophilum* infection above 80 %. Roe deer are considered as a major reservoir for this pathogen, but the genotypes they carry are different from the ones that infect domestic animals and humans. We thus investigated whether roe deer was the main source of *A. phagocytophilum* genotypes circulating in questing *I. ricinus* nymphs collected either in pastures or in forest of a French fragmented agricultural landscape.

We first identified infected samples from 1,837 *I. ricinus* nymphs, sampled on geo-referenced transect lines, and 75 roe deer, tracked with GPS devices. Molecular characterization has been performed with high-throughput sequencing to take into account potential co-infections. A target region of around 350 bp was amplified by nested PCR for each marker gene: *groEL*, an housekeeping gene (coding for a chaperone protein), *msp4* and *ankA*, 2 genes associated to host specificity. We defined different alleles for each locus and analyzed the observed multilocus genotype structure.

The analysis of high-throughput *A. phagocytophilum* sequences resulted in the delineation of several genotypes for each locus: 4 for *msp4*, 5 for *ankA* and 9 for *groEL* with a frequent occurrence of co-infections. A graph approach focusing on the distribution of alleles among hosts and vectors identified two groups of alleles at the different loci, *i.e.* alleles in linkage disequilibrium, respectively associated to roe deer and tick samples. A multidimensional scaling (MD) approach combined with a multivariate analysis of variance also indicated that ticks and roe deer carried different genotypes.

<footnote n="1">¹ Evolutionary Ecology Group, Univ. Antwerpen, 2610 Wilrijk, Belgium</footnote>

<footnote n="2">² EPIA, INRA, VetAgro Sup, 63122, Saint Genès Champanelle, France</footnote>

<footnote n="3">³ CEFS, UR0035, Comportement et Ecologie de la Faune Sauvage, Université de Toulouse, INRA, 24 chemin de Borde-Rouge, F-31326, Castanet-Tolosan cedex, France</footnote>

<footnote n="4">⁴ BIOEPAR, INRA, Oniris, 44307, Nantes, France</footnote>

<footnote n="5">⁵ MIVEGEC (UMR 5290), Maladie Infectieuses et Vecteurs: Ecologie, Génétique Evolution et Contrôle, Centre National de la Recherche Scientifique-Université de Montpellier-Institut de Recherche pour le Développement (UR224, 911 Avenue d'Agropolis, BP 64501, F-34394 cedex 5, Montpellier, France</footnote>

<footnote n="6">⁶ EPIA, INRA, and Equine Medicine, VetAgro Sup, 69280, Marcy-L'Étoile, France</footnote>
# A transcriptional study of five fungal *Mucor* strains

Annie LEBRETON<fnref n="1" />, Laurence MESLET-CLADIERE<fnref n="1" />, Jean-Luc JANY<fnref n="1" />, Georges BARBIER<fnref n="1" /> and Erwan CORRE<fnref n="2" />

Corresponding Author: corre@sb-roscoff.fr

The fungal genus *Mucor* belongs to the Mucoromycota phylum, one of the five groups of the early diverging fungi. *Mucor* species are ubiquitous, they show diverse lifestyles and may have contrasting impacts on human activities. Indeed, some pathogenic *Mucor* species represent a threat for human health, some others can be involved in food spoilage whereas some few others can be used for Asian fermented food manufacturing or cheese ripening. Despite these impacts on human activities, little is known on the genus *Mucor* and most of the studies focused only on human pathogens. Here, we are investigating on specificities linked to *Mucor* species lifestyles. We engaged a transcriptomic analysis focused on five species: *M. fuscus* and *M. lanceolatus*, two technological species used in cheese ripening, *M. racemosus*, a recurrent cheese spoiler, *M. circinelloides*, a pathogenic species and *M. endophyticus*, a plant endophyte species.

Strains of *M. fuscus* UBOCC 1.09.160 (MF), *M. lanceolatus* UBOCC 1.09.153 (ML), *M. racemosus* UBOCC 1.09.155 (MR), obtained from the *Université de Bretagne Occidentale Culture Collection*, *M. endophyticus* CBS 385-95 (ME) and *M. circinelloides* CBS 277.49 (MC) ordered from the Centraal Bureau Voor Schimmelculture CBS were grown on a standard medium and total RNA was extracted. Between 25 millions (ML) and 35 millions (ME) pairs of reads were obtained from the paired end sequencing. Transcriptomes were assembled *de novo* with Trinity, low coverage transcripts were detected with RSEM and removed from transcriptomes. Since the percent of gene with isoforms was different among species (from 7% for ME to 31% for ML), a single transcript per Trinity gene was selected to create the studied transcriptomes. Completeness of these new transcriptomes were assessed with BUSCO. Ribosomal RNA were detected with RNAmmer, CDS were predicted with Transdecoder. On these predicted proteomes, protein domains, signal peptides and transmembrane domains were annotated with respectively HmmScan against PFAM-A, signalP and tmhmm. Homologies were searched against Uniref90, swissprot-uniprot and tree Mucoromycota species. To this functional annotation were added GO terms and EC numbers transferred from Mucoromycota homologies. EC numbers were also inferred by profile detection with PRIAM. Orthogroups were predicted with OrthoFinder allowing us to propose a core transcriptome and transcripts specific to groups of species. Each of these groups of transcripts was functionally characterized by examining the composition of functional annotation (GO terms, EC numbers, protein signal...). A special interest was given for groups composed only by transcripts of species sharing the same lifestyle such as the technological species MF and ML. In the same time was investigated the repartition of genes suspected to show specificities linked to *Mucor* species lifestyles like excreted proteins and secondary metabolites. These investigations allowed to spot groups of transcripts that could be linked to a given species. This study is expected to give new hints regarding adaptation to specific habitat and/or lifestyle.

<footnote n="1">¹ Laboratoire Universitaire de Biodiversité et Ecologie Microbienne (LUBEM) - Université de Bretagne Occidentale (UBO), ESIB - Parvis Blaise Pascal - Technopôle Brest-Iroise, 29280, Plouzané, France</footnote>

<footnote n="2">² Station biologique de Roscoff - plateforme ABiMS - CNRS : FR2424 - Université Pierre et Marie Curie (UPMC) - Paris VI, Place Georges Teissier, BP 74 29682, Roscoff CEDEX, France</footnote>
Multi-Cloud deployment for microbial genomes analysis

Jonathan LORENZO<fnref n="1" />, Bryan BRANCOTTE<fnref n="1" />, Thomas LACROIX<fnref n="2" />, Mohamed BEDRI<fnref n="1" />, Jean-François GIBRAT<fnref n="1" /> and Christophe BLANCHET<fnref n="1" />

Corresponding Author: jonathan.lorenzo@france-bioinformatique.fr,
Thomas.lacroix@inra.fr

In the post-NGS area, sequencing bacterial genomes is very cheap (few hundreds €). Most of the time, users are no longer content to analyse a single genome; they want to compare large collections of related genomes (strains). This entails that biologists have to pay too much attention and dedicate their time to sequence the genomes, instead of thoroughly analysing the genomic data. Thus, this brings light to the increasing need for automating the annotation of bacterial genomes and carrying out efficient data mining.

In that context, IFB hub and the IFB-MIGALE platform developed a virtual environment (appliance), based on virtual machines, called "bacterial genomics" that aims to provide biologists and bioinformaticians access to suitable resources via the cloud. For example, Prokka [1] is a software tool for the rapid annotation of prokaryotic genomes. Insygh [2] developed by IFB-MIGALE is a tool for the visualization of the syntenies (local conservation of the gene order along the genomes) and the exploration of the landscape of both conserved and idiosyncratic genomic regions across multiple genomes. The platform automatically launches a set of bioinformatics tools (e.g. BLAST, HMMER, Prodigal...) to analyse the data and stores the results in a relational database (PostgreSQL). These tools use several public reference data collections. A web interface allows the user to browse the results. Setting up the platform requires solid skills in system administration since many bioinformatics tools with different dependences need to be installed as well as a relational database management system, a web server and servlet container, etc. Moreover, performing the analysis of a large number of genomes requires large computing resources and the use of parallel computing.

The goal is to deploy the “appliance” in one click over one or more cloud infrastructures. To achieve this, new features to automate deployment of complex application were added to the IFB’s cloud portal [3] through the connection to the SlipStream cloud broker [4]. Developed by SixSq, SlipStream is a multi-cloud application management platform. It automates the full application management lifecycle, within Infrastructure as a Service (IaaS) cloud infrastructures. Such complex application deployments can be done over several cloud infrastructures and provide scientists with high-level cloud features such as the dynamic allocation of a dedicated network for the isolation of the virtual machines, with the replication of the user data and with a direct link from the cloud portal to the Insygh web portal [5]. The appliance is available in the RAINBio catalogue of virtual images on the Biosphere web portal [6], and several tutorials on IFB bioinformatics cloud services usage are also available online on the main IFB website.

<footnote n="1">¹ CNRS, UMS 3601 ; Institut Français de Bioinformatique, IFB-core, Avenue de la Terrasse, F-91190 Gif-sur-Yvette, France</footnote>

<footnote n="2">² MaIAGE, INRA, Université Paris-Saclay, 78350 Jouy-en-Josas, France</footnote>

<footnote>[1] Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014 Jul 15;30(14):2068-9. PMID:24642063</footnote>

<footnote>[2] Lacroix T., Loux V., Gendrault A., Hoebeke M., and Gibrat J.F. Insygh: navigating amongst abundant homologues, syntenies and gene functional annotations in bacteria, it's that symbol! Nucleic Acids Res. 2014 Dec 1; 42(21): e162. doi: 10.1093/nar/gku867 ; PMCID: PMC4245967</footnote>

<footnote>[3] https://biosphere.france-bioinformatique.fr/</footnote>

<footnote>[4] http://sixsq.com/products/slipstream/index.html</footnote>

<footnote>[5] https://cyclone.france-bioinformatique.fr/usecases/view/125</footnote>

<footnote>[6] https://biosphere.france-bioinformatique.fr/catalogue/appliance/19/</footnote>
# Evaluation of 15 in silico prediction tools for the classification of *MED13L* missense variations

Thomas SMOL<sup>1,2</sup>, Caroline THUILLIER<sup>2</sup>, Sylvie MANOUVRIER-HANU<sup>1,3</sup> and Jamal GHOUMID<sup>1,3</sup>

<sup>1</sup> RADEME Research Team EA7364, Université de Lille, 59000, Lille, France

<sup>2</sup> Institut de génétique médicale, CHRU Lille, 59000, Lille, France

<sup>3</sup> Service de génétique clinique, CHRU Lille, 59000, Lille, France

Corresponding Author: thomas.smol@chru-lille.fr

## Abstract

**Introduction.** *MED13L* is a well conserved gene involved in the structure of the Mediator Complex. Truncating variations were reported in patients with intellectual disability (ID). Hence, *MED13L* was included in sequencing panels for ID diagnosis and the analysis of missense variations was not obvious. Here, we compared 15 bioinformatics predictive tools to aid in the *MED13L* missense interpretations.

**Methods.** All missense positions for *MED13L* were extracted from ExAC database, and considered as class-1 or 2 [1] if there were also present in EVS database or if the allele counts were upper than 1 in ExAC. All class-4 and 5 missenses [1] were found in Clinvar, Decipher, Pubmed, or in our in-house *MED13L* database as french referent laboratory for *MED13L* pathogenic variations for the healthcare network "DefiScience". Fifteen *in silico* predictive tools were used. They were based on evolutionary conservation (FATHMM, MutationAssessor, PhD-SNP, PANTHER, SIFT), on protein structure/function and evolutionary conservation (Align GVGD, MutationTaster, PolyPhen2, SNAP2, UMD-predictor), on protein function (SNPs&GO), on similarity between variant sequence and homolog sequence (PROVEAN), or on combined tools (CONDEL, CADD and REVEL) with different cut-offs. We compared sensibility (SE), specificity (SP), accuracy (ACC), and Matthew's correlation coefficients (MCC) [2]. A second analysis was performed on missense considered as deleterious for CONDEL. All databases were consulted in January 2017.

**Results.** We analysed 513 missense variations from *MED13L*. Eleven missenses were considered as class-4 or 5 and 502 as class-1 or 2. All pathogenic missenses were never found in ExAC database. Their median PhyloP score was 5.731 [4.304 to 6.172] and median Grantham score was 94 [15 to 145]. The median number of allele count in ExAC for benign missenses was 7, with a mean count of 25, a median PhyloP score of 2.872 [-2.455 to 6.318] and a median Grantham score of 58 [0 to 215]. CONDEL showed the best performance in MCC measures with 0.33, and all pathogenic variations were properly classified (SE = 100%). With caution, due to the small number of pathogenic missenses, the higher SE values (100%) were obtained with CONDEL, Polyphen2, CADD (cut-off 15 and 20), UMD-predictor, PANTHER and MutationTaster. Conversely, these 6 tools shared lower SP. After filtering on non-deleterious variations for CONDEL (n = 239), CADD (cut-off 20) showed the best compromise with a SE of 100%, a SP of 22.67%, and a MCC measure of 0.12.

**Conclusions.** Considering MCC as the best parameter to measure predictor's performance and the need to keep all pathogenic variations, CONDEL seems to present the best compromise. Moreover, combination between combined tools CONDEL and CADD (cut-off 20) has allowed us to reduce the number of class 1 and 2 variations without affecting the number of class-4 and 5 variations in *MED13L* gene analysis.

## References

[1] Richards S, et al. *Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology*. Genet Med. 2015 May;17(5):405-24.

[2] Johnson MM, et al. *Screening for deleterious nonsynonymous single-nucleotide polymorphisms in genes involved in steroid hormone metabolism and response*. Cancer Epidemiol Biomark. 2005 May;14(5):1326-9.
HiFit: robust data analysis method for High-throughput qPCR

Mathieu BAHIN¹, Quentin VIAUTOUR¹,², Elise DIAZ²,³, Bertrand Ducos²,³,⁴ and Auguste GENOVESIO¹

¹ Scientific Center for Computational Biology, Institut de Biologie de l'ENS, 46, rue d'Ulm, 75005, Paris, France

² High Throughput qPCR Facility, Institut de Biologie de l'ENS, 46, rue d'Ulm, 75005, Paris, France

³ LPS-ENS, CNRS UMR8550, 46, rue d'Ulm, 75005, Paris, France

⁴ Laser Microdissection Facility, CIRB Collège de France, 11 Place Marcelin Berthelot, 75005, Paris, France

Corresponding Author: mathieu.bahin@biologie.ens.fr

Real-time quantitative polymerase-chain-reaction (RT-qPCR) is frequently used as a standard technique for various applications such as research or clinical diagnostic. To date, several data analysis strategies have been proposed to extract meaningful information from single RT-qPCR curves [1,2,3]. Most of them are in fact semi-automated because they were developed in the context of low-throughput RT-qPCR data where each reaction can be visually investigated and its analysis manually corrected. We observed that portability of those methods to high throughput data was far to be straightforward and lacked robustness. In fact several of them could simply not be fully automated. To address this question we have developed an absolute high throughput qPCR data analysis approach based on a robust fitting of a four or six parameters sigmoid model. We take advantage of the throughput such that the search of the optimal parameters for each curve is achieved using information gathered from the fitting of large sets of curves obtained from the whole dataset. Our approach brings the level of robustness required to address high throughput qPCR data.

References

[1] Kenneth J Livak and Thomas D Schmittgen. Analysis of relative gene expression data using real-time quantitative pcr and the 2-δ̂δct method. *Methods*, 25(4):402-408, 2001.

[2] DV Rebrikov and D Yu Trofimov. Real-time pcr: a review of approaches to data analysis. *Applied Biochemistry and Microbiology*, 42(5):455-463, 2006.

[3] Robert G Rutledge and Don Stewart. Assessing the performance capabilities of lre-based assays for absolute quantitative real-time pcr. *PLoS One*, 5(3):e9731, 2010.
# Is UniProtKB Missing Knowledgeable Proteins?

Benoit BELY<fnref n="1" />, Sara BENMOHAMMED<fnref n="1" />, Guoying Qi<fnref n="1" />, Nidhi TYAGI<fnref n="1" /> and Maria MARTIN<fnref n="1" />

Corresponding Author: benoit.bely@ebi.ac.uk

## 1 Introduction

In 2015 UniProt removed 46.9 million unreviewed UniProtKB/TrEMBL records found in redundant proteomes (see: http://www.uniprot.org/help/proteome_redundancy). The redundancy pipeline [1] computes proteome redundancy for bacteria proteomes and, since UniProt 2016_08 release, for fungi proteomes as these two divisions constitute the majority of the new proteomes sequenced. Since 2015 the number of removed records has steadily increased such that UniProtKB (2017_01 release) was composed of 81.2 million protein identifiers (PIDs) from the INSDC (International Nucleotide Sequence Database Collaboration), generating 73.7 million UniProtKB/TrEMBL records with 153.5 million PIDs excluded because they were classified as redundant. Therefore, two-thirds (65.3%) of the INSDC proteins are missing in UniProtKB and by removing these sequences is the UniProtKB no longer a comprehensive resource?

## 2 153 million protein identifiers not in UniProtKB

A large proportion of new genomic sequencing data are from closely related organisms already sequenced and published in the INSDC. This means that the protein sequences from these organisms are not unique but do generate new PIDs. Therefore, these redundant protein sequences are stored in UniParc, UniProt's sequence archive by default. It contains all the protein sequences, including those from the INSDC, where a unique sequence will have a unique UniParc identifier (UPI) and then identifier cross-references to database sources having the unique sequence. Therefore, a UPI can have multiple links to UniProtKB and INSDC as long as the sequence represented by that UPI is the same. Among the 153.5 million PIDs not integrated into UniProtKB 149.7 million have a UPI which also links to a UniProtKB accession. Only 3.8 million PIDs (2.5%) represented in 666,988 UPIs (unique sequences) do not have identical sequence in UniProtKB.

Question: Of the 666,988 missing sequences is UniprotKB missing important functional knowledge that requires new UniProtKB/TrEMBL records with unique annotations? Before answering this question, it is important to know that annotations made in UniProtKB/TrEMBL come from automatic annotation pipelines using InterPro integrated signatures and if two different sequences contain the same set of InterPro integrated signatures, then both records will have exactly the same annotation. We evaluated if any of the 666,988 UPIs match a sequence in the Pan-Proteomes or UniRef90 using Blastp. Then we analyzed whether statistically significant subject/query hits have a differential InterPro integrated signature set by using InterProScan to see if UPIs would make new unique automatic annotation. 65,238 and 582,164 UPIs have a significant hit (identity>80% and hit coverage>50% length of query and subject) in Pan-Proteomes and UniRef90, respectively. From these hits, 122 UPIs have a differential InterPro integrated signature set that could potentially lead to a new annotation. However, 18,803 UPIs did not return a significant Blastp hit; where 9,811 UPIs are fragments, identity and coverage of the query is > 90% but subject coverage is < 50%. The other 8,992 UPIs, 2,643 UPIs have InterPro signature set that could potentially lead to a new annotation.

