Insect genomics to reduce phytosanitary product usage

Through its long term collaboration with INRAE IGEPP, GenScale is involved in various genomic projects in the field of agricultural research. In particular, we participate in the genome assembly and analyses of some major agricultural pests or their natural ennemies such as parasitoids. The long term objective of these genomic studies is to develop control strategies to limit population outbreaks and the dissemination of plant viruses they frequently transmit, while reducing the use of phytosanitary products.

Energy efficient genomic computation through Processing-in-Memory

All current computing platforms are designed following the von Neumann architecture principles, originated in the 1940s, that separate computing units (CPU) from memory and storage. Processing-in-memory (PIM) is expected to fundamentally change the way we design computers in the near future. These technologies consist of processing capability tightly coupled with memory and storage devices. As opposed to bringing all data into a centralized processor, which is far away from the data storage and is bottlenecked by the latency (time to access), the bandwidth (data transfer throughput) to access this storage, and energy required to both transfer and process the data, in-memory computing technologies enable processing of the data directly where it resides, without requiring movement of the data, thereby greatly improving the performance and energy efficiency of processing of massive amounts of data potentially by orders of magnitude. This technology is currently under test in GenScale with a revolutionary memory component developed by the UpMEM company. Several genomic algorithms have been parallelized on UpMEM systems, and we demonstrated significative energy gains compared to FPGA or GPU accelerators. For comparable performances (in terms of execution time) on large scale genomics applications, UpMEM PIM systems consume 3 to 5 times less energy.

7.1.1 SVJedi

• Keywords: High throughput sequencing, Structural Variation, Genome analysis
• Functional Description: SVJedi is a structural variation (SV) genotyper for long read data. Based on a representation of the different alleles, it estimates the genotype of each variant in a given individual sample based on allele-specific alignment counts. SVJedi takes as input a variant file (VCF), a reference genome (fasta) and a long read file (fasta/fastq) and outputs the initial variant file with an additional column containing genotyping information (VCF).
• URL: https://github.com/llecompte/SVJedi
• Contacts: Claire Lemaitre, Lolita Lecompte
• Participants: Claire Lemaitre, Lolita Lecompte, Pierre Peterlongo, Dominique Lavenier

7.1.2 MinYS

• Name: MineYourSymbiont
• Keywords: High throughput sequencing, Genome assembly, Metagenomics
• Functional Description: MinYS allows targeted assembly of a bacterial genome of interest in a metagenomic short read sequencing sample using a reference-guided pipeline. First, taking advantage of a potentially distant reference genome, a subset of the metagenomic reads is assembled into a set of backbone contigs. Then, this first draft assembly is completed using the whole metagenomic readset in a de novo manner. The resulting assembly is output as a genome graph, allowing to distinguish different strains with potential structural variants coexisting in the sample.
• URL: https://github.com/cguyomar/MinYS
• Contacts: Claire Lemaitre, Cervin Guyomar

7.1.3 Simka

• Keywords: Comparative metagenomics, K-mer, Distance, Ecology
• Functional Description: Simka is a comparative metagenomics method dedicated to NGS datasets. It computes a large collection of distances classically used in ecology to compare communities by approximating species counts by k-mer counts. The method scales to a large number of datasets thanks to an efficient and parallel kmer-counting strategy that processes all datasets simultaneoulsy. SimkaMin is distributed also with Simka. SimkaMin is a faster and more resource-frugal version of Simka. It outputs approximate (but very similar) results by subsampling the kmer space.
• Release Contributions: Since release version 1.5.0, SimkaMin is distributed alongside Simka. SimkaMin is also a de novo comparative metagenomics tool. It is a faster and more resource-frugal version of Simka. It outputs approximate (but very similar) results as Simka by subsampling the kmer space. With this strategy, and with default parameters, SimkaMin is an order of magnitude faster, uses 10 times less memory and 70 times less disk than Simka.
• URL: https://gatb.inria.fr/software/simka/
• Publications: hal-01595071, hal-02308101
• Authors: Gaëtan Benoit, Claire Lemaitre, Pierre Peterlongo
• Contacts: Gaëtan Benoit, Claire Lemaitre
• Participants: Claire Lemaitre, Dominique Lavenier, Gaëtan Benoit, Pierre Peterlongo, Charles Deltel