## 3 Conclusion

It is correct not to integrate 65.3% of INSDC into UniProtKB. The CDSs from INSDC have a high level of redundancy, 97.5%, to CDSs sequences already present in UniProtKB. Of the remaining 2.5% sequences that are technically missing from the UniProtKB only 4.2% of these sequences (2,765/666,988) could potentially add valuable knowledge to the knowledgebase by inferring new functional annotations.

## References

[1] Bursteinas B, Britto R, Bely B, Auchincloss A, Rivoire C, Redaschi N, O'Donovan C and Martin MJ. Minimizing proteome redundancy in the UniProt Knowledgebase *Database (Oxford)*, 2016 Dec 26, 2016.

<footnote n="1">¹ Protein Function Development EMBL-EBI, Wellcome Trust Genome Campus, CB10 1SD, Hinxton, UK</footnote>
Co-option of complex molecular system in bacterial membranes

Rémi DENISE<fnref n="1" />,<fnref n="2" />, Sophie ABBY<fnref n="3" />,<fnref n="4" /> and Eduardo PC ROCHA<fnref n="1" />,<fnref n="2" />

Corresponding Author: remi.denise@pasteur.fr

Protein secretion systems exist in many bacterial and archaeal species and are important for bacterial virulence. These systems are complex machineries made of many different proteins that interact together to allow other proteins to pass through the cell wall and be secreted outside of the cell. The proteins constituting these systems show various evolutionary rates and patterns of conservation within the secretion systems. Many of these systems were co-opted in complex evolutionary processes from other molecular structures.

The co-option is the emergence of new complex forms and molecular systems from other systems with different functions. This process of co-option (or exaptation) of functional or structural traits is thought to be a major driver of functional innovation [1]. The appendages of bacteria provide some striking examples of these processes. For example, the type III secretion system (T3SS) was co-opted from the bacterial flagellum [2].

Biochemical, phylogenetic, and structural evidence show that the family of molecular machineries including type II secretion system (T2SS, involved in protein secretion), type IV pilus (T4P, involved in cell motility, adherence and virulence), Tad pilus (idem), the competence apparatus (Com, involved in natural transformation) and the archaeal flagellum (Archaellum, motility) share homologous genes and have a similar genetic organization [3].

We designed custom comparative genomic tools to detect and distinguish macromolecular systems in genome sequences, based on their particular components and genetic organization (MacSyFinder, [4]). This allows us to investigate the evolutionary origins of these machineries by phylogenetic and comparative genomics approaches, and thus to decipher some mechanisms of co-option involved in the diversification of microbial cellular machineries. Another goal would be the use of these phylogenetic analyses to facilitate the discrimination between related systems, and produce tools to perform the automatic annotation of an unknown system.

We have detected more than 6400 systems of these family of systems among all the bacteria (mostly in Proteobacteria), and we have identified their key components (ATPase, inner membrane platform, major pilin, prepilin peptidase, secretin) on a dataset of more than 5750 complete genomes. For each key component, we've established a phylogeny, and we are now trying to reconcile them and understand the biological reasons of their discordance by inferring the phylogeny of this family of systems.

The systems analyzed are probably among the most complex network of molecular co-options analyzed to date and should provide an excellent basis to (i) infer the frequency of horizontal transfer of each type of derived molecular system, and (ii) study the evolution of the genetic organization of the loci encoding these systems in the light of their evolutionary history.

1. Gould, S.J. and E.S. Vrba, *Exaptation-A Missing Term in the Science of Form*. Paleobiology, 1982. **8**: p. 4-15.

2. Abby, S.S. and E.P. Rocha, *The non-flagellar type III secretion system evolved from the bacterial flagellum and diversified into host-cell adapted systems*. PLoS Genetics, 2012. **8**(9): p. e1002983.

3. Korotkov, K.V., M. Sandkvist, and W.G. Hol, *The type II secretion system: biogenesis, molecular architecture and mechanism*. Nature Reviews. Microbiology, 2012. **10**(5): p. 336-51.

4. Abby, S.S., et al., *MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas Systems*. PLoS ONE, 2014. **9**(10): p. e110726.

<footnote n="1">¹ Institut Pasteur, Microbial Evolutionary Genomics Group, 28 rue du Docteur Roux, 75015, Paris, France</footnote>

<footnote n="2">² CNRS, UMR 3525, 28 rue du Docteur Roux, Paris, France</footnote>

<footnote n="3">³ Univ. Grenoble Alpes, Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble (TIMC-IMAG), F-38000 Grenoble, France</footnote>

<footnote n="4">⁴ CNRS, TIMC-IMAG, F-38000 Grenoble, France</footnote>
# High-quality, fast, and memory-efficient assembly of metagenomes and large genomes using Minia-pipeline

Rayan Chikhi¹, Charles Deltel², Guillaume Rizk², Claire Lemaitre², Pierre Peterlongo², Kristoffer Sahlin⁴, Lars Arvestad³, Paul Medvedev⁴ and Dominique Lavenier²

¹ CRISTAL, (UMR CNRS 9189), France. ² IRISA, Univ Rennes, France. ³ Stockholm University, Sweden. ⁴ Pennsylvania State University, USA.

Corresponding author: rayan.chikhi@univ-lille1.fr

## 1 Abstract

Gigabase-scale genome projects and large metagenomics studies have flourished thank to high-throughput sequencing technologies. However, performing de novo assembly of such data remains challenging. In the landscape of assembly software, the tools that produce high-quality assemblies typically require significant computational resources, while the fast and memory-efficient ones yield relatively inferior results. We present Minia-pipeline: an assembler that combines efficiency and high-quality results. Minia-pipeline is geared towards large datasets of metagenomes and eukaryotic genomes, and recently provided high-ranking assemblies in the Critical Assessment of Metagenomic Interpretation challenge. This poster describes the overall architecture of the pipeline, key algorithmic improvements, and demonstrate its effectiveness on both large genome and metagenome samples. The pipeline is modular and integrates several components: an error-correction module, a unitig assembly tool (BCALM 2 [1]), a multi-k contigs assembly module (Minia 3), and a scaffolder (BESST [2]). Software is available at https://github.com/GATB/gatb-minia-pipeline

## References

[1] Rayan Chikhi, Antoine Limasset, and Paul Medvedev. Compacting de bruijn graphs from sequencing data quickly and in low memory. *Bioinformatics*, 32(12):i201–i208, 2016.

[2] Kristoffer Sahlin, Rayan Chikhi, and Lars Arvestad. Assembly scaffolding with pe-contaminated mate-pair libraries. *Bioinformatics*, page btw064, 2016.
# Debugging long-read genome and metagenome assemblies using string graph analysis

Pierre MARIJON<fnref n="1" />, Jean Stéphane VARRÉ<fnref n="2" /> and Rayan CHIKHI<fnref n="2" />

Corresponding author: pierre.marijon@inria.fr

Third-generation long-read sequencing technologies tackle the repeat problem in genome assembly by producing reads that are long enough to span most repeat instances. In principle one expects that with such reads most bacterial genomes will be assembled into a single contig [1]. However in practice, some datasets fail to be perfectly assembled even with leading assemblers, and are fragmented into a handful of contigs. As a mean to investigate those cases, we consider the string graphs that are generated by assemblers during intermediate stages of the assembly process. We seek to establish a coherent framework for analyzing these graphs, in the hope that they will help us determine the biological causes that led the assembler to output shorter contigs. This poster presents some preliminary results of such an analysis.

We visualized, analyzed and compared assembly graphs generated by *Canu* [2] and *Miniasm* assemblers [3] on biological (MBRAC-26 [4]) and synthetic datasets (created with LongISLND [5]). We introduce the concept of *graph projection* of an assembly graph onto another, taking advantage of the recent GFA format. We are thus able to observe how reads that are neighbors of contigs extremities overlap, in terms of error rate and overlap length. We implemented an automatic and user-friendly *snakemake* pipeline that generates a HTML report for each assembly. We identified cases of contigs that were not joined by the assembler despite indications in the string graph that such joins could have been made. These cases highlight potential directions on how to improve the assembly process. In future work we will take advantage of this investigation to propose alternative assembly hypotheses based on string graph analysis.

## References

[1] Sergey Koren and Adam M Phillippy. One chromosome, one contig: complete microbial genomes from long-read sequencing and assembly. *Current Opinion in Microbiology*, 23:110-120, 2015.

[2] Sergey Koren, Brian P. Walenz, Konstantin Berlin, Jason R. Miller, Nicholas H. Bergman, and Adam M. Phillippy. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. *Genome Research*, page gr.215087.116, 2017.

[3] Heng Li. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. *Bioinformatics*, 32(14):2103-2110, 2016.

[4] Esther Singer, Bill Andreopoulos, Robert M. Bowers, Janey Lee, Shweta Deshpande, Jennifer Chiniquy, Doina Ciobanu, Hans-Peter Klenk, Matthew Zane, Christopher Daum, Alicia Clum, Jan-Fang Cheng, Alex Copeland, and Tanja Woyke. Next generation sequencing data of a defined microbial mock community. *Scientific Data*, 3:160081, 2016.

[5] Bayo Lau, Marghoob Mohiyuddin, John C. Mu, Li Tai Fang, Narges Bani Asadi, Carolina Dallett, and Hugo Y. K. Lam. LongISLND: in silico sequencing of lengthy and noisy datatypes. *Bioinformatics*, 32(24):3829-3832, 2016.

<footnote n="1">¹ Inria, Université de Lille, CNRS, Centrale Lille, UMR 9189 - CRISTAL - Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France</footnote>

<footnote n="2">² Univ. Lille, CNRS, Centrale Lille, Inria, UMR 9189 - CRISTAL - Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France</footnote>
# Labsquare une communauté de développeurs libres
http://www.labsquare.org

Sacha SCHUTZ¹, Pierre MARIJON¹, Jérémie ROQUET¹, Francisco PINA-MARTINS¹, June SALLOU¹, Eugene TROUNEV¹, Anne-Sophie DENOMMÉ¹ and Olivier GUEUDELOT¹

Labsquare Team

Corresponding author: sacha@labsquare.org

## 1 Objectif

Avec l'évolution des technologies de séquençage haut débit, la bioinformatique est devenue un point de passage obligatoire en biologie ou encore en médecine pour le diagnostic moléculaire. L'analyse de ces données génomiques nécessite le plus souvent des compétences informatiques hors de portée d'un non-bioinformaticien qui souhaite gagner en autonomie. Plusieurs startups ont d'ores et déjà pris le devant pour répondre à cette demande en produisant des logiciels payants accessibles à tous à l'aide d'interfaces graphiques simples et épurées. Labsquare est une communauté de développeurs qui veut se poser comme alternative aux solutions commerciales en produisant des interfaces graphiques libres et accessibles dans le domaine de la génomique en suivant le même modèle que certaines associations à but non lucratif comme Framasoft ou KDE.

## 2 Communauté

Nous sommes pour l'instant une petite équipe composée de bioinformaticiens, developpeurs, designers, médecins et généticiens. Nous communiquons et travaillons ensemble grâce à des outils comme Github, Gitter ou Framatalk.

## 3 Technologie

Le framework C++ Qt est une de nos technologies préférées en nous permettant de réaliser des interfaces graphiques modernes et multiplateformes. A titre d'exemple Rstudio, Mendeley, Bandage et Alamut sont codé avec Qt. Les applications labsquare sont et seront toujours libres sous licence GPL3 en respectant les standards du GA4GH (Global Aliance for Genomics and Health).

## 4 Applications

Quatres applications sont déjà disponibles ou en cours de developpement.

* **FastQt** est le clone de FastQC[1] et permet d'analyser des fichiers FASTQ.
  https://github.com/labsquare/fastQt

* **CuteVCF** est un viewer de fichier VCF.
  La prochaine version, CuteVariant se calquera sur le fonctionnement de variant-tools[2].
  https://github.com/labsquare/CuteVCF

* **CutePeaks** Un simple visualisateur de fichier AB1 (Sanger trace file).
  https://github.com/labsquare/CutePeaks

* **BigBrowser** Un genome browser mixant les caractéristiques d'IGV[3] [4] et d'Alamut visual.
  https://www.youtube.com/watch?v=Y7ouuS8Ooo0

## References

[1] Simon Andrews et al. Fastqc: a quality control tool for high throughput sequence data, 2010.

[2] San Lucas, F Anthony, Gao Wang, Paul Scheet, and Bo Peng. Integrated annotation and analysis of genetic variants from next-generation sequencing studies with variant tools. *Bioinformatics*, 28(3):421–422, 2012.

[3] Helga Thorvaldsdóttir, James T Robinson, and Jill P Mesirov. Integrative genomics viewer (igv): high-performance genomics data visualization and exploration. *Briefings in bioinformatics*, 14(2):178–192, 2013.

[4] James T Robinson, Helga Thorvaldsdóttir, Wendy Winckler, Mitchell Guttman, Eric S Lander, Gad Getz, and Jill P Mesirov. Integrative genomics viewer. *Nature biotechnology*, 29(1):24–26, 2011.
# Influence of SNP coding on the analysis of disease risk

Hélène SARTER¹, Corinne GOWER-ROUSSEAU¹, Guillemette MAROT²,³

¹ Lille Inflammation Research International Center - U995, F-59000 Lille, France

² Univ. Lille, EA 2694 - Santé publique : épidémiologie et qualité des soins, F-59000 Lille, France

³ Inria Lille Nord Europe, MODAL, F-59000 Lille, France

Corresponding Author: helene.sarter@chru-lille.fr

## 1 Introduction

In genome wide association studies, although it is a strong biological assumption, SNP (Single Nucleotide Polymorphisms) additive model is often used before testing the association between a phenotype of interest and SNPs. SNP additive model refers to the coding of SNP by the number of rare allele and allows to consider SNPs as continuous variables and to use statistical methods that only allow continuous predictors (for example lasso or PLS regression). Yet, data on the impact of this choice is lacking. The objective of our study was to test, in a simulation framework, the influence of SNP coding on the selection of SNPs linked to a phenotype of interest.

## 2 Methods

We used data from EPIMAD registry [1] on the severity of Crohn's disease. 156 patients have been genotyped for 369 variants. We used the following simulation framework: the 12 most significant SNPs in univariate analysis were considered as "influent" and others as "non influent". We randomly permutated all "non influent" SNPs, using the same permutation for all SNPs in order to keep the correlation structure between SNPs. "Influent" genes were not permuted in order to keep their relation with the dependent variable. This sample set was used to test the selection of SNPs with the lasso [2] and stability selection method [3] with 5 SNPs codings: additive, dominant, recessive and heterozygote models and finally the group-lasso method [4] that allows to code SNPs as dummy variables.

## 3 Results

With a threshold of 0.6 in stability selection, heterozygote and group-lasso models permitted to select more "influent" SNPs than other models, including the additive model: heterozygote and group-lasso selected 7/12 SNPs whereas additive model selected only 3/12. All models failed to select all "influent" SNPs and to avoid false positives. This might be related to the simulation framework using real data: "influent" SNPs might be only slightly related to the dependent variable or correlated to other SNPs. Our study highlighted that the additive genetic model can fail to select the real variables and that more interest should be given to the research and use of statistical models allowing qualitative variables, especially group methods in the field of sparse regression methods. Yet, our study needs to be replicated on another public data set with more strong association of SNPs and the dependent variable. Sparse-PLS regression methods need also to be tested since they might better take correlation between SNPs into account.

## References

[1] Gower-Rousseau C., Vasseur F., Fumery M., Savoye G., Salleron J., Dauchet L., Turck D., Cortot A., Peyrin-Biroulet L., Colombel J.F. Epidemiology of inflammatory bowel diseases: new insights from a French population-based registry (EPIMAD). Dig Liver Dis. 2013 Feb;45(2):89-94.

[2] Tibshirani R. (1996). Regression shrinkage and selection via the lasso. *J. Royal. Statist. Soc B.*, Vol. 58, No. 1, pages 267-288.

[3] Meinshausen N. and Bühlmann P. (2010). Stability selection (with discussion). *J. Royal. Statist. Soc B*, 72, 417-473.

[4] Yuan M. Model selection and estimation in regression with grouped variables. *J. Royal. Statist. Soc B.*, Vol. 68, No. 1, pages 49-67.
Ultra High throughput, single molecule mapping of replicating DNA

Nikita MENEZES BRAGANCA<fnref n="1" />, Francesco DE CARLI<fnref n="1" />, Wahiba BERRABAH<fnref n="2" />, Valérie BARBE<fnref n="2" />, Auguste GENOVESIO<fnref n="1" /> and Olivier HYRIEN<fnref n="1" />

Corresponding Author: menezes@biologie.ens.fr

Visualization and mapping of DNA molecules at single molecule level genome-wide is
possible thanks to a new device based on microfluidics developed by BionanoGenomics. DNA
fibers stained with a fluorescent intercalator, YOYO-1, and labeled by incorporation of fluorescent
nucleotides using a nicking endonuclease creating a restriction map (barcode), are stretched in
nanochannel arrays and imaged automatically. We typically collect, in a single run, over 32 000
images and more than 15 000 Mbp of DNA in the form of >100 Kbp long DNA molecules. This
device, made for finding structural variations in a given genome and for de novo assembly, is now
being used for the study of replication origins.

From each of the images the DNA molecule intensity profile is extracted after having achieved
preprocessing and registration steps on the raw images. Based on the analysis of these one
dimension profiles, we would like to know if there is a correlation between tracts of higher intensity
(doubling) and replication bubbles that has been observed in previous studies performed in our lab.
To do so, adaptation of the provided proprietary softwares and new tool development are required.
One of the tools that I have already implemented, enables us to visually check the DNA molecule
detection and the optical mapping performed and see where origins of replication are fired molecule
by molecule genome wide. I will be presenting the current effort I put to automatically analyze the
thousands of DNA molecules in order to validate our observations regarding this doubling of
intensity.

References

[1] Hyrien, O. Peaks cloaked in the mist: The landscape of mammalian replication origins. *Journal of Cell Biology, **208***, 147–160. doi:10.1083/jcb.201407004, 2015

[2] Hyrien, O. *The Initiation of DNA Replication in Eukaryotes*, D Kaplan, Ch. 4, Springer, 2016

[3] Hyrien, O. and al. From simple bacterial and archaeal replicons to replication N/U-domains. *Journal of Molecular Biology*. doi:10.1016/j.jmb.2013.09.021, 2013

[4] Michalet, X. Dynamic Molecular Combing: Stretching the Whole Human Genome for High-Resolution Studies. *Science*, 277(5331), 1518–1523. doi:10.1126/science.277.5331.1518, 1997

[5] De Carli, F., Gaggioli, V., Millot, G. A., & Hyrien, O. Single-molecule, antibody-free fluorescent visualisation of replication tracts along barcoded DNA molecules. *The International Journal of Developmental Biology*, (May), 1–9. doi:10.1387/ijdb.160139oh, 2015

<footnote n="1">¹ Institut de Biologie de l'Ecole Normale Supérieure (IBENS), 46 rue d'Ulm, 75005 Paris,
France</footnote>

<footnote n="2">² Génoscope, 2 rue Gaston Crémieux, 91057 Evry, France</footnote>
# Data updates on Norine, the reference Non-Ribosomal Peptide knowledge base

Yoann DUFRESNE<fnref n="1" />, Juraj MICHALIK<fnref n="1" />, Areski FLISSI<fnref n="1" />, Valerie LECLÈRE<fnref n="1" />,<fnref n="2" /> and Maude PUPIN<fnref n="1" />

Corresponding author: yoann.dufresne0@gmail.com

## 1 The Norine database

Norine, first released in 2006 [1], remains the unique platform dedicated to computational analysis of non-ribosomal peptides (NRPs). Among others, NRPs can act as antibiotics, siderophores, surfactants or protease inhibitors. The Norine database is the reference NRP knowledge base, containing more than 1200 peptides composed of almost 530 different monomers (various building blocks including amino acids). Each referenced NRP have a dedicated web page with various informations, including the most important, their composition and their biological activities. The monomeric representation, correspond to the nearest representation of the NRP assembly process. The other representation is the atomic representation, obtained by reconstruction after a mass spectrum analysis. The knowledge of the monomeric representation allow to understand the synthesis pathway. It has also been proved [2] that, the activity of the molecule can be predicted from this representation.

## 2 Improving the data quality and quantity

We developed a tool called smiles2monomers (s2m) [3] that automatically creates NRP annotations. From a SMILES [4], s2m infers the monomeric structure of the NRP. In Norine, a significant amount of NRP entries (around 30%) are annotated with both structures. We used s2m on the atomic structures to verify the integrity of the data and we found a few errors (50 NRPs with a wrong atomic or monomeric structure). To avoid the insertion of new errors, we included the s2m software in the crowdsourcing tool MyNorine.

We identified 3 main databases that could be sources of new NRPs for Norine: MIBiG [5] (stores gene clusters of secondary metabolites), BIRD [6] (Centralisation of external resources about "interesting" molecules), StreptomeDB [7] (molecules produced by bacteria in the *Streptomyces* genus). The Norine database is well known for the quality of its manual annotations. So, we did not want to add wrong informations from an automatic filling of the database. For this reason, we created a strict validation pipeline for the potential new entries. After the filtering process, we found 472 NRPs unreferenced in Norine: 235 from MIBiG, 162 from BIRD and 75 from StreptomeDB. Those data represent an increase of 30% of the entries in the Norine database.

## 3 Conclusion

In this poster we present an update of the data from the knowledge base Norine. Using tools like smiles2monomer we detected a few errors in the annotations. We corrected them and created safeguards to avoid errors in future user submissions. In a second time, we used several tools to retrieve and filter many possible new NRP entries in the database. The work on automatic filing scripts led us to a data increase of 30%. So, in the coming release of Norine we strongly improve the data quantity and quality available for all.

## References

[1] Ségolène Caboche, Maude Pupin, Valérie Leclère, Arnaud Fontaine, Philippe Jacques, and Gregory Kucherov. NORINE: a database of nonribosomal peptides. 36:D326-D331.

[2] Ammar Abdo, Ségolène Caboche, Valérie Leclère, Philippe Jacques, and Maude Pupin. A new fingerprint to predict nonribosomal peptides activity. 26(10):1187-1194.

[3] Yoann Dufresne, Laurent Noé, Valérie Leclère, and Maude Pupin. Smiles2monomers: a link between chemical and biological structures for polymers. 7:62.

[4] David Weininger. SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules. 28(1):31-36.

[5] MIBiG: Minimum information about a biosynthetic gene cluster.

[6] Helen Berman, Kim Henrick, and Haruki Nakamura. Announcing the worldwide protein data bank. 10(12):980-980.

[7] Xavier Lucas, Christian Senger, Anika Erxleben, Björn A. Grüning, Kersten Döring, Johannes Mosch, Stephan Fleming, and Stefan Günther. StreptomeDB: a resource for natural compounds isolated from streptomyces species. 41:D1130-D1136.

<footnote n="1">¹ Équipe Bonsai, Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRISTAL - Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France</footnote>

<footnote n="2">² Équipe ProBioGEM, Univ. Lille, INRA, ISA, Univ. Artois, Univ. Littoral Côte d'Opale, EA 7394 - ICV - Institut Charles Viollette, F-59000 Lille, France</footnote>
# NRPro: a Bioinformatics Tool for Nonribosomal Peptides Identification by Tandem Mass Spectrometry

Emma RICART<fnref n="1" />, Mickael CHEVALIER<fnref n="2" />, Maude PUPIN<fnref n="3" />,<fnref n="4" />, Valerie LECLERE<fnref n="2" />, Christophe FLAHAUT<fnref n="2" /> and Frederique LISACEK<fnref n="1" />

Corresponding Author: emma.ricart@sib.swiss

Nonribosomal Peptides (NRPs) are natural compounds enzymatically synthetized by microorganisms such as bacteria and fungi. These peptides have shown a wide range of biological properties such as antibiotics, antitumor or immunosuppressant, being of great importance to the pharmacological and agricultural industries. Due to its high sensitivity and accuracy, Mass Spectrometry (MS) is crucial for the identification of these biomolecules. However, the unusual chemical structures of NRPs (cyclic, polycyclic, branched...) and the presence of highly modified non-proteogenic amino-acids complicate the interpretation of the MS/MS spectra. Tools for the identification of some simple NRPs already exist, but they do not cover all NRP specificities, lack flexibility, efficient scoring and statistical validation as well as user friendliness. Here we present a new bioinformatics tool to match predicted MS/MS spectra against their experimental counterparts, either exactly or in a modification tolerant way.

Norine, a database entirely dedicated to non-ribosomal peptides [1], is used to retrieve the molecular structure of the NRPs. Based on this information we have developed a fragmentation model to calculate the MS/MS fragments of each peptide and predict their theoretical spectrum. The model covers all the structures observed in NRPs (cyclic, multicyclic and branched) and includes the 500+ non-proteogenic monomers. All the known fragmentation characteristics of NRPs have been included and multiple ring breakages are taken into account in order to calculate the putative fragment masses. Additionally, a combinatorics algorithm has been developed in order to allow modification and adduct tolerant searches. Once a spectrum-peptide match (PSM) is confidently identified, it can be added to a spectral library with its corresponding annotations.

Our software is presented as a web application developed in Javascript, CSS and HTML for the client side and Java for the server side. It provides a highly interactive interface and it is able to perform a configurable and complete computational fragmentation of NRPs, including those presenting complex structures containing multicycles and several branches. Preliminary tests with experimental MS/MS data show positive results: the tool is able to match all the high intensity peaks. Furthermore, this is the first NRP fragmentation tool that includes modification tolerant searches, which will be very useful for the identification of new peptides.

## Acknowledgements

We would like to thank the Swiss Institute of Bioinformatics for providing financial support for this project, Yoann Dufresne for his advises regarding the usage of his bioinformatics tool [2] and Markus Müller for sharing his expertise on mass spectrometry and MzJava [3].

## References

[1] Flissi Areski, Dufresne Yoann, Michalik Juraj, Tonton Laurie, Janot Stephane, Noe Laurent, Jacques Philippe, Leclere Valerie and Pupin Maude: Norine, the knowledgebase dedicated to non-ribosomal peptides, is now open to crowdsourcing. In *Nucleic Acids Research* 44(D1), pages D1113–D1118. Oxford Academic, 2016.

[2] Dufresne Yoann, Noe Laurent, Leclere Valerie and Pupin Maude: Smiles2Monomers: a link between chemical and biological structures for polymers. In *Journal of cheminformatics* 7(1), page 62. Springer, 2015.

[3] Horlacher Oliver, Nikitin Frederic, Alocci Davide, Mariethoz Julien, Müller Markus and Lisacek Frederique: MzJava: An open source library for mass spectrometry data processing. In *Journal of Proteomics* 129, pages 63–70. Elsevier, 2015.

<footnote n="1">¹ Proteome Informatics Group, Swiss institute of Bioinformatics and University of Geneva, CUI-7 Route de Drize, CH-1211, Geneva, Switzerland</footnote>

<footnote n="2">² University of Lille, INRA, ISA, University of Artois, Univ. Littoral Côte d'Opale, EA 7394-ICV- Institut Charles Viollette, F-59000 Lille, France</footnote>

<footnote n="3">³ University of Lille, CNRS, Centrale Lille, UMR 9189-CRISTAL- Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France</footnote>

<footnote n="4">⁴ Inria-Lille Nord Europe, Bonsai team, F-59655 Villeneuve d'Ascq Cedex, France</footnote>
# Multi-scale characterization of symbiont diversity in the pea aphid complex through metagenomic approaches

Authors : Cervin Guyomar<fnref n="1" />,<fnref n="2" />, Fabrice Legeai<fnref n="1" />,<fnref n="2" />, Christophe Mougel<fnref n="1" />, Claire Lemaitre<fnref n="2" />, Jean-Christophe Simon<fnref n="1" />

Most metazoans are involved in durable symbiotic relationships with microbes which can take several forms, from mutualism to parasitism. The advances of NGS technologies and bioinformatics tools have opened new opportunities to shed light on this hidden but very influential diversity. The pea aphid is a model insect system for symbiont studies. It harbors both an obligatory symbiont supplying key nutrients and several facultative symbionts bringing some novel functions to the host, such as protection against natural enemies and thermal stress. The pea aphid is organized in a complex of biotypes, each adapted to a specific host plant of the legume family and having its own symbiont composition. Yet, the metagenomic diversity of the biotype-associated symbiotic community is still largely unknown. In particular, little is known on how the symbiotic genomic diversity is structured at different scales: across host biotypes, amongst individuals of the same biotype, or within individual aphids.

We used high throughput whole genome metagenomic sequencing to characterize with a fine resolution the metagenomic diversity of both individual resequenced aphids and biotype specific pooled aphids. By a reference genome mapping approach, we first assessed the taxonomic diversity of the samples and built symbiont specific read sets. We then performed a genome-wide SNP-calling, to examine the differences in bacterial strains between samples. Our results revealed different diversity patterns at the three considered scales for the pea aphid symbionts. At the inter-biotype and intra-biotype scales, the primary symbiont *Buchnera* and some secondary symbionts such as *Serratia* showed a biotype specific diversity. We showed evidence for horizontal transfer of a *Hamiltonella* strain between biotypes, and found two distinct strains of *Regiella* symbionts within some biotypes. At the finest intra-host diversity scale, we also showed that these two strains of *Regiella* may coexist inside the same aphid host. This study highlights the huge potential of bioinformatics analyses of metagenomic dataset in exploring microbiome diversity in relation with host variation.

Keywords: metagenomics, aphids, diversity, symbionts

<footnote n="1">1 : INRA, UMR 1349 INRA/Agrocampus Ouest/Université Rennes 1, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Le Rheu, France</footnote>

<footnote n="2">2 : INRIA/IRISA/GenScale, Campus de Beaulieu, Rennes, France</footnote>
iMOMi: a database dedicated to integration and exploration of multi-omic data.

Nicolas PONS<fnref n="1" />, Jean-Michel BATTO<fnref n="2" />, Amine GHOZLANE<fnref n="3" />, Kevin WEISZER<fnref n="1" />, Pierre LEONARD<fnref n="1" />, Emmanuelle LE CHATELIER<fnref n="1" />, Pierre RENAULT<fnref n="2" /> and S. Dusko EHRLICH<fnref n="1" />

Corresponding Author: nicolas.pons@inra.fr

With the explosion of data produced especially in the human metagenomic domain, numerous heterogeneous sources of data are available to the scientific community: metagenomic/metatranscriptomic reads, metagenome assemblies, reference gene catalogues, gene abundance profiles, sample metadata etc. In order to explore fully various facets of human microbiota, biologists need to access and browse integrated databases connecting these data. In this context, we developed an extension for iMOMi (integrative MultiOmic Mining relational database) in order to unify and centralize metagenomic reference gene catalogues with their accompanying data and provide a common datasource to biologists.

The iMOMi design is a modular database related to annotations data (Genbank, EMBL, COG/eggNOG, KEGG...) and *in silico* analysis (such as orthologous classification or regulatory motifs delineation) [1]. Current version of iMOMi is a relational database centered on the integration and exploration of (meta)genomic data. The database contains more than 150 tables with specific rules: annotation, phylogeny, taxonomy, metabolism, gene expression regulation, protein structure. iMOMi is capable to integrate genome data but also metagenomic data in particular large reference gene catalogues used for building abundance gene profiles in quantitative metagenomic studies. Furthermore, we added a powerful extension to handle (i) the concept of MetaGenomic Species; (ii) the relation between reference genes and their metagenome source and (iii) the gateways between several reference gene catalogues.

We developed user-friendly tools, for example iMOMi Studio which (i) facilitates the importation of new data (direct download of genomes from the NCBI, functional annotation of metagenome...) and (ii) allows a certain data browsing, in particular for metagenomic data: functional potential projection onto iPath2 [2] visualization for a genome/metagenome/MGS or taxonomic distribution with KRONA representation [3] for examples.

Furthermore, to help querying multiple tables, we developed a dedicated API (Application Programming Interface) based on PL/SQL stored procedures that can be called into any programming language as long as they can import SQL interface (Delphi, Python, Ruby, R...). The API contains functions for data integration, MGS content extraction, taxonomic distribution, functional extraction...

Finally, with the multiplication of reference gene catalogues increasingly reconstructed specifically for a quantitative metagenomic study, it becomes complicated for an investigator to cross the results of different studies. In this context, the implementation of iMOMi seeks to address this issue (i) by integrating numerous catalogues, (ii) by unifying their annotations (functional and taxonomic) and (iii) above all by bridging them. iMOMi have been optimized and is currently capable to handle dozen of catalogues representing hundreds of millions of features.

The client software iMOMi Studio is available at http://mgps.eu/index.php?id=ibs-tools

References

[1] Pons N, Batto JM, Ehrlich SD, Renault P. Development of software facilities to characterize regulatory binding motifs and application to Streptococcaceae. *J Mol Microbiol Biotechnol.* 14(1-3):67-73, 2008.

[2] Yamada T1, Letunic I, Okuda S, Kanehisa M, Bork P. iPath2.0: interactive pathway explorer. *Nucleic Acids Res.* Jul;39(Web Server issue):W412-5. 2011.

[3] Ondov BD1, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a Web browser. *BMC Bioinformatics.* 12:385. 2011.

<footnote n="1">¹ METAGENOPOLIS, INRA Université Paris-Saclay, 78350, Jouy-en-Josas, France</footnote>

<footnote n="2">² MICALIS, INRA Université Paris-Saclay, 78350, Jouy-en-Josas, France</footnote>

<footnote n="3">³ Bioinformatics and Biostatistics Hub, Institut Pasteur, 75015, Paris France</footnote>
# Sequence composition-based read binning and taxonomic profiling in infectious metagenomics diversity analyses

Mathias VANDENBOGAERT, Charlotte BALIÈRE and Valérie CARO

Pole for Genotyping of Pathogens, Laboratory for Urgent Response to Biological
Threats, Environment and Infectious Risks Research and Expertise Unit, Institut
Pasteur, 25-28 rue du docteur Roux, F-75724 Paris cedex 15, France

Corresponding Author: mathias.vandenbogaert@pasteur.fr

## 1 Introduction/Context

High-Throughput Sequencing (HTS) has gained in throughput and cost-efficiency, strongly affecting public health and biomedical research and enable to conduct large scale genomic projects. In infectious metagenomics studies, it is critical to detect rapidly and sensibly potential life-threatening pathogens. Our objective is to characterize the bacterial and viral composition inherent to clinical samples. Optimized methodologies for fast and accurate recovery of the known microbial biodiversity, followed with phylogenetic approaches to study the remaining ("dark matter", "BLAST-resistant") species are required to drive thorough sample comparisons in order to pinpoint both common and sample-specific infectious agents.

## 2 Discerning discriminative reads in complex metagenomics datasets

Clinical samples often contain either a number of (meta-) genomes that are at best divergently related to known references, or a limited number of genomes with very low coverage. Meta-genomic samples represent a large number of reads, rendering assembly without pre-condition computationally inefficient, and often proves to result in under-assembly and chimeric contigs. Variable levels of similarity challenges identification in diagnostics settings, where the distinction between presence and absence of single species or relative abundance levels are of eminent importance [1]. Sequence alignment through mapping proves difficult in such cases, and "genome finishing" often turns out to be impossible. To overcome the limitations of alignment-based comparisons, ultrafast alignment-free methods are exploited [2-4], providing a powerful comparison strategy to distinguish different organisms present in meta-genomics HTS read datasets.

We emphasize on clustering of sequencing reads, based on k-mer counts, as a preliminary and valuable step prior to assembly, as reads belonging to possibly different species show different k-mer compositions. Results indicate that, as alignment-free methods relying on clustering of word enumerations are obviously less accurate than direct sequence alignments, they should only be used when direct alignment is either impossible (due the high level of meta-genomic divergence) or computationally too complex. In addition, to be able to more thoroughly distinguish pathogen from host, the size of k has to be optimized as a function of the length of the reads. The length of the reads is indeed steadily increasing, with the different sequencing technologies at hand, i.e. the Illumina HiSeq/MiSeq technology generates 100 to 250 bp reads, whereas Life Technology's Ion Torrent PGM/Proton systems generate reads up to 300, 400 bp and above.