7.1.4 DiscoSnpRad

• Name: DISCOvering Single Nucleotide Polymorphism, Indels in RAD seq data
• Keyword: RAD-seq
• Functional Description:

  Software discoSnpRad is designed for discovering Single Nucleotide Polymorphism (SNP) and insertions/deletions (indels) from raw set(s) of RAD-seq data. Note that number of input read sets is not constrained, it can be one, two, or more. Note also that no other data as reference genome or annotations are needed. The software is composed of several modules. First module, kissnp2, detects SNPs from read sets. A second module, kissreads2, enhances the kissnp2 results by computing per read set and for each variant found i/ its mean read coverage and ii/ the (phred) quality of reads generating the polymorphism. Then, variants are grouped by RAD locus, and a VCF file is finally generated. We also provide several scripts to further filter and select informative variants for downstream population genetics studies.

  This tool relies on the GATB-Core library.

• Release Contributions: * Substantive improvements: better quality of results (accuracy and recall), better filtering of obtained results * Formal improvements: better organization of scripts, better presentation of results
• URL: https://github.com/GATB/DiscoSnp
• Contact: Pierre Peterlongo
• Participants: Pierre Peterlongo, Claire Lemaitre

7.1.5 MTG-link

• Keywords: Bioinformatics, Genome assembly, High throughput sequencing
• Functional Description: MTG-Link is a gap-filling tool for draft genome assemblies, dedicated to linked-read data generated for instance by 10X Genomics Chromium technology. It is a Python pipeline combining the local assembly tool MindTheGap and an efficient read subsampling scheme based on the barcode information of each read. It takes as input a set of reads, a GFA file with gap coordinates and an alignment file in BAM format. It outputs the results in a GFA file.
• URL: https://github.com/anne-gcd/MTG-Link
• Contacts: Claire Lemaitre, Anne Guichard, Fabrice Legeai
• Partner: INRAE

7.1.6 kmtricks

• Keywords: High throughput sequencing, Indexing, K-mer, Bloom filter, K-mers matrix
• Functional Description: kmtricks is a tool suite built around the idea of k-mer matrices. It is designed for counting k-mers, and constructing bloom filters or counted k-mer matrices from large and numerous read sets. It takes as inputs sequencing data (fastq) and can output different kinds of matrices compatible with common k-mers indexing tools. The software is composed of several modules and a library which allows to interact with the module outputs.
• URL: https://github.com/tlemane/kmtricks
• Authors: Teo Lemane, Rayan Chikhi, Pierre Peterlongo
• Contacts: Teo Lemane, Pierre Peterlongo
• Participants: Teo Lemane, Rayan Chikhi, Pierre Peterlongo

7.1.7 ORI

• Name: Oxford nanopore Reads Identification
• Keywords: Bioinformatics, Bloom filter, Spaced seeds, Long reads, ASP - Answer Set Programming, Bacterial strains
• Functional Description: ORI (Oxford nanopore Reads Identification) is a software using long nanopore reads to identify bacteria present in a sample at the strain level. There are two sub-parts in ORI: (1) the creation of the index containing the reference genomes of the interest species and (2) the query of this index with long reads from Nanopore sequencing in order to identify the strain(s).
• URL: https://github.com/gsiekaniec/ORI
• Authors: Gregoire Siekaniec, Teo Lemane, Jacques Nicolas
• Contacts: Gregoire Siekaniec, Jacques Nicolas
• Participants: Gregoire Siekaniec, Teo Lemane, Jacques Nicolas, Emeline Roux

7.1.8 StrainFLAIR

• Name: STRAIN-level proFiLing using vArIation gRaph
• Keywords: Indexation, Bacterial strains, Pangenomics
• Functional Description: StrainFLAIR (STRAIN-level proFiLing using vArIation gRaph) is a tool for strain identification and quantification that uses a variation graph representation of gene sequences. The input is a collection of complete genomes, draft genomes or metagenome-assembled genomes from which genes will be predicted. StrainFLAIR is sub-divided into two main parts: first, an indexing step that stores clusters of reference genes into variation graphs, and then, a query step using mapping of metagenomic reads to infer strain-level abundances in the queried sample.
• URL: https://github.com/kevsilva/StrainFLAIR
• Contacts: Kevin Da Silva, Pierre Peterlongo

8.1.1 Small variant discovery in RAD-like data

Participants: Fabrice Legeai, Claire Lemaitre, Pierre Peterlongo.