## 3 Taxonomic profiling and comparison of infectious metagenomics samples

As an extension, comparison of samples has the potential to recover the shared background diversity, and can shed light onto the microbiotic identity of samples, easing the pathogen identification task. K-mer frequencies are used either for taxonomic binning of individual reads or for computing the overall composition. K-mer distributions of a set of metagenomic samples give an indication of the presence, abundance and evolutionary relatedness of novel organisms present in the samples.

## References

[1] Lindner MS and Renard BY. Metagenomic abundance estimation and diagnostic testing on species level. Nucl. Acids Res. (7 January 2013) 41 (1): e10.

[2] Vinga S and Almeida J. Alignment-free sequence comparison-a review. Bioinformatics 2003, 19(4):513-523.

[3] Song K, Ren J, Zhai Z, Liu X, Deng M and Sun F. Alignment-free sequence comparison based on next-generation sequencing reads. J Comput Biol 2013, 20(2):64-79.

[4] Trifonov V and Rabadan R. Frequency analysis techniques for identification of viral genetic data. MBio 2010, 1(3).
# REGOVAR, logiciel libre pour l'analyse de données de séquençage haut débit pour les maladies génétiques rares

Anne-Sophie DENOMMÉ-PICHON<fnref n="1" />, Olivier GUEUDELOT<fnref n="2" />, Jérémie ROQUET³,
June SALLOU<fnref n="4" />, Sacha SCHUTZ<fnref n="5" />, David GOUDENÈGE<fnref n="2" />

Corresponding Author: asdp@regovar.org

## 1 Présentation

REGOVAR est un projet collaboratif, libre et ouvert de logiciel d'analyse de données de séquençage haut débit avec une interface graphique simple et conviviale pour les panels de gènes, l'exome et le génome (DPNI, recherche de SNV, CNV, SV...). Le projet est financé dans le cadre d'un PHRCI du Grand Ouest (Angers, Brest, Nantes, Poitiers, Rennes et Tours) pour structurer les généticiens cliniciens, biologistes et bioinformaticiens impliqués dans le diagnostic moléculaire des maladies génétiques rares. Si la bioinformatique médicale appliquée au NGS permet aujourd'hui d'analyser avec succès un grand nombre de données au sein d'un CHU ou d'une région, elle souffre d'un manque de coordination à l'échelle nationale. REGOVAR vise à impliquer et fédérer les différentes communautés concernées, sans limites institutionnelles ou géographiques. Il se base exclusivement sur des technologies et des logiciels libres et gratuits, éliminant toute contrainte contractuelle et budgétaire.

## 2 Objectifs

REGOVAR est un logiciel permettant le traitement de données génétiques, depuis la récupération des fichiers produits par les séquenceurs, quelle qu'en soit la technologie, jusqu'à la génération de rapports illustrés et de comptes-rendus d'analyse en passant par les contrôles de qualité, la détection, l'annotation, le filtrage, la priorisation et la visualisation de variants. Son architecture client-serveur permet une utilisation depuis des ordinateurs de bureau sous Windows, Linux et macOS, via une interface graphique claire et intuitive conçue pour permettre l'analyse des données par des généticiens n'ayant pas de compétence spécifique en bioinformatique (filtres de variants enregistrables, simplification de la bioanalyse...). Sa conception modulaire permet d'intégrer dynamiquement de nouveaux pipelines, quelles que soient leurs dépendances, qui peuvent être partagés au sein de la communauté. Ces échanges de pipelines permettront à terme l'harmonisation des bonnes pratiques avec des pipelines unifiés nationalement et validés par l'ANPGM. REGOVAR intègre une base de données principale dimensionnée pour supporter aussi bien l'analyse de panels, d'exomes, que de génomes complets. Cette base est enrichie de données publiques telles que celles provenant de gnomAD et dbNSFP, ainsi que de données locales. Des échanges anonymisés de certaines informations recueillies sont possibles. REGOVAR permet une utilisation aussi bien en recherche qu'en diagnostic.

## 3 Appel à collaboration

REGOVAR a déjà suscité l'intérêt d'autres acteurs comme les laboratoires de génétique des CHU de Nancy et de Montpellier. Le projet est ouvert à toute personne souhaitant apporter sa contribution : idées, intégration de pipeline, développement, test, documentation... Informations disponibles sur https://regovar.org/.

### Abréviations

ANPGM : Association Nationale des Praticiens de Génétique Moléculaire

DPNI : Dépistage Prénatal Non Invasif

PHRCI : Projet Hospitalier de Recherche Clinique Interrégional

<footnote n="1">¹ Laboratoire de génétique - UMR INSERM 954, CHRU de Nancy, 54500 Vandœuvre-lès-Nancy, France</footnote>

<footnote n="2">² Laboratoire de génétique - UMR CNRS 6214-INSERM 1083, CHU d'Angers, 49933 Angers, France
³ dnalynize.me, 75013 Paris, France</footnote>

<footnote n="4">⁴ Univ. Bordeaux, Centre de BioInformatique de Bordeaux (CBiB), 33000 Bordeaux, France</footnote>

<footnote n="5">⁵ Laboratoire de génétique, CHRU de Brest, 29609 Brest, France</footnote>
# Alignement à grande échelle pour une approche métagénomique dans le cadre du projet Tara Oceans

Artem KOURLAIEV<fnref n="1" />, Corinne DA SILVA<fnref n="1" />, Stefan ENGELEN<fnref n="1" />, Alexis BERTRAND<fnref n="1" />, Aimeric BRUNO<fnref n="1" />, Eric PELLETIER<fnref n="2" />, Patrick WINCKER<fnref n="2" /> and Jean-Marc AURY<fnref n="1" />

Corresponding Author: artem.kourlaiev@cea.fr

Le projet *Tara Oceans* (2009-2013) a pour objectif d'étudier globalement les écosystèmes planctoniques marins (des virus aux métazoaires) [1]. Des échantillons d'eau ont été prélevés puis filtrés sur 88 stations couvrant l'ensemble des océans [2]. Le séquençage de l'ADN et de l'ARN présents dans ces échantillons a été effectué au Genoscope. Ce projet constitue le plus grand effort de séquençage jamais réalisé pour des organismes marins.

La métagénomique est une méthode permettant d'étudier l'ensemble des génomes (et des transcriptomes pour la métatranscriptomique) des populations de micro-organismes d'un écosystème donné à partir d'un échantillon environnemental. C'est l'une des approches choisies pour l'étude de la biodiversité des océans dans le cadre du projet *Tara Oceans*.

Pour l'étude des eucaryotes présents dans ces échantillons, un catalogue de gènes a été constitué à partir des données métatranscriptomiques. Plus de 116 millions de gènes ont été identifiés. Afin de déterminer les niveaux d'abondance et d'expression de chacun de ces gènes à chaque station de prélèvement, il s'est révélé nécessaire d'aligner l'ensemble des lectures métagénomiques et métatranscriptomiques sur cette référence. Ceci apporte un premier niveau de valorisation aux données et offre des nouvelles voies d'analyses.

La volumétrie du catalogue de gènes et de lectures à aligner (plus de 460 milliards) a nécessité la mise en place d'une méthode d'alignement massive, en plusieurs étapes : dans un premier temps, la référence a été fragmentée et l'ensemble des échantillons ont été alignés sur chacun des fragments. Ensuite, des références de plus petite taille (contenants uniquement les gènes avec au moins un match obtenu lors du premier alignement) ont été constituées pour chaque échantillon. Enfin, un second alignement du même ensemble d'échantillons a été effectué sur ces références réduites.

Grâce à une conception massivement parallèle et à l'utilisation des moyens de calculs du TGCC-CCRT [3], des dizaines de milliers de cœurs ont pu être utilisés en parallèle pour diminuer le temps de restitution. Nous avons ainsi pu obtenir l'ensemble des résultats en une semaine. La méthode mise en place est extensible, et pourra être utilisée pour d'autres projets de métagénomique.

## Références

<footnote n="1">¹ Commissariat à l'Energie Atomique (CEA), Institut François Jacob, Genoscope, 91000 Evry, France.</footnote>

<footnote n="2">² CEA / Genoscope, CNRS UMR 8030, Université d'Evry, France.</footnote>

<footnote>[1] Karsenti, E., S. G. Acinas, et al. A holistic approach to marine eco-systems biology. *PLoS Biol* 9(10): e1001177, 2011.</footnote>

<footnote>[2] Pesant, S. et al. Open science resources for the discovery and analysis of *Tara Oceans* data. *Sci. Data* 2, 150023 (2015).</footnote>

<footnote>[3] Très Grand Centre de Calcul du CEA, http://www-hpc.cea.fr/fr/complexe/tgcc.htm</footnote>
Seqenv: linking sequences to environments through text mining

Lucas Sinclair¹,†, Umer Z Ijaz²,†, Lars Juhl Jensen³, Marco Coolen⁴, Cecile Gubry-Rangin⁵, Alica Chrokov⁶, Anastasia Oulas⁷,⁸, Christina Pavloudi⁷, Julia Schnetzer⁹, Aaron Weimann¹⁰, Ali Zeeshan Ijaz¹¹, Alexander Eiler¹, Christopher Quince¹²,*, Evangelos Pafilis⁷,\*.

1 Department of Ecology and Genetics, Limnology, Uppsala University, Sweden

2 Infrastructure and Environment Research Division, School of Engineering, University of
Glasgow, United-Kingdom

3 The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark

4 Western Australia Organic and Isotope Geochemistry Centre (WA-OIGC), Department of Chemistry, Bentley Campus, Curtin University, Australia

5 Institute of Biological & Environmental Sciences, University of Aberdeen, Scotland,
United Kingdom

6 Biology Centre of the Czech Academy of Sciences, Institute of Soil Biology, Czech Republic

7 Institute of Marine Biology Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion Crete, Greece

8 Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
9 Max Planck Institute for Marine Microbiology, Department of Molecular Ecology, Microbial Genomics and Bioinformatics Group, Bremen, Germany

10 Computational Biology of Infection Research, Helmholtz Centre for Infection Research,
Braunschweig, Germany

11 Hawkesbury Institute for the Environment, Western Sydney University, Hawkesbury, Australia

12 Warwick Medical School, University of Warwick, Coventry, United-Kingdom
† These authors contributed jointly to this work.

* Corresponding authors: C. Quince, c.quince@warwick.ac.uk

* Address: Warwick Medical School, University of Warwick, Coventry CV4 7AL, U.K.

* Corresponding authors: E. Pafilis, pafilis@hcmr.gr

* Address: Institute of Marine Biology Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O. Box 2214, Heraklion, 71003, Crete, Greece.

Corresponding author: c.quince@warwick.ac.uk

# 1 Abstract

Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes, leading to the production of individual unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, *seqenv*, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts – if it is available – the *isolation source* textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of *seqenv* to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install *seqenv*, go to: https://github.com/xapple/seqenv.
# Predicting the ecological quality status of marine environments from eDNA metabarcoding data using supervised machine learning

Tristan CORDIER<fnref n="1" />, Philippe ESLING<fnref n="2" />, Franck LEJZEROWICZ<fnref n="1" />, Joana VISCO<fnref n="3" />, Amine OUADAHI<fnref n="1" />, Catarina MARTINS<fnref n="4" />, Tomas CEDHAGEN<fnref n="5" /> and Jan PAWLOWSKI<fnref n="1" />,<fnref n="3" />

Corresponding Author: tristan.cordier@unige.ch

## Abstract

Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environment. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators. We found similar ecological status as obtained from morphology-based macrofaunal inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring.

<footnote n="1">¹ Department of Genetics and Evolution, University of Geneva, Boulevard d'Yvoy 4, CH 1205 Geneva, Switzerland</footnote>

<footnote n="2">² IRCAM, UMR 9912, Université Pierre et Marie Curie, 4 place Jussieu, 75005 Paris, France</footnote>

<footnote n="3">³ ID-Gene ecodiagnostics, Ltd, chemin des Aulx 14, 1228 Plan-les-Ouates, Switzerland</footnote>

<footnote n="4">⁴ Marine Harvest ASA, Sandviksboder 77AB, Bergen, 5035 Bergen, Norway</footnote>

<footnote n="5">⁵ Department of Bioscience, Section of Aquatic Biology, University of Aarhus, Nordre Ringgade 1, 8000 Aarhus, Denmark</footnote>
Extended HLA haplotypes in Membranous Nephropathy

LOUIS ÉDOUARD CHAVUIÈRE¹, PIERRE RONCO¹ and HANNA DEBIEC¹

¹ UMR-S 1155, Hôpital Tenon, 4 rue de la Chine, Bât. Recherche, 75020 PARIS

Corresponding Author: louis.chavuiere@inserm.fr

Membranous Nephropathy (MN) is an Auto-immune and multifactorial disease caused by the production of PLA2R1 antigens in podocytes [1] (specialized cells for filtration in kidney), resulting in the activation of the immune response and podocyte destruction. Recently, using genomics studies, risk variants were found on PLA2R1 gene [2] and HLA-II cluster, namely on genes coding for the antigen implicated in MN and for proteins implicated in antigen presentation.

Data analysis was focused on the three HLA regions using Whole Exome Sequencing data from 123 patients. The HLA-II risk haplotype in Caucasian European population [3] was first confirmed as HLA-DRB1*03:01, HLA-DQA1*05:01, HLA-DQB1*02:01 (known as DR3-DQ2 haplotype). This haplotype minor allele frequency is 0.29 in the MN cohort versus 0.09 in the control Caucasian European population.

The aim was to fully explore the entire HLA cluster and to find new MN risk factors. To search for epistatic variant interactions, we have used regression-based methods. This analysis demonstrated that the haplotype can be extended with HLA-I allelotypes: HLA-A*01:01, HLA-B*08:01, HLA-C*07:01 (A1-B8-DR3-DQ2). Between those two cluster, HLA-III groups numerous different types of genes that are important in the Immune Response as C2, C4A and C4B (complement components), CFB (Complement Factor B) or TNF.

In HLA-III region, the analysis was focused on paralogous C4A and C4B genes. Only five nucleotides on exon 26 are different between the two genes. Each one can possess an endogenous retrovirus (HERV-K(C4)) [4], located in intron 9, resulting in a longer protein (C4L). In addition, Copy Number Variations (CNVs) were found on each gene. CNVs were detected aligning C4A and C4B reads on C4A reference sequence. Then, the mean depth of coverage on exon 26 was calculated to establish C4 copy number. C4A and C4B were split using the proportion of variants that identify the two genes. An interaction was detected between the loss of C4A and the extended haplotype A1-B8-DR3-DQ2. Variants located in HLA-I genes as DDR1, VARS2, SFTA, TRIM26 or HCG17, interact with the loss of C4A and could be added to this extended haplotype.

The risk factor for MN is finally not only linked with DR3-DQ2 haplotype in HLA-II region, but with an extended haplotype covering all of the three HLA regions. The next step will be to explore intronic and intergenic regions, especially HERV-K(C4).

References

[1] Laurence H. Beck, Ramon G.B. Bonegio, Gérard Lambeau, David M. Beck, David W. Powell, Timothy D. Cummins, Jon B. Klein, David J. Salant. M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy. *N Engl J Med*, 361: 11–21, 2009

[2] Horia C. Stanescu, Mauricio Arcos-Burgos, Alan Medlar, Detlef Bockenhauer, Anna Kottgen, Liviu Dragomirescu, Catalin Voinescu, Naina Patel, Kerra Pearce, Mike Hubank, Henry A.F. Stephens, Valerie Laundy, Sandosh Padmanabhan, Anna Zawadzka, Julia M. Hofstra, Marieke J.H. Coenen, Martin den Heijer, Lambertus A.L.M. Kiemeney, Delphine Bacq-Daian, Benedicte Stengel, Stephen H. Powis, Paul Brenchley, John Feehally, Andrew J. Rees, Hanna Debiec, Jack F.M. Wetzels, Pierre Ronco, Peter W. Mathieson, Robert Kleta. Risk HLADQA1 and PLA(2)R1 alleles in idiopathic membranous nephropathy. *N Engl J Med*, 364:616–626, 2011

[3] Peggy Skula, Yong Li, Horia C. Stanescu, Matthias Wuttke, Arif B. Ekici, Detlef Bockenhauer, Gerd Walz, Stephen H. Powis, Jan T. Kielstein, Paul Brenchley, GCKD Investigators, Kai-Uwe Eckardt, Florian Kronenberg, Robert Kleta, Anna Köttgen. Genetic risk variants for membranous nephropathy: extension of and association with other chronic kidney disease aetiologies. *Nephrol Dial Transplant*, 32 (2): 325-332, 2017.

[4] Mike J. Mason, Cate Speake, Vivian H. Gersuk, Quynh-Anh Nguyen, Kimberly K. O'Brien, Jared M. Odegard, Jane H. Buckner, Carla J. Greenbaum, Damien Chaussabel and Gerald T. Nepom. Low HERV-K(C4) copy number is associated with Type 1 Diabetes. *Diabetes*, 63(5): 1789-1795, 2014.
RSAT matrix-clustering: dynamic exploration and redundancy reduction of
transcription factor binding motif collections

Jaime Abraham CASTRO-MONDRAGON¹, Sébastien JAEGER², Denis THIEFFRY³, Morgane THOMAS-CHOLLIER³,\* and Jacques VAN HELDEN¹,\*
¹Aix-Marseille Univ, Inserm, TAGC, Technological Advances for Genomics and Clinics, UMR_S 1090, Marseille, France.
²Aix Marseille Univ, CNRS, INSERM, CIML, Marseille, France
³IBENS, Département de Biologie, Ecole Normale Supérieure, CNRS, Inserm, PSL Research University, F-75005 Paris, France

¹ Aix-Marseille Univ, Inserm, TAGC, Technological Advances for Genomics and Clinics, UMR_S 1090, Marseille, France.

² Aix Marseille Univ, CNRS, INSERM, CIML, Marseille, France

³ IBENS, Département de Biologie, Ecole Normale Supérieure, CNRS, Inserm, PSL Research University, F-75005 Paris, France

\* Corresponding Authors: Jacques.van-Helden@univ-amu.fr , mthomas@biologie.ens.fr

Transcription Factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g., ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present *matrix-clustering* [1], a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feature unique to *matrix-clustering* is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that *matrix-clustering* considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ~11,000 motifs from 24 entire databases, showing that *matrix-clustering* correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. *matrix-clustering* is integrated within the RSAT suite (http://rsat.eu/) [2], accessible through a user-friendly web interface or command-line for its integration in pipelines.

Availability: http://rsat.eu/

Manuscript in press in Nucleic Acids Research.

Preprint accessible on bioRxiv: doi: https://doi.org/10.1101/065565

References

[1] Jaime A. Castro-Mondragon, Sébastien Jaeger, Denis Thieffry, Morgane Thomas-Chollier\# and Jacques van Helden\#, RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections. *Nucleic Acids Research*, in press.

[2] Medina-Rivera A\*, Defrance M\*, Sand O\*, Herrmann C, Castro-Mondragon J, Delerce J, Jaeger S, Blanchet C, Vincens P, Caron C, Staines DM, Contreras-Moreira B, Artufel M, Charbonnier – Khamvongsa L, Hernandez C, Thieffry D, Thomas-Chollier M\#, van Helden J\# (2015) RSAT 2015: Regulatory Sequence Analysis Tools. *Nucleic Acid Research* **43**: W50-6.
# Assemblage de novo de quasi-espèces virales basé sur un graphe de chevauchements

Jasmijn A. BAAIJENS<fnref n="1" />, Amal Zine EL AABIDINE<fnref n="2" />,<fnref n="3" />, Eric RIVALS<fnref n="2" />,<fnref n="3" />,† et Alexander SCHÖNHUTH†<fnref n="1" />,†

Auteur référent: rivals@lirmm.fr

† Ces auteurs ont des contributions égales et sont tous deux auteurs correspondants.

Durant une épidémie, les virus qui infectent des humains (ou d'autres espèces) doivent échapper au système immunitaire de l'hôte. Pour cela, leur génome mute rapidement et cela engendre une diversité des virus dont le comportement évolue – on parle alors de quasi-espèces virales. Grâce à ces mutations génomiques, certains de ces virus pourront mieux combattre le système immunitaire ou mieux résister à un traitement. On ne sait pas à l'avance quelles quasi-espèces s'adaptent le mieux à l'hôte, ni lesquelles deviennent majoritaires dans la population de virus d'un individu ; la fréquence relative des quasi-espèces est une inconnue importante du point de vue biologique et médical. Pour lutter contre les épidémies ou suivre les effets d'un traitement, on doit savoir quelles quasi-espèces acquièrent des mutations avantageuses, et il est donc crucial d'obtenir les séquences de leurs génomes et d'estimer leurs proportions dans la population virale d'un individu. On peut séquencer les génomes viraux présents chez l'hôte, grâce aux techniques de séquençage à haut débit qui produisent des millions de lectures courtes (reads en anglais). Il faut ensuite assembler ces fragments pour obtenir les séquences génomiques des quasi-espèces. Bien que de nombreux outils bioinformatiques d'assemblage de génome existent, ils sont inadaptés au cas des quasi-espèces pour lequel le logiciel doit deviner combien de quasi-espèces sont présentes, puis reconstruire partiellement ou entièrement le génome de chacune, afin d'identifier leurs différences. Les méthodes existantes reposent sur une séquence de référence du génome de l'espèce virale. Malheureusement, on dispose rarement d'un génome de référence, en particulier lors d'épidémie ou lors de l'apparition de nouveaux virus (par exemple dans le cas de zoonoses). Il faut donc considérer le cas dit de novo où aucune séquence de référence n'est disponible. Une des difficultés est d'assembler des génomes similaires tout en les distinguant (en séparant les quasi-espèces). Nous proposons une méthode nommée SAVAGE pour assembler les génomes de quasi-espèces virales lorsque l'on ne dispose pas déjà d'un génome de référence (le cas le plus difficile d'assemblage). SAVAGE se base sur un graphe de chevauchement (ou *overlap graph* en anglais) qu'il calcule grâce à un algorithme performant. Les tests sur des données de HIV, des virus de l'hépatite C, des virus de Zika et d'Ebola démontrent la capacité de SAVAGE à reconstruire les quasi-espèces et à estimer leur fréquence relative [1]. Ce travail offre des perspectives nouvelles pour le suivi des infections chez les patients par des approches basées sur le séquençage à haut débit.

SAVAGE est accessible à https://bitbucket.org/jbaaijens/savage.

## Remerciements

Les travaux de AS sont financés par Vidi grant 639.072.309, de la Netherlands Organisation for Scientific Research (NWO). Ceux de ER, AZ sont financés par l'Institut de Biologie Computationnelle (ANR-11-BINF-0002), France Génomique, et le Défi Mastodons C3G.

## Références

[1] Jasmijn A. Baaijens, Amal Zine El Aabidine, Eric Rivals, and Alexander Schönhuth. De novo assembly of viral quasispecies using overlap graphs. *Genome Research*, 27(5):835-848, 2017.

<footnote n="1">¹ Centrum Wiskunde & Informatica, Amsterdam, Netherlands</footnote>

<footnote n="2">² LIRMM, CNRS and Université de Montpellier, Montpellier, France</footnote>

<footnote n="3">³ Institut Biologie Computationnelle, CNRS and Université de Montpellier, France</footnote>
Caractérisation de données biologiques

Arthur CHAMBON¹, Tristan BOUREAU², Frédéric LARDEUX¹ and Frédéric SAUBION¹

¹ LERIA, Université d'Angers, France

² IRHS, Université d'Angers, France

Corresponding author: arthur.chambon@univ-angers.fr

L'analyse logique de données [1,2,3] constitue une alternative originale aux approches traditionnelles de classification de données issues de l'apprentissage artificiel. L'objectif principal de cette méthodologie consiste à se concentrer sur la justification explicite de la classification de données dans des groupes/classes. Considérons un ensemble Ω d'observations, dont chaque élément est représenté par des données appartenant à un ensemble d'attributs Booléens A. Cet ensemble d'observations étant divisé en deux groupes (groupe positif P et groupe négatif N), l'analyse logique de données (LAD) consiste à trouver des "patterns" (motifs) caractérisant un groupe, i.e., des expressions booléennes sur un sous-ensemble de A vérifiées par au moins une observation d'un groupe, et chez aucune observation de l'autre groupe.

Le problème de caractérisation multiple de données (MCP) [4,5] consiste également à caractériser les observations. Toutefois, il diffère de l'approche LAD sur différents points:

- Une solution du MCP sera non pas un pattern, mais un sous ensemble d'attributs.

- Une instance du MCP peut contenir plus de deux groupes. Une solution au MCP caractérisera tous les groupes simultanément.

Il s'agit donc de déterminer un ensemble d'attributs, caractérisant chacun des groupes.

Nous pouvons ainsi représenter ces données par une matrice telle que :

- Chaque ligne *i* représente les observations.

- Chaque colonne *j* représente les attributs.

- La valeur $x_{ij}$ vaut 1 si l'attribut *j* est présent chez l'observation *i*, 0 sinon.

<table><thead><tr><th rowspan="2">Observations</th><th rowspan="2">Groupes</th><th colspan="5">Attributs</th></tr><tr><th>a</th><th>b</th><th>c</th><th>d</th><th>e</th></tr></thead><tbody><tr><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td></tr><tr><td>3</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>4</td><td>2</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>5</td><td>2</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td></tr><tr><td>6</td><td>3</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td></tr></tbody></table>

Nous déterminons l'ensemble des solutions optimisant deux critères :

- Minimiser le nombre d'attributs dans les solutions afin de déterminer le groupe auquel appartiennent les observations à moindre coût.

- Maximiser la similarité des observations de même groupe afin de mieux analyser les différents groupes.

References

[1] Gabriela Alexe, Sorin Alexe, Tibérius O. Bonates, and Alexander Kogan. Logical analysis of data - the vision of peter I. hammer. *Ann. Math. Artif. Intell.*, 49(1-4):265-312, 2007.

[2] Endre Boros, Yves Crama, Peter L. Hammer, Toshihide Ibaraki, Alexander Kogan, and Kazuhisa Makino. Logical analysis of data: classification with justification. *Annals OR*, 188(1):33-61, 2011.

[3] Peter L. Hammer and Tibérius O. Bonates. Logical analysis of data - an overview: From combinatorial optimization to medical applications. *Annals OR*, 148(1):203-225, 2006.

[4] Fabien Chhel, Frédéric Lardeux, Adrien Goëffon, and Frédéric Saubion. Minimum multiple characterization of biological data using partially defined boolean formulas. In *Proceedings of the ACM Symposium on Applied Computing, SAC 2012, Riva, Trento, Italy, March 26-30, 2012*, pages 1399-1405, 2012.

[5] Arthur Chambon, Tristan Boureau, Frédéric Lardeux, Frédéric Saubion, and Marion Le Saux. Characterization of multiple groups of data. In *Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on*, pages 1021-1028. IEEE, 2015.
# Conservation of interaction energy landscapes across structural homologs through cross-docking calculations

Hugo SCHWEKE<fnref n="1" />, Sophie SACQUIN-MORA<fnref n="2" />, Marie-Hélène MUCCHIELLI-GIORGI<fnref n="1" />,<fnref n="3" /> and Anne LOPES<fnref n="1" />

Corresponding Author: anne.lopes@u-psud.fr

In 2003, Aloy *et al.* showed that homologs sharing at least 30% sequence identity almost invariably interact the same way [1]. In this study, we ask whether the whole interaction energy landscape of two interacting partners is conserved during evolution. Particularly, are the low-energy binding sites on a protein surface, as well as the less energetically favourable regions conserved among structural homologs? We tested this hypothesis through a large scale cross-docking experiment where structural homologs and arbitrary ligands were docked with a same receptor. We constituted a database of 72 protein structures divided into 12 structural homolog families. We performed a complete cross-docking experiment with ATTRACT [2]. Each protein played alternately the role of the receptor and of the ligand. For each pair of proteins, we produced a two dimensional (2D) energy map reflecting its docking energy landscape. We compared the energy maps of a receptor docked with all the ligands and ask whether the energy maps produced by structurally related ligands are more similar than those produced by unrelated ones.

This experiment highlights three major results: (i) for each receptor of the database, docking energy landscapes are more similar for ligands belonging to the same family. To quantify this effect, we measured our capacity to retrieve the corresponding families of the 72 ligands from the classification of their energy maps solely. The resulting Area Under the Curve (AUC) value is 0.83 and reflects clearly that structural homologs share similar interaction energy landscapes when interacting with a same receptor. (ii) the classification reveals that four structural families are subdivided into two sub-clusters that produce clearly distinct docking energy landscapes. This split cannot be explained by classical descriptors such as sequence identity or RMSD. Instead this is mainly explained by different distributions of charges at the surfaces of homologs. Interestingly, these subdivisions seem to reveal particular biophysical or functional properties of these proteins. (iii) to distinguish regions favourable to interactions from unfavourable ones, we discretized the 2D energy maps into distinct energy classes. This representation enables to pinpoint interaction hot-spots, warm and cold spots. Interestingly we show that the information provided by either the warm and cold spots is sufficient to correctly classify the ligands suggesting that not only the hot spots but also the rest of the surface have been constrained during evolution.

These results show that (i) the whole docking energy landscape seems to be conserved among structural homologs, (ii) protein docking can highlight biophysical and functional properties of structural homologs that could not be revealed by classical descriptors and (iii) warm and cold spots contain important information on the properties of a protein family. These regions may play an important role in protein interaction by competing with the effective native binding site.

## References

[1] Aloy P, Ceulemans H, Stark A, Russell RB. The relationship between sequence and interaction divergence in proteins. J Mol Biol., 332:989-98, 2003.

[2] Fiorucci S, Zacharias M. Binding site prediction and improved scoring during flexible protein-protein docking with ATTRACT. Proteins, 78:3131-9, 2010.

<footnote n="1">¹ Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France</footnote>

<footnote n="2">² Laboratoire De Biochimie Théorique, CNRS UPR 9080, Institut De Biologie Physico-Chimique, Paris, France</footnote>

<footnote n="3">³ Sorbonne Universités, UPMC Univ Paris 06, UFR927, F-75005, Paris, France</footnote>
# Dynamic model of Central Carbon Metabolism and electrochemical reactions of the cell.

Cécile MOULIN<fnref n="1" />,<fnref n="2" />, Jorgelindo DA VEIGA MOREIRA<fnref n="3" />, Erwan BIGAN<fnref n="3" />, Laurent SCHWARTZ<fnref n="4" />, Mario JOLICOEUR<fnref n="5" />, Laurent TOURNIER<fnref n="2" /> and Sabine PERES<fnref n="1" />,<fnref n="2" />

Corresponding author: cecile.moulin@lri.fr

## Abstract

In order to better understand cell proliferation, we propose to study several well-known intracellular oscillators such as NAD(H), pH, ATP, NADP(H). To understand and explain some experimental data [1] that have been obtained from fresh normal and cancer cells, extracted from human colon after a colectomy, we have constructed a dynamical model which synergistically combines two layers: a metabolic model [2] and an electrochemical model [3]. The metabolic part is focused on central carbon metabolism (CCM), since it represents the metabolic fingerprint of the cell that intimately interconnects with all cellular functions and intrinsic regulatory mechanisms. The electrochemical part represents the set of ionic reactions impacting the pH of the cell. In this work, the CCM was anchored to the electrochemical dynamics by means of the intracellular protons (H+) and energy (ATP-ADP) management.

## References

[1] J Da Veiga Moreira. The Redox Status of Cancer Cells Supports Mechanisms behind the Warburg Effect. *Metabolites*, 6(4):1-12, 2016.

[2] Julien Robitaille, Jingkui Chen, and Mario Jolicoeur. A single dynamic metabolic model can describe mab producing CHO cell batch and fed-batch cultures on different culture media. *PLOS ONE*, 10(9):1-23, 2015.

[3] Yann Bouret, Médéric Argentina, and Laurent Counillon. Capturing intracellular ph dynamics by coupling its molecular mechanisms within a fully tractable mathematical model. *PLOS ONE*, 9(1):1-11, 2014.

<footnote n="1">¹ LRI, Université Paris-Sud & UMR CNRS 8623, F-91405 Orsay, France</footnote>

<footnote n="2">² MAIAGE, INRA, Université Paris-Saclay, 78350 Jouy-en-Josas, France</footnote>

<footnote n="3">³ Laboratoire d'Informatique du l'X, UMR 7161, Ecole Polytechnique, F-91128 Palaiseau, France</footnote>

<footnote n="4">⁴ Assistance Publique des Hopitaux de Paris, F-75015 Paris, France</footnote>

<footnote n="5">⁵ Research Laboratory in Applied Metabolic Engineering, Department of Chemical Engineering, Ecole Polytechnique de Montreal, C.P. 6079, Centre-ville Station, Montreal (Quebec), Canada</footnote>
# Proposition d'un workflow d'analyse QIIME dans Galaxy et évaluation de trois techniques d'extraction d'ADN pour l'analyse du microbiote intestinal 16S

S. BUFFET BATAILLON<fnref n="1" />, M. BEN ABDALLAH<fnref n="1" />, P. BORDRON<fnref n="2" />, E. CORRE<fnref n="2" />, S. KAYAL<fnref n="1" />

Corresponding Author: marouane.bah@gmail.com

## Introduction

Le microbiote intestinal est composé de l'ensemble des microorganismes présents dans le tractus digestif. Il est aujourd'hui identifiable grâce à une révolution biotechnologique : le séquençage à haut débit (NGS). Cette technique permet d'identifier jusqu'à 500 OTU (Operational Taxonomic Units) ou espèces bactériennes dans un microbiote intestinal. Elle nécessite différentes étapes que sont l'extraction, l'amplification, le séquençage d'ADN et le traitement des données. L'extraction d'ADN peut être faite sur colonne par des kits manuels comme Powerfec Mobio® (Gold standard) (PWF), ou par des appareils automatiques sur billes magnétiques comme MagNapure 2.0 Roche® (MA) et Qiasymphony Qiagen®(QIA). L'amplification a lieu dans la zone V3-V4 de la région de l'ARN ribosomal 16S présente chez toutes les bactéries.

## Objectifs et méthodes

Notre objectif principal est de créer et valider un workflow complet d'analyse des données de reads s'appuyant sur le package *Quantitative Insights Into Microbial Ecology* (QIIME) au sein d'une instance Galaxy. Notre objectif secondaire est d'analyser avec ce workflow et RStudio (RStudio Inc) l'impact de l'extraction d'ADN par PWF, MA et QIA sur les résultats NGS du microbiote 16S au CHU de Rennes.

## Résultats

Le workflow QIIME sur Galaxy que nous proposons comporte 10 étapes principales de traitement de données allant de la préparation des séquences jusqu'au calcul de l'alpha et de la beta diversité de nos échantillons. Plus précisément, nous avons tout d'abord (1) décrit nos échantillons (*Validate mapping file*). Nous avons ensuite (2) sélectionné des amplicons d'intérêt (*Split Fastq Libraries*), puis nous avons (3) regrouper les séquences similaires en OTU (*Pick OTU*) avec un seuil de similarité de 0.97. (4) La séquence la plus abondante de chaque OTU obtenu a ensuite été choisi comme séquence représentative de l'OTU et les OTU ont été filtrés selon leur abondance (*Pick rep set*). (5) nous avons ensuite aligné les séquences représentatives (*Align sequences*) selon les méthodes Pynast et Uclust pour ensuite (6) leur assigner une taxonomie issue de la base Greengenes (*Assign taxonomy*). A partir de ce moment-là, (7) nous avons pu calculer la répartition du nombre d'OTU par échantillon (*Make OTU table*) tout en prenant soin de retirer les OTU non pertinents (*Filters OTU from OTU table*). (8) Nous avons également présenté nos OTU selon la distance euclidienne (*Make phylogeny*). (9) Nous avons ensuite estimé l'alpha diversité selon les indices de Chao1, Shannon et Simpson et (10) la beta diversité selon les distances Unifrac et Weighted Unifrac en utilisant respectivement les outils *Calculate Alpha diversity* et *Calculate Beta diversity*. L'ADN de 5 échantillons de selles a été extrait pour un même patient : 1 par PWF, 2 par MA, 2 par QIA. La table d'occurrence des OTU était identique quelque soit la technique d'extraction d'ADN utilisée. Aucune différence significative n'était montrée en analyse en composante principale entre les 3 méthodes d'extraction d'ADN (Indice KMO =0,72 et test de Bartlett <0,001). Les résultats d'alpha diversité et de bêta diversité étaient significativement comparables par tests ANOVA, par la méthode ADONIS.

## Conclusion

Notre workflow est opérationnel sur la plateforme Galaxy ABIMS de la Station Biologique de Roscoff. Il nous a permis de montrer que notre méthode d'extraction automatique d'ADN MagNapure 2.0 Roche® peut être utilisée comme technique d'extraction d'ADN pour de l'analyse de microbiote 16S par NGS au CHU de Rennes.

<footnote n="1">¹ Service Bactériologie, Hygiène hospitalière, Centre Hospitalier Universitaire de Rennes,
2 rue Henri Le Guilloux, 35033, Rennes, France</footnote>

<footnote n="2">² CNRS - FR2424 - ABiMS, Station Biologique de Roscoff, Place Georges Teissier, 29680, Roscoff,
France</footnote>
# BIOSPECIMENS v1.5: a web platform to facilitate collaborative research on infectious diseases

Karen LOUIS<fnref n="1" />, Blandine RIMBAULT<fnref n="2" />, Clément DELESTRE<fnref n="1" />, Yoann MOUSCAZ<fnref n="1" />, Marine ALBRIEUX<fnref n="1" />, Christelle BOISSE<fnref n="1" />, Régis VILLET<fnref n="1" />, Guillaume BOISSY<fnref n="1" />

Corresponding Author: yoann.mouscaz@bioaster.org

## BIOSPECIMENS, a web application fostering collaborations in the scope of infectious diseases and microbiology

BIOSPECIMENS [1] is a free to use collaborative platform which brings together project leaders and biological sample holders in the fields of infectious diseases and microbiota. Important changes come with this new major release which included to take some significant choices. Several clinical research engineers, bioinformaticians and informaticians were gathered in the interest of this translational project.

## Major technological changes and open source priority

Most of the innovation in the version 1.5 is focused on the technical part of the application which was subjected to a complete overhaul favoring the wide incorporation of open-source components: Symfony [2] as web PHP framework, Doctrine [3] for the ORM (Object Relational Mapping) andTwig [4]. Moreover, some notable improvements in the MariaDB [5] database architecture were made to obtain better performances.

BIOSPECIMENS v1.5 is now more hardened to host repositories of data complying with heterogeneous sources and is ready to be proposed as a main portal to academic and industrial collection-holders or to be incorporated as a component of future BIOASTER collaborative projects.

## A robust development methodology

Due to project complexity and to the large scale of technical fields covered in the team, we had to find an efficient way to interact between us. Self-hosted and open source DevOps solutions were studied and tested.

Redmine [6] and GitLab [7] platforms appeared to be the good candidates regarding our needs. Concerning the first one, it was used as documentation, Gantt and ticketing tool. In addition, it is usable by each range of users with different level of affinity regarding computer skills; whereas the second one was used by the bioinformaticians and informaticians to develop, to version the source code and to proceed of its continuous integration.

## Acknowledgements

Thanks to Amila Malinovic for her works produced on BIOSPECIMENS v1.5 during her Master internship.

## References

[1] https://biospecimens.bioaster.org

[2] https://symfony.com/

[3] http://www.doctrine-project.org/

[4] https://twitter.sensiolabs.org/

[5] https://www.mariadb.org/

[6] http://www.redmine.org/

[7] https://www.gitlab.com/

<footnote n="1">¹ BIOASTER, 40 avenue Tony Garnier, 69007, Lyon, France</footnote>

<footnote n="2">² BIOASTER Paris, Bâtiment F. Jacob - 28 rue du docteur Roux, 75015, Paris, France</footnote>
Host tropism and host-pathogen interplay of typhoidal *Salmonella enterica*

Ludovic MALLET¹, Claire HOEDE¹, Franck CERUTTI¹, Annick MOISAN¹, Christine GASPIN¹, Isabelle VIRLOGEUX-PAYANT², Inna SHOMER³, Ohad GAL-MOR³, Thomas SCHIEX¹ and Hélène CHIAPELLO¹

¹Inra, UR 875 MIAT, Auzeville, 31326 Castanet-Tolosan, France, ²Inra, UMR1282 Infectiologie et Santé Publique, F-37380 Nouzilly, France, ³The Infectious Diseases Research Laboratory, Sheba Medical Center, Tel-Hashomer, 5262100, Israel.

Corresponding Author: helene.chiapello@inra.fr

The species *Salmonella enterica* is one of the most prevalent human and animal pathogens, it includes Non Typhoidal *Salmonella* (NTS) serovars like Typhimurium and Enteridis, that are generalist pathogens with broad host specificity and Typhoidal *Salmonella* (TS) serovars, like Typhi and Paratyphi A, that are specialized pathogens strictly adapted to the human host and the cause of an invasive, dangerous disease known as enteric (typhoid) fever [1,2,3].

The SalHostTrop project aims at identifying, characterizing and understanding the human-restricted tropism of Typhoidal *Salmonella* (TS) using comparative dual-RNAseq sequencing and other complementary approaches.

We combine state of the art genome and transcriptome sequencing methods to decipher the molecular basis of host-tropism in clinical strains. We contrast the comparative genomics and differential expression analyses to explore and assess the variability and plasticity of pathogenesis routes among and between typhoidal and non-typhoidal serovars.

We present our on-going work including the Pacbio long-read genomic sequencing, assembly and annotation [4] of a new S. Typhi strain (120130191) and the dual RNAseq data analysis of a pilot experiment of S. Typhimurium and S. Paratyphi A during human epithelial cells infection. The new S. Typhi strain includes one circularized complete chromosome and one plasmid of about 4.78 Mb with 4638 coding genes and 106.7 kb with 128 coding genes, respectively. The dual RNAseq pilot first analyses demonstrate the feasibility of the protocol to target both pathogen and host transcripts simultaneously during infection. We also built a S. enterica subsp. enterica reference phylogenetic tree from the super-alignment of Salmonella core genes in 214 complete genomes of various serotypes that is in agreement with previous studies and will be used to explore pseudogene content of serotypes according to their evolutionary history.

Acknowledgements

This work was supported by the Infect-ERA SalHostTrop projet. We thank the platforms GenoToul Bioinfo and GenoToul GetPlage for support, resources and services.

References

[1] Langridge GC, Fookes M, Connor TR, Feltwell T, Feasey N, Parsons BN, Seth-Smith HM, Barquist L, Stedman A, Humphrey T *et al*: Patterns of genome evolution that have accompanied host adaptation in Salmonella. Proc Natl Acad Sci U S A 2015, 112(3):863-868.

[2] Gal-Mor O, Boyle EC, Grassl GA: Same species, different diseases: how and why typhoidal and non-typhoidal Salmonella enterica serovars differ. Frontiers in microbiology 2014, 5:391.

[3] Grépinet O, Rossignol A, Loux V, Chiapello H, Gendrault A, Gibrat J-F, Velge P, Virlogeux-Payant I: Genome sequence of the invasive Salmonella enterica subsp. enterica Serotype Enteritidis Strain LA5. Journal of Bacteriology 2012, 194(9):2387-2388.

[4] Sallet E, Gouzy J, Schiex T: EuGene-PP: a next-generation automated annotation pipeline for prokaryotic genomes. Bioinformatics 2014, 30(18):2659-2661.
# Towards a new heuristics to compute Consensus Ranking of Big Biological Data

Pierre ANDRIEU¹, Laurent BULTEAU², Sarah COHEN-BOULAKIA¹, Alain DENISE¹,³, Anthony LABARRE², Adeline PIERROT¹ and Stéphane VIALETTE²

¹ Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud, CNRS, Université Paris-Saclay, France

² Laboratoire d'Informatique Gaspard-Monge (LIGM), Université Marne-la-Vallée, CNRS, France

³ Institut de Biologie Intégrative de la Cellule (I2BC), Université Paris-Sud, CNRS, CEA, Université Paris-Saclay, France

Corresponding author: pierre.andrieu@lri.fr, sarah.cohen-boulakia@lri.fr

The aim of biological data ranking is to help users faced with huge amount of data and choose between alternative pieces of information. This is particularly important when querying biological data integration systems, where even very simple queries can return thousands of answers. For instance, searching for the set of human genes involved in breast cancer returns thousands of answers in the reference database EntrezGene without any ranking in terms of importance. The need for ranking solutions, able to order answers, is crucial for helping scientists to organize their time and prioritize the new experiments to be possibly conducted. However, ranking biological data is a difficult task for various reasons: biological data are usually annotation files which reflect expertise, they thus may be associated with various degrees of confidence; the need expressed by scientists may also be taken into consideration whether the most well-known data should be ranked first, or the freshest, etc. As a consequence, although several ranking methods have been proposed in the last years within the bioinformatics community, none of them has been deployed on systems currently in use.

## 1 Consensus Ranking for Biological data

The approach we propose to follow [1] is to rank biological data by considering two steps. First, several ranking methods are applied to biological data (results are ordered using alternative ranking criteria). Second, we use consensus ranking methods reflecting the input rankings' common points while not putting too much importance on elements classified as "good" by only one or a few rankings. The problem, known as the median problem for a set of rankings, is **NP-hard**. However, since providing a consensus ranking is a crucial need for big biological data sets, designing scalable algorithms is highly challenging. Besides, the problem has been mainly studied in the case of *permutations* where elements are strictly ordered while in real applications some elements may be placed at the same position (considered as equally important). The challenge is then to design an algorithm computing one consensus ranking from a set of rankings with ties.

## 2 Towards a partitioning solution for ranking with ties

We introduce a new algorithm computing a consensus ranking from a set of rankings with ties. The originality of our approach lies in providing an efficient solution (i) based on a graph decomposition of the datasets to partition it efficiently and (ii) having several interesting and fundamental properties, which allow to evaluate the relevance of a given solution and able to provide the exact consensus in many cases. A set of experiments has been conducted on several hundreds of biological and synthetic data sets [2]. First results appear to be very promising, making our algorithm able to compete with the best currently available algorithms while being efficient enough to be used on real settings [3] in particular as the algorithm used on http://conqur-bio.lri.fr/.

## Acknowledgements

This work was supported by the CNRS Mastodons *QualiBioConsensus* project.

## References

[1] Sarah Cohen Boulakia, Alain Denise, and Sylvie Hamel. Using medians to generate consensus rankings for biological data. In *Scientific and Statistical Database Management - 23rd International Conference, SSDBM 2011, Portland, OR, USA, July 20-22, 2011. Proceedings*, pages 73-90, 2011.

[2] Bryan Brancotte, Bo Yang, Guillaume Blin, Sarah Cohen Boulakia, Alain Denise, and Sylvie Hamel. Rank aggregation with ties: Experiments and analysis. *PVLDB*, 8(11):1202-1213, 2015.

[3] Bryan Brancotte, Bastien Rance, Alain Denise, and Sarah Cohen Boulakia. Conqur-bio: Consensus ranking with query reformulation for biological data. In *Data Integration in the Life Sciences - 10th International Conference, DILS 2014, Lisbon, Portugal, July 17-18, 2014. Proceedings*, pages 128-142, 2014.
# Titre : « Comparaison de pipelines pour la découverte de signatures métagénomiques à partir de séquençage 16S en contexte clinique »

Auteurs : Aziza Caidi<fnref n="1" />, Emma Bergsten<fnref n="2" />, Iradj Sobhani³, Denis Mestivier<fnref n="1" />

Auteur correspondant: aziza.caidi@u-pec.fr

## Résumé

Le microbiote intestinal joue un rôle important dans la santé de son hôte et des changements de sa composition (dysbiose) sont associés avec des états pathologiques (obésité, diabète, cancers, etc)[1] [2].

L'amplification et le séquençage du gène de la sous unité ribosomale 16 (16S) reste la technologie la plus utilisée pour l'identification des microbiotes en métagénomique clinique[3]. Plusieurs pipelines bioinformatiques existent pour permettre l'identification de signatures métagénomiques (compositions différentielles de bactéries) et/ou de bactéries biomarqueurs dans le diagnostic[4].

Dans le contexte clinique du cancer colo-rectal (CCR) qui est l'un des trois cancers les plus fréquents, des études ont mis en évidence l'implication de quelques bactéries dans le développement de ce cancer[4]. Ces signatures métagénomiques ont été identifiées par des pipelines différents (Mothur, QIIME, MEGAN), mais aucune comparaison de ces pipelines n'a été effectuée, rendant difficile la confrontation des différentes signatures métagénomiques publiées.

L'objectif de ce travail, qui bénéficie de deux cohortes monocentriques d'une même population, est de préciser les limites et la reproductibilité de ces pipelines dans l'identification de signatures métagénomiques.

La signature métagénomique sera évaluée par le package metagenomeSeq du logiciel R pour chacune des tables d'annotation produites par les pipelines sur chaque cohorte. La reproductibilité des pipelines sera présentée pour les différents niveaux taxonomiques et des règles pourront être proposées de choix d'un pipeline dans ce contexte clinique.

## Références

[1] C. Jin, J. Henao-Mejia, and R. A. Flavell, "Innate immune receptors: Key regulators of metabolic disease progression," *Cell Metab.*, vol. 17, no. 6, pp. 873-882, 2013.

[2] I. Sobhani et al., "Microbial dysbiosis in colorectal cancer (CRC) patients," *PLoS One*, vol. 6, no. 1, 2011.

[3] R. S. Mandal, S. Saha, and S. Das, "Metagenomic Surveys of Gut Microbiota," *Genomics, Proteomics Bioinforma.*, vol. 13, no. 3, pp. 148-158, 2015.

[4] G. Zeller et al., "Potential of fecal microbiota for early-stage detection of colorectal cancer.," *Mol. Syst. Biol.*, vol. 10, no. 11, p. 766, 2014.

<footnote n="1">¹ Plateforme Bioinformatique/Institut Mondor de Recherche biomédicale (IMRB-Inserm U955) - Université Paris-Est Créteil, Faculté de Médecine de Créteil, 8 rue du Général Sarrail 94010 Créteil cedex, France</footnote>

<footnote n="2">² Early detection of Colon Cancer using Molecular Markers and Microbiota (EC2M3) - IMRB/EA 7375, Groupe Hospitalier Henri Mondor, Service de Gastroentérologie, 51 Av Maréchal de Lattre de Tassigny, 94010 Créteil, France</footnote>
# Horizontal gene transfer from viruses in the genomes of plant-parasitic nematodes

Carole BELLIARDO, Corinne RANCUREL, Etienne G.J. DANCHIN and Marc BAILLY-BECHET

Institut Sophia Agrobiotech, INRA, CNRS, Univ. Côte d'Azur, 06900 Sophia Antipolis, France

Corresponding author: carole.Belliardo@etu.unice.fr

## 1 Introduction

Root-knot nematodes, genus *Meloidogyne*, are one of the most damaging plant-pest around the world and sufficient control methods are missing. To develop new control methods, we need to better understand the biological processes and evolutionary history of *Meloidogyne*. For all plant-parasitic nematodes characterized at the omics level, several horizontal gene transfer (HGT) from bacteria or fungi have been highlighted. HGT can originate from soil dwelling organisms and pathogens of nematodes or plants [1]. The role of viruses as potential donors is unclear, and few nematode viruses are known.

## 2 Objectives

Recently, metagenomic sequencing has increased the availability of virus sequences in public databases[2]. These data open a new opportunity to determine whether there is a viral contribution to the *Meloidogyne* genomes. Here, we aim at identifying *Meloidogyne* genes of viral origins and the families of potential viral donors.

## 3 Material and Methods

We used two approaches to identify and characterize candidate HGT of viral origins. First, we detect HGT based on sequence homology with two different tools: (i) Alienness, a taxonomy aware BLAST of *Meloidogyne* proteins against nrNCBI-DB and calculation of difference in magnitude of e-value between metazoan and viral hits[3]; (ii) Retrieval of viral HMM profiles from ImgVR-DB [2] and scan against *Meloidogyne* proteins. In second step, we look at GC percent and codon usage bias of *Meloidogyne* protein coding genes by multivariate analysis. This way, we compare the composition of genes acquired from viruses to the rest of the genome, to determine the molecular signatures of viral HGT and discriminate old and recent HGT.

## 4 Results and discussion

We found 80 sequences with putative viral origin in coding sequences of the model root-knot nematode, *Meloidogyne incognita*. Among those, there is a significant enrichment of *Herpesviridae*, *Baculoviridae* and *Adenoviridae*, animal virus strains. All of these genes are supported by expression data, so we suppose a domestication by host genome. Analysis of the codon usage bias brings to light a significant difference between these viral sequences: on one side, domesticated sequences indicating old HGT, with *Baculoviridae* lineage annotations, and on the other side recent HGT with *Adenoviridae* and *Herpesviridae* annotation. Comparative studies with other *Meloidogyne* and *C. elegans*, converge toward that a part of HGT is ancient and the other part is recent.

These results provide a field of investigation to further characterize the candidate viral donors at the species level that could today infect *Meloidogyne* species. These findings could be useful for new bio-controls methods or in biotechnology. Indeed, no virus based bio-control or transformation methods are known in *Meloidogyne* so far.

## References

[1] E. G. J. Danchin & al. Multiple lateral gene transfers and duplications have promoted plant parasitism ability in nematodes. *Proceedings of the National Academy of Sciences*, October 2010.

[2] David Paez-Espino, Emiley A. Eloe-Fadrosh, Georgios A. Pavlopoulos, Alex D. Thomas, Marcel Huntemann, Natalia Mikhailova, Edward Rubin, Natalia N. Ivanova, and Nikos C. Kyrpides. Uncovering Earth's virome. *Nature*, 536(7617):425-430, August 2016.

[3] C. Rancurel & al. Rapid detection of horizontal gene transfers in metazoan genomes. https://f1000research.com/posters/1096828, November 2014.
# Mutation of Tyr137 of the universal Escherichia coli fimbrial adhesin FimH relaxes the tyrosine gate prior to mannose binding

Eva-Maria Krammer¹, Goedele Roos¹, Martine Prévost², Julie Bouckaert and Marc F Lensink¹

¹ University of Lille, CNRS UMR8576 UGSF, F-59000 Lille, France

² Structure et Fonction des Membranes Biologiques, Université Libre de Bruxelles (ULB), Brussels, Belgium

Corresponding Author: eva-maria.krammer@univ-lille1.fr

The most prevalent diseases manifested by Escherichia coli are acute and recurrent bladder infections and chronic inflammatory bowel diseases such as Crohn's disease [1,2]. E. coli clinical isolates express the FimH adhesin, which consists of a mannose-specific lectin domain connected via a pilin domain to the tip of type 1 pili. Although the isolated FimH lectin domain has affinities in the nanomolar range for all high-mannosidic glycans, differentiation between these glycans is based on their capacity to form predominantly hydrophobic interactions within the tyrosine gate at the entrance to the binding pocket [3].

Single-residue mutations in the tyrosine gate (Tyr48Ala and Tyr137Ala) have a moderate effect on the affinity of FimH for mannose, but a markedly lower affinity is observed for heptyl- and biphenyl-substituted mannosides that intercalate in the tyrosine gate. In the crystals of the Y137A mutant, a breakdown of the binding site with a severe loss of specificity is observed [4]. Using quantum-mechanical calculations, we could demonstrate that the wild-type Tyr137 introduces strain in the polypeptide backbone. This maintains a high energetic potential that is normally only released upon the binding of an oligomannosidic ligand. Using molecular-dynamics simulations, we could highlight that this energetic potential is coupled to the other tyrosine of the gate, Tyr48, via the inner mannose-binding residue Ile52. In conclusion, the mutation of Tyr137 to alanine relaxes the binding site prematurely, whereby the stringent selectivity of the FimH lectin for mannose is disrupted and the binding affinity decreases.

## References

[1] Goneau et al. *Subinhibitory Antibiotic Therapy Alters Recurrent Urinary Tract Infection Pathogenesis through Modulation of Bacterial Virulence and Host Immunity* mBio, 2015. 6: p. e00356-15.

[2] Darfeuille-Michaud et al. *High prevalence of adherent-invasive Escherichia coli associated with ileal mucosa in Crohn's disease*. Gastroentrology 2004. **127**: p 412-421.

[3] Roos et al. *Validation of Reactivity Descriptors to Assess the Aromatic Stacking within the Tyrosine Gate of FimH*. ACS Med Chem Lett. 2013. **4**: p.1085-1090.

[4] Rabbani et al, *Mutation of Tyr137 of the universal Escherichia coli fimbrial adhesin FimH relaxes the tyrosine gate prior to mannose binding*, IUCRJ, 2017. **4**: p.7-23
Collapsing reads while maintaining qualities : srnaCollapser

Walid BEN SAOUD BENJERRI, Christine GASPIN and Matthias ZYTNICKI

MIAT, 24 Chemin de Borde Rouge, 31320, Auzeville, France

Corresponding author: matthias.zytnicki@inra.fr

1 Background

sRNAs are a class of non coding RNA molecules. Many of them play key roles in the cell life, such as microRNAs, that target messenger RNA to inhibit translation. Besides their short size ~15–200, they are also characterized by their diversity of abundance as some of them are highly transcribed [1]. Sequencing technologies are used to get the ordered list of nucleotides of many sRNAs in a sample. The output takes the form of a FASTQ file. However, sequencing is error prone, there is no guarantee that the correct nucleotide matches the one output by the machine. To estimate the reliability of a base calling, current technologies attach to each nucleotide a quality value which is function of the probability that the correct base was called.

The amount of generated data is huge and highly redundant. Because of that, RNA sequencing data analysis is preceded by a preprocessing step that aims to remove this redundancy. Typically, users will eliminate duplicated reads keeping only one sequence while maintaining the number of occurrences for this sequence, discarding the associated read original qualities. This is known by “collapsing” [2].

2 A new approach for small RNA read collapsing

We propose an alternative way of collapsing, wherein a synthetic quality is calculated for all the duplicates of the same sequence. Keeping qualities, as opposed to the naive usage, will give additional information at the next step of the pipeline, the read mapping. It is especially useful for reads having few duplicates or unique reads. The emphasis is put on efficiency of the implementation so that it can be used as part of larger pipelines.

The synthetic quality is calculated as follows. For each base *b*, the synthetic quality value is given by the maximum over the qualities associated to *b* in the set of identical reads to collapse.

Two other functionalities are provided. For each sequence, its number of occurrences is computed by sample. Finally results are given in a sorted manner as a FASTQ-like file.

3 Implementation

To make the approach efficient, reads are inserted in a Trie. Usage of a trie allows to aggregate similar sequences to minimize spatial disparity which offers quick retrieval (or quick rejection) when inserting new sequences. High speed is achieved by using low level optimizations such as bit-manipulations.

4 Performance Evaluation

We compared the runtime and space usage of our approach to a naive solution built-on the Unix tool sort (with an additional script to collapse qualities), and a hashtable solution. Result show performance improvement of srnaCollapser over the other approaches.

<table><thead><tr><th>Method</th><th>1 file (1.7 GB)</th><th>3 files (4.8 GB)</th><th>6 files (9.3 GB)</th><th>9 files (14.1 GB)</th></tr></thead><tbody><tr><td>srnaCollapser</td><td>36s</td><td>1m28s</td><td>3m42s</td><td>5m34s</td></tr><tr><td>GNU Sort</td><td>4m</td><td>11m50s</td><td>≥ 20m</td><td>out of memory</td></tr><tr><td>Hashtables</td><td>22s</td><td>1m30s</td><td>4m20s</td><td>out of memory</td></tr></tbody></table>

References

[1] Shirley Tam, Ming-Sound Tsao, and John D. McPherson. Optimization of miRNA-seq data preprocessing. *Briefings in Bioinformatics*, 16(6):950, 2015.

[2] Filipe Borges and Robert A. Martienssen. The expanding world of small RNAs in plants. *Nat Rev Mol Cell Biol*, 16(12):727–741, 2015.
Posters partenaires
# BioMAn™: a user-friendly interface for targeted metagenomic data visualization and analysis

Pauline Vaissiég<fnref n="1" />, Christophe Camus<fnref n="1" />, Yao Amouzou<fnref n="1" />, Thomas Carton<fnref n="1" />, Sophie Le Fresne-Languille<fnref n="1" />, Françoise Le Vacon<fnref n="1" />, Murielle Cazaubiel<fnref n="1" /> and Sébastien Leuillet<fnref n="1" />

Corresponding Author: pauline.vaissie@mxns.com

With the recent advances in the field of next-generation sequencing (NGS), metagenomics allow to explore the biodiversity of microbial ecosystems or microbiota. Dedicated bioinformatic pipeline focusing on targeted metagenomics (16S rRNA) provides to biologists the bacterial composition in OTUs (Operational Taxonomic Units) of the samples. Nevertheless, NGS produces massive data which requires substantial computer processing to extract information. Faced with this large amount of data, their visualization and appropriate statistical analysis are essential for scientists to adequately explore and interpret their experiments. In this context, we have developed an R [1] and Shiny [2] web based platform called BioMAn™ (Biofortis Metagenomics Analysis) which mixes the statistical power of dedicated R packages (metagenomeSeq, mixOmics...) with a user friendly web design. This interface allows users to interactively look into their project by manipulating, filtering or gathering information for further interpretation or communications purposes. Focusing on a subgroup of samples is made very easy by the integration of metadata table (information on samples such as experimental conditions). The core of the tool is focused on data visualization, which offers the possibility to depict taxonomic composition throughout several graphical interactive representations such as barplots, boxplots, heatmaps, Krona [3] or hierarchical trees. BioMAn™ also provides information (tables and graphs) about diversity indices to help users in the interpretation of results. This turnkey product is an easy way for scientists to conduct ordination analysis such as PCoA with a lot of customizable graphical and analytical options. The platform can also be used to run specific statistical analysis like discriminant analysis (LDA and FDA). Other statistical approaches are currently being added to the application (PERMANOVA/ANOSIM, differential analysis...) in order to create the fullest possible metagenomic toolbox. During the process, user can easily retrieve objects by downloading them in high quality or by inserting them one by one into a custom PowerPoint template. BioMAn™ is deployed on a Shiny Server Pro, implemented by a secure health data hosting provider according to the French regulatory requirements, to protect the confidentiality, integrity and availability of patient and user data.

## References

[1] R Core Team. R:A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013.

[2] Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2017). shiny: Web Application Framework for R. R package version 1.0.0. https://CRAN.R-project.org/package=shiny

[3] Ondov, Brian D., Nicholas H. Bergman, and Adam M. Phillippy. "Interactive metagenomic visualization in a Web browser." *BMC bioinformatics* 12.1 (2011): 385.

<footnote n="1">¹ Biofortis Mérieux NutriSciences</footnote>
# WHORMSS : un nouvel outil pour l'exploration taxonomique et fonctionnelle sans à priori des métagénomes.

Yannick LAURENT¹, Jérémie DENONFOUX², Antoine BODEIN², Stéphanie FERREIRA²

1 Genoscreen, Service Bioinformatique, 1 rue du Professeur Calmette, 59000 Lille, France

2 Genoscreen, Service Recherche-Développement-Innovation, Communautés Microbiennes, 1 rue du Professeur Calmette, 59000 Lille, France

Contact : yannick.laurent@genoscreen.fr

Les nouvelles technologies de séquençage (NGS) permettent d'accéder, à très haut débit, à des informations génomiques complexes et conséquentes. Cependant, la taille encore trop courte des lectures, les erreurs de séquençage, la quantité de données à analyser ou encore la mixité variable des matrices initiales nécessite le développement d'outils bioinformatiques adaptés et performants permettant d'extraire les informations microbiennes au sein des métagénomes. Les méthodes sans a priori de type Whole Metagenome Shotgun (WMS) s'affranchissant de la PCR et se basant sur un séquençage direct de l'ensemble des génomes présents au sein d'un échantillon, elles représentent des stratégies d'interrogations originales et puissantes des environnements. Ces dernières permettent d'avoir accès notamment à la fraction inconnue et non cultivable des communautés microbiennes, représentant un vivier de nouvelles fonctions. Cependant, l'importante diversité de ces communautés, la connaissance partielle que nous en avons à l'heure actuelle combinées à l'information fragmentaire obtenue et la courte taille des lectures rendent l'annotation des séquences complexe. Les approches métagénomiques à haut-débit WMS représentent donc un réel défi analytique.

Afin de pouvoir proposer un outil automatisé d'analyse de données métagénomiques à haut-débit de type WMS dans un contexte d'étude des communautés microbiennes, le pipeline WHORMSS pour WHOle Recovering from Metagenome Shotgun Sequencing a été développé. Ce dernier est dédié à l'analyse de données de séquençage de type Illumina « paired-end ». WHORMSS intègre un contrôle qualité des séquences, une étape d'assemblage et une stratégie d'annotation taxonomique et fonctionnelle, répondant aux contraintes des analyses métagénomiques WMS à haut-débit. De plus, un mode de recherche ciblée de fonctions microbiennes permet de mettre en lumière des capacités métaboliques connues et inconnues. Les performances de WHORMSS en termes de qualité des affiliations mais également de rapidité et de potentiel d'étude de la structure et fonctions des communautés, ont été éprouvées sur un échantillon de composition maîtrisée et sur des échantillons complexes environnementaux (eau de mer et microbiote intestinal).

WHORMSS a démontré tout son potentiel pour décrire les communautés microbiennes dans un cadre d'étude d'échantillons environnementaux complexes par des approches sans à priori. WHORMSS est également capable de mettre en lumière et à façon des fonctions métaboliques d'intérêt.

**Mots clés :** Métagénomique sans a priori, NGS, diversité microbienne, pipeline, annotation taxonomique et fonctionnelle
# CRISPR LifePipe®: tools for the design of gRNAs and donor sequence required for genome editing using CRISPR/Cas9 system

Virginia CHESNAIS¹, Emmanuel CHAPLAIS¹, Alban OTT¹ and Eric GINOUX¹

¹ Life&Soft, 8b avenue Descartes, 92350, Plessis-Robinson, France

Corresponding Author: vchesnais@lifeandsoft.com

The introduction of targeted genomic sequences modifications by CRISPR technology into living cells is becoming a powerful tool for gene therapy or disease modelling [1]. CRISPR only requires a nuclease and customized nucleic sequences. Preliminary bioinformatics analysis for both gRNA design and donor template can improve the success of the experiment. This is where CRISPR LifePipe will make genome editing as simple as using a text editor.

The gRNA (guide RNA) is a short RNA sequence which guides the Cas9 endonuclease to the targeted region to cut on the genome. gRNA is crucial for CRISPR gene editing, because it provides targeting efficiency and specificity on the region of interest on the genome, while limiting the non-specific off-targets. Our gRNA design tool is built to allow you to target different genomic regions: (i) targeting an exon present on most of the transcripts of a gene, (ii) targeting an exon or an intron of a transcript, (iii) targeting UTR region, (iv) targeting a particular amino acid or (v) targeting a particular DNA sequence. The efficiency of gRNAs is determined by various annotations like secondary structure, presence of SNP on the sequence, prediction score... The research of off-targets for all gRNAs assesses their specificity and help the user to choose the best gRNA.

The donor template is a DNA sequence inserted into the cell along with the gRNA and the Cas9 endonuclease to replace DNA sequence of the cell. Different donor sequences can be designed with our tool according to the modification that is expected in the cell: (i) insertion of a mutation, (ii) gene tagging, (iii) gene knock-out and/or (iv) insertion of a DNA fragment, like a selection cassette. During the process, the donor sequence is inactivated to prevent the Cas9 to cut the donor sequence. Quality control and annotation are also performed to assess the quality of the donor.

Our CRISPR LifePipe is based on a workflow built with the snakemake tool [2]. An intuitive, responsive and ergonomic web interface make a better user experience. One-page web application was developed with Django and Angular UI [3]. Finally, all source code and third-party applications were encapsulated in a docker image, which provides an easier deployment in any production informatics structure [4].

In summary, CRISPR LifePipe tools have been developed to meet all the needs of the CRISPR users. These user-friendly tools will facilitate and improve all steps required for a high quality and successful CRISPR experiment preparation.

## References

[1] L. Cong, F.A. Ran, D. Cox, S. Lin, R. Barretto, N. Habib, P.D. Hsu, X. Wu, W. Jiang, L.A. Marraffini, F. Zhang, Multiplex genome engineering using CRISPR/Cas systems, Science. 339 (2013) 819-823.
doi:10.1126/science.1231143.

[2] J. Köster, S. Rahmann, Snakemake—a scalable bioinformatics workflow engine, Bioinformatics. 28 (2012) 2520–2522. doi:10.1093/bioinformatics/bts480.

[3] AngularUI for AngularJS, (n.d.). https://angular-ui.github.io/ (accessed March 20, 2017).

[4] Docker, Docker. (n.d.). https://www.docker.com/ (accessed March 20, 2017).
# Bioinfo-fr.net : présentation du blog communautaire scientifique francophone par les Geekus biologicus

LA COMMUNAUTÉ DE GEEKUS BIOLOGICUS¹

1 Bioinfo-fr.net

Corresponding Author: admin@bioinfo-fr.net

## Pourquoi

?

"Bioinformatique ? C'est quoi ça ? De l'informatique respectueuse des terres ???". Qui n'a jamais eu ce genre de remarque sur notre profession ? Qui n'a jamais peiné pour expliquer à son entourage son travail de tous les jours ? Depuis quelques années, une petite communauté de bioinformaticien·nes francophones s'est formée suite à un simple constat : il y avait bel et bien un trou dans l'Internet francophone par rapport à notre science ! Nous ne pouvions pas laisser cela tel quel, c'était devenu notre mission. www.bioinfo-fr.net était né !

## Qui

?

Au commencement, nous n'étions qu'une petite dizaine avec tout plein d'idées par dessus la tête et une envie commune de faire avancer les choses. Aujourd'hui la recette a séduit, les fondations ont été posées, le mécanisme est bien huilé et nous sommes plus d'une soixantaine de collaborateurs bénévoles dispersés de partout à travers le monde, toujours avec la même motivation. Pas de hiérarchie stricte clairement mise en place, si ce n'est un petit groupe d'administrateurs dont les rôles principaux sont de maintenir le site à jour, veiller à la bonne cohérence des articles, fournir un planning de publication, accueillir les nouveaux venus, relancer les auteurs et les relecteurs de temps en temps, communiquer avec l'extérieur et essayer de mettre en place les nouvelles idées venant de tout un chacun.

## Comment

?

D'abord via un canal IRC (#bioinfo-fr, réseau FREENODE). Mais très vite nous avons constaté que de nombreuses questions similaires ressortaient et qu'il serait plus efficient de garder les réponses sur un support écrit et de façon pérenne. Le blog nous a semblé être le support le mieux adapté cela. Chaque semaine nous nous efforçons de fournir un article qui a subit un processus de relecture scientifique robuste et qui doit avoir un rapport de près ou de loin avec la bioinformatique. Un article doit entre-autre chose permettre au lecteur, averti ou non, de comprendre une méthode, de reproduire une expérience, de plonger directement dans un code solutionnant un problème biologique ou encore de l'informer sur une toute nouvelle découverte. Au fil des années et des articles nous avons essayé de décomposer ces articles en catégories afin de faciliter la visite et la recherche du lecteur.

## Bilan

En quelques années nous avons réussi à tisser un large réseau de professionnels/étudiants/passionnés capable de produire des articles d'intérêt public et de répondre à des problématiques axées autour de la bioinformatique. Cela nous a permis également de mieux connaitre nos spécialités, nos manières de vivre, notre métier, et de représenter à notre niveau la force de la bioinformatique francophone. Nous ne comptons pas en rester là et vous encourageons fortement à venir discuter avec nous les Geekus biologicus. Peut-être, qui sait, franchirez-vous le cap et nous rejoindrez-vous dans l'aventure !
Liste des contributeurs

ABBY S., 169
ABRAHAM A.-L., 6
ABRAHAM B., 44
AGRET C., 132
ALBRIEUX M., 191
ALIZON S., 151
ALLART É., ii
ALMUNIA C., 149
ALVAREZ A.-S., 162
AMANZOUGARENE S., 158
AMBROISE C., 31
AMOUYEL P., 107
AMOUZOU Y., 199
ANDRIEU P., 193
ANDRÉ C., 18, 20
ANTONARAKIS S., 126
AOUAD M., 82
ARIGON CHIFOLLEAU A.-M., 148
ARMENGAUD J., 149
ARVESTAD L., 170
AUBERT G., 150
AUBOEUF D., 135
AURY J.-M., 121, 181
AZIZA C., 194

BAAIJENS J., 186
BAHIN M., 167
BAILLY X., 163
BAILLY-BECHET M., 195
BALIÈRE C., 179
BALL S., 152
BALZERGUE S., 158
BAPTESTE E., i
BARBE V., 174
BARBIER G., 164
BARETTE C., 143
BARRAS F., 144
BARRAY A., ii, 160
BARREY E., 108
BARTHELEMY M., 143
BASTARD K., 120
BATLEY J., 150
BATMANOV K., 53
BATT G., 53

BATTAIL C., 122
BATTO J.-M., 178
BAUD S., 66
BAUDOT A., 38
BAULANDE S., 125
BAUTERS C., 107
BAYER P., 150
BECAVIN C., 54
BECHELER A., 139
BECKING T., 124
BEDRAN G., 122
BEDRIM., 61, 165
BEIGNON A.-S., 28
BELHAJJAME K., 58
BELLIARDO C., 195
BELLOY N., 66
BELY B., 80, 168
BEN ABDALLAH M., 190
BEN SAOUD BENJERRI W., 197
BENACHI A., 136
BENMOHAMMED S., 168
BENOIT G., 3
BERNARD V., i, 125
BERRABAH W., 174
BERTHELOT C., 104
BERTRAND A., 181
BESANÇON C., 66
BIECHE L., 125
BIGAN E., 189
BIHOUÉE A., 118
BIOLOGICUS G., 202
BIRER A., 111-114
BITARD-FEILDEL T., 75
BITTNER L., 130
BLAISE S., 66
BLANCHET C., 58, 61, 154, 165
BLANCK S., ii
BLANQUART S., i, 10
BLUEON C., 112
BODEIN A., 200
BOISSE C., 191
BOISSY G., 191
BONNET E., 122
BORDRON P., 190
BOREL C., 126
BOTHEREL N., 18
BOUCHEZ O., 155
BOUCHIER C., 60
BOUCHOUICHA N., 135
BOUCKAERT J., 196
BOURBEILLON J., 157
BOURDON J., i, 57, 110, 123
BOUREAU T., 187
BOURRET J., 151
BOUSSAU B., i
BRADNER J., 44
BRANCOTTE B., 61, 154, 165
BRAVO I., 151
BRAYET J., 143
BRELURUT G., 129
BRETAUDEAU A., 56
BROCHIER C., 99
BROCHIER-ARMANET C., 78, 82, 90
BRUN C., i
BRUNAUD V., 142, 158
BRUNO A., 181
BRYSBAERT G., 137
BUFFARD M., 138
BUFFET-BATAILLON S., 190
BIISINE M.-P., 159
BULTEAU L., 193
BURDEN F., 115
BURSTIN J., 150
BÉLIARD A., ii
BÉRARD C., 140, 141
BÉRARD S., i

CABASSI A., 115
CABOCHE S., i, ii
CADIEU E., 18, 20
CADIX M., 117
CAIUS J., 158
CALTEAU A., 119
CAMUS C., 199
CARBONE A., 65
CARGNELUTTI B., 163
CARO V., 179
CAROLINE M., 108
CARON B., 103
CARTON T., 199
CASTRO-MONDRAGON J., 185
CAU P., 38
CAZALS F., i

CAZAUBIEL M., 199
CEDHAGEN T., 183
CENCI U., 152
CERUTTI F., 54, 75, 192
CHABIRAND A., 146
CHAGNY G., 141
CHAMBON A., 187
CHANNAROND A., 141
CHAPLAIS E., 136, 201
CHARPENTIER E., 118, 123
CHASTAGNER A., 163
CHATEAU A., i
CHATEIGNER A., 158
CHAUMOT A., 149
CHAUVIÈRE L., 184
CHEBBI M., 124
CHESNAIS V., 136, 201
CHEVALIER M., 176
CHIAPELLO H., ii, 54, 75, 192
CHIKHI R., i, 170, 171
CHOPARD J., 58
CHOURAKI V., i
CHROKOV A., 182
CLÉMENT Y., 104
COGEZ V., 160
COGNE Y., 149
COHEN-BOULAKIA S., i, 58, 63, 193
COKELAER T., 60
COLLEONI C., 152
COLLIN O., 58
COOLEN M., 182
CORDAUX R., 124
CORDIER T., 183
CORON C., 139
CORRE E., 164, 190
CORREIA D., 63
CORTAL A., 79
COSMA A., 28
COSSART P., 54
COSTA J.-M., 136
COULPIER F., 112
COUTANT A., 45
CROVILLE G., 116
CRUVEILLER S., 31, 119
CUVELLIEZ M., 107
CUVIER O., 100

D'AGATA L., 121
DA SILVA C., 181
DA VEIGA MOREIRA J., 189

DAMERON O., 56

DANCHIN E., 195

DANIS B., 36

DAUCHEZ M., 66

DAVID A., 18

DAVID L., 123

DAVID M., 33

DE CARLI F., 174

DE RUYCK J., 160

DEBIEC H., 184

DELAHAYE-DURIEZ A., 36

DELANNOY E., 142

DELESTRE C., 191

DELEUZE J.-F., 122

DELOGER M., 127

DELPUECH E., 116

DELTEL C., 170

DEMAY C., ii

DENIS M., 194

DENISE A., 193

DENISE R., 169

DENOMME A.-S., 172

DENOMMÉ-PICHON A.-S., 180

DENONFOUX J., 200

DERMITZAKIS E., 126

DERNONCOURT M.-B., ii

DEROZIER S., 6

DERRIEN T., 18, 20

DESHAIES V., 143

DESVILLECHABROL D., 60

DEVAILLY G., 133

DEYELL M., 53

DIAZ E., 167

DILLIES M.-A., i

DJEMIEL C., 134

DONNADIEU C., 155

DOPPELT-AZEROUAL O., 63

DOWNES K., 115

DREZEN E., 3

DRIOUCH K., 125

DROIT A., 128

DUBOIS E., 106

DUCATEZ M., 152

DUCOS B., 167

DUFRESNE Y., 10, 175

DUPAS S., 139

DUPUIS F., 157

DURIMEL K., 145

DUSKO EHRLICH S., 178

DUTERTRE M., 117

EDWARDS D., 150

EHRLICH D., 162

EILER A., 182

EL AABIDINE A., 186

EMMA B., 194

ENGELEN S., 181

ESCOFFIER J., 126

ESLING P., 183

ESQUERRÉ D., 108

EVEILLARD D., i

EVEN G., ii

FALQUE M., 150

FARROW S., 115

FEDERATION A., 44

FELTEN A., 145

FERET J., i

FERRATO-BERBERIAN L., 111–114

FERREIRA S., 200

FERTIN G., 33

FIGEAC M., i, 159

FILANGI O., 56

FLAHAUT C., 176

FLISSI A., 175

FLOT J.-F., 32

FOERCH P., 36

FOURGOUX P., 142

FOURNIER C., 131

FRASSE P., 155

FRATERNALI F., 64

FROIDEVAUX C., 58

FRONTINI M., 115

FRÉOUR T., 123

FURLONG E., 77

GABILLARD S., 136

GABORIT V., 109, 110

GACHET M., 119

GAIGNARD A., 57, 58, 118

GAILLARD S., 157

GAL-MOR O., 192

GALLINA S., ii

GARNIER X., 56

GASCUEL O., 63

GASPIN C., i, 54, 192, 197

GAUTREAU G., 28, 31, 119
GAZINA E., 36
GEFFARD O., 149
GENOVESIO A., 167, 174
GESTRAUD P., 117, 127
GHEYOUCHE E., 62
GHOUMID J., 166
GHOZLANE A., 178
GIBRAT J.-F., 61, 154, 165
GILBERT C., 124
GINOUX E., 136, 201
GIRARDOT C., 77
GIRAUD I., 124
GOUDENÈGE D., 180
GOURAUD W., 110
Gouveia D., 149
GOUY M., 82
GOUZY J., 155
GOWER-ROUSSEAU C., 173
GRANGEASSE C., 78
GREC S., ii, 134
GRIBALDO S., 90
GRIFFIN J., 115
GROUX-DEGROOTE S., 160
GUBRY-RANGIN C., 182
GUERIN J.-L., 116
GUERIN-CHARBONNEL C., 109
GUEUDELOT O., 172, 180
GUIGON I., ii
GUIGUEN Y., 155
GUILLIER L., 145
GUILLOT A., 66
GUIRIMAND T., 6
GUYOMAR C., 177
GUZIOLOWSKI C., i, 57

HAMMAMI F., 144
HARDUIN-LEPERS A., 160
HAWKINS S., 134
HAYASHI S., 150
HERNANDEZ C., 129
HERSEN P., 53
HEUILLARD P., 155
HEURTEAU A., 108
HINSEN K., 58
HITTE C., 18, 20
HOEDE C., 54, 108, 192
HOT D., ii
HUELSENBECK J., 81
HYRIEN O., 174

HÉDAN B., 18, 20

IHN LEE T., 44
IJAZ A., 182
IJAZ U., 182
IRADJ S., 194

JACOB L., i
JAEGER S., 185
JANOT S., ii
JANY J.-L., 164
JAUFFRIT F., 78
JENSEN L., 182
JOHNSON M., 36
JOLICOEUR M., 189
JONQUET J., 66
JORGE V., 158
JOSHI A., 133
JOSSO A., 119
JOURDAN F., i
JOURDAN L., ii
JOURDREN L., 111–114, 129
JOURNOT L., 106

KACZMAREK A., 53
KADOUCHE D., 152
KAMINSKI R., 36
KAYAL S., 190
KELDER T., 107
KEMPSTER C., 115
KENNEDY S., 60
KILENS S., 123
KIRK P., 115
KLOPP C., 116, 155
KOPP M., i
KOUGBEADJO A., 150
KOURLAIEV A., 181
KOUTERO M., 54
KRAMMER E.-M., 196
KREPLAK J., 150
KRESS A., 153
KREUZHUBER R., 115
KUCHLY C., 155

LAANISTE L., 36
LABARRE A., 193
LACROIX T., 165
LACROIX V., i
LAGARDE N., 65
LAGOUTTE L., 18, 20
LAJUS A., 119
LAMBOURNE J., 115
LANGLEY S., 36
LANGLOIS J., 119
LAO J., 161
LARDEUX F., 187
LARMANDE P., 58
LAURENT B., 65
LAURENT Y., 200
LAVENIER D., 3, 170
LE BRAS Y., 58
LE BÉGUEC C., 18, 20
LE CHATELIER E., 162, 178
LE CROM S., 111-114, 129
LE FRESNE-LANGUILLE S., 199
LE GRAND R., 28
LE VACON F., 199
LEBLOND A., 163
LEBRETON A., 164
LECHAT P., 54
LECLERC J., 159
LECLERQ M., 128
LECLÈRE V., i, ii, 175, 176
LECOCQ M., 82
LECOMPTE L., 130
LECOMPTE O., 153
LECROQ T., 67
LEFEBVRE A., 67
LEFEBVRE M., 57
LEFEUVRE P., 146
LEFORT V., 63, 148
LEGEAI F., 18, 56, 177
LEGENDRE R., 60
LEHMANN N., 129
LEJZEROWICZ F., 183
LELÈVRE A., 157
LELÈVRE Y., 118, 123
LEMAITRE C., i, 3, 170, 177
LEMOINE F., 58
LEMOINE G., 128
LEMOINE S., 111-114
LENSINK M., i, 137, 196
LERMINE A., 143
LESAGE-DESCAUSES M.-C., 158
LEUILLET S., 199
LEVY N., 38
LEWITUS E., 156
LHOUSSAINE C., i, I, 53

LI L., 100
LIMASSET A., 32, 130
LIN C., 44
LISACEK F., 176
LLAMOSI A., 53
LOPES A., i, 188
LOPEZ C., 138
LORENZO J., 61, 154, 165
LORGOUILLOUX K., 137
LOUIS A., 98
LOUIS K., 191
LOUTRET B., 163
LOUVEL G., 156
LOUX V., 6
LURIN C., 142
LÉONARD P., 162, 178
MACH N., 108
MAGRANGEAS F., 109, 110
MAILLET N., 54
MALLET L., 75, 192
MAMAN S., 116
MAMMAR H., 125
MANCHERON A., 132
MANDIN P., 144
MANNO M., 155
MANOUVRIER-HANU S., 166
MANTSOKI A., 133
MARCHET C., 32, 130
MAREUIL F., 58, 63
MARGUERON R., 127
MARIADASSOU M., 3, 6, 161
MARIJON P., ii, 171, 172
MAROT G., ii, 173
MARSCHALL T., 30
MARTIN M., 80, 168
MARTINS C., 183
MASLIAH PLANCHON J., 125
MATIAS C., 31
MAYJONADE B., 155
MAZZUFERI M., 36
MCCOY K., 163
MCKINNEY H., 115
MEDVEDEV P., 170
MEISTERMANN D., 123
MENEZES BRAGANCA N., 174
MENG A., 130
MERCIER J., 119
MESLET-CLADIÈRE L., 164
MICHALIK J., 175
MINVIELLE S., 109, 110
MISTOU M.-Y., 145
MOHAMMAD A., 112
MOISAN A., 192
MONFORT M., 77
MOREAU P., 109
MORENO-MORAL A., 36
MORISSE P., 67
MORLON H., 156
MOUGEL C., 177
MOULIN C., 189
MOUMEN B., 124
MOURAD R., 100
MOUSCAZ Y., 191
MOYON L., 104
MROZ F., 53
MUCCHIELLI M.-H., 188
MÉDIGUE C., 31, 119, 120
MÉNAGER H., 58

NAVARRO C., 38
NECSULEA A., 131
NEF S., 126
NEIRJINCK Y., 126
NEVERS Y., 153
NGUYEN N., 98
NIGHTINGALE A., 80
NIKOSLKI M., i
NIN S., 106
NOÉ L., ii, 159

ODELIN G., 38
ORTEGA O., 138
OTT A., 136, 201
OUADAHI A., 183
OUDART A., 82, 90
OULAS A., 182
OUWEHAND W., 115

PAFILIS E., 182
PARK A., 115
PAULEVÉ L., i
PAUVERT C., 6
PAVLOUDI C., 182
PAWLOWSKI J., 183
PECCOUD J., 124
PEJOSKI D., 28
PELLETIER E., i, 181

PELLETIER S., 157
PEREIRA H., 119, 120
PERES S., 189
PERICARD P., ii, 10
PERRIN A., 31
PERRIN S., 61, 154
PERRIN S., 38
PERRIÈRE G., i
PETERLONGO P., i, 3, 32, 130, 170
PETRETTO E., 36
PETROU S., 36
PEYRET P., i
PFLIEGER D., 122
PIBLE O., 149
PICOT D., 163
PIERROT A., 193
PINA-MARTINS F., 172
PINET F., 107
PION A., 163
PLANEL R., 31, 119, 120
PLANTARD O., 163
POCH O., 153
POIRAUDEAU A., 155
POLVÈCHE H., 135
PONS N., 162, 178
PONTY Y., 102
POULAIN J., 121
POUX C., i, ii
POUX V., 163
PRADAL C., 58
PRUVOST O., 146
PRÉVOST M., 196
PUPIN M., ii, 175, 176

QI G., 168
QUINCE C., 182

RADOMSKI N., 145
RADONJIC M., 107
RADULESCU O., 138
RANCUREL C., 195
RAUSELL A., 79, 103
RAVIGNÉ V., 146
RAYNAL V., 125
REBOUL G., 120
REDDY J., 44
REMY E., 38, 144
RENAULT P., 6, 178
RIALLE S., 106
RICARD A., 108
RICART E., 176
RICHARD D., 146
RICHARD H., i, 159
RICHARDS K., 36
RICHARDSON S., 115
RIGAILL G., 142
RIMBAULT B., 191
RIPP R., 153
RIVALS E., 186
RIZK G., 18, 170
ROBERT C., 108
ROCHA E., 31, 169
ROCHE A., 141
ROCHE D., 119
ROCHER T., ii
ROEST CROLLIUS H., 98, 104, 156
ROGIER O., 158
ROGNIAUX H., 33
ROLLIN ROLLIN J., 119
RONCO P., 184
RONDINEAU J., 109, 110
ROOS G., 196
ROPERS D., i
ROQUES C., 155
ROQUET J., 172, 180
ROULET A., 155
ROUVEIROL C., 45
ROUX F., 155
ROUY Z., 119
RUIZ M., 132

SAAD C., ii, 159
SAAIDI A., 102
SACQUIN-MORA S., 65, 188
SAEZ-RODRIGUEZ J., 35
SAHLIN K., 170
SAINT-ANDRÉ V., 44
SALIN G., 155
SALLOU J., 172, 180
SALSON M., ii
SAND O., ii
SARGUEIL B., 102
SARTER H., 173
SAUBION F., 187
SAUTREUIL M., 140, 141
SAVAGE D., 115
SCHBATH S., 3, 161
SCHIEX T., 192

SCHNETZER J., 182
SCHOENHUTH A., 186
SCHUTZ S., 172
SCHWARTZ L., 189
SCHWEKE H., 188
SCOTT-BOYER M., 128
SEELEUTHNER Y., 121
SEGURA V., 158
SEPOU NGAÏLO A., 61
SERVANT N., i, 117, 127
SEYRES D., 115
SHKURA K., 36
SHOMER I., 192
SIEGEL A., i, 56
SIMON GARCIA P., 78
SIMON J.-C., 177
SINCLAIR L., 182
SLIM L., 103
SMITH L., 126
SMOL T., 166
SOUBIGOU-TACONNAT L., 158
SOUCHET S., 118
SOULA H., i
SOULIÉ M., 148
SRIVASTAVA P., 36
STAM M., 120
STEGLE O., 115
STOMA S., 53
STÉVANT I., 126, 202
SÉJOURNÉ M., 117
SÉNÉ F., 61

TAIB N., 82
TANAKA I., 117
TARIO Z., 125
TCHITCHEK N., 28, 54
TESSIER D., 33
THIEFFRY D., 129, 185
THIRION F., 162
THOMAS-CHOLLIER M., 129, 185
THOUROUDE T., 157
THUILLIER C., 166
TICHIT L., 38
TOUCHON M., 31
TOURNIER L., 189
TOUZET H., i, I, 10, 159
TOUZET P., i, ii
TROUNE E., 172
TYAGI N., 168
TÉLETCHÉA S., 62

VAGNER S., 117

VAISSIÉ P., 199

VALDEOLIVAS A., 38

VALLENET D., 31, 119, 120

VAMPARYS L., 65

VAN HELDEN J., i, 185

VANDECASTEELE C., 155

VANDENBOGAERT M., 179

VANDENBROUCK Y., 122

VANDEWALLE V., i

VARRÉ J.-S., ii, 171

VAULOUP-FELLOUS C., 136

VERGNE N., 141

VERHEYDEN H., 163

VERSARI C., i, 53

VIALETTE S., 193

VIART B., 120

VIAUTOUR Q., 167

VIDAL M., 155

VILA NOVA M., 145

VILLET R., 191

VINTACHE D., 118

VIRLOGEUX-PAYANT I., 192

VISCO J., 183

VOURC'H G., 163

VRANKEN W., 137

WEIMANN A., 182

WEISZER K., 178

WINCKER P., 2, 121, 181

WUCHER V., 18, 20

YOUNG R., 44

ZANCHETTA C., 155

ZANDER M., 150

ZOUINE M., 155

ZYTNICKI M., 197
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Journées Ouvertes en
Biologie, Informa-
tique et Mathématiques

Lille, 3-6 juillet 2017