Restriction site Associated DNA Sequencing (RAD-Seq) is a technique characterized by the sequencing of specific loci along the genome, that is widely employed in the field of evolutionary biology since it allows to exploit variants (mainly Single Nucleotide Polymorphism—SNPs) information from entire populations at a reduced cost, and without relying on a reference genome. Common RAD dedicated tools are based on all-vs-all read alignments, which require consequent time and computing resources. We present an original method, DiscoSnp-RAD, that avoids this pitfall since variants are detected by looking for specific motifs in the de Bruijn graph built from the whole read sets. We tested the implementation on simulated datasets of increasing size, up to 1,000 samples, and on real RAD-Seq data from 259 specimens of Chiastocheta flies, morphologically assigned to seven species. DiscoSnp-RAD allowed all individuals to be successfully assigned to their species. Moreover, identified variants succeeded to reveal a within-species genetic structure linked to the geographic distribution. Furthermore, our results show that DiscoSnp-RAD is significantly faster than state-of-the-art tools, in particular on large datasets 20.

In the context of the SPECREP ANR project, DiscoSnpRAD was then used on several mimetic butterfly species. It allowed the comparison of the genetic structure and hybridization patterns of two mimetic butterfly species, Ithomia salapia and Oleria onega (Nymphalidae: Ithomiini), each consisting of a pair of lineages differentiated for their wing colour pattern and that come into contact in the Andean foothills of Peru. Our results highlight major differences, both at the genomic and phenotypic level, between the two species and contrast with the genomic patterns observed for other well studied mimetic butterlflies 19.

8.1.2 Structural Variation genotyping

Participants: Dominique Lavenier, Lolita Lecompte, Claire Lemaitre, Pierre Peterlongo.

Structural variations (SV) are genomic variants of at least 50 base pairs (bp) that can be rearranged within the genome and thus can have a major impact on biological processes. Sequencing data from third generation technologies have made it possible to better characterize SVs. One of the problems is the genotyping of variants. It consists in estimating the presence and ploidy or absence of a set of known variants in a newly sequenced individual. Although many SV callers have been published recently, there was no published method to date dedicated to genotyping SVs with this type of data. We present here a novel method and its implementation, SVJedi, to genotype SVs with long reads. From a set of known SVs and a reference genome, our approach first generates local sequences representing the two possible alleles for each SV. Long read data are then aligned to these generated sequences and a careful analysis of the alignments consists in identifying only the informative ones to estimate the genotype for each SV. SVJedi achieves high accuracy on simulated and real human data and we demonstrate its substancial benefits with respect to other existing approaches, namely SV discovery with long reads and SV genotyping with short reads 24.

8.1.3 Genome assembly of targeted organisms in metagenomic data

Participants: Wesley Delage, Fabrice Legeai, Claire Lemaitre.

In this work, we propose a two-step targeted assembly method tailored for metagenomic data, called MinYS (for MineYourSymbiont). First, a subset of the reads belonging to the species of interest are recruited by mapping and assembled de novo into backbone contigs using a classical assembler. Then an all-versus-all contig gap-filling is performed using a novel version of MindTheGap 8 with the whole metagenomic dataset. The originality and success of the approach lie in this second step, that enables to assemble the missing regions between the backbone contigs, which may be regions absent or too divergent from the reference genome. The result of the method is a genome assembly graph in gfa format, accounting for the potential structural variations identified within the sample. We showed that MinYS is able to assemble the Buchnera aphidicola genome in a single contig in pea aphid metagenomic samples, even when using a divergent reference genome. It runs at least 10 times faster than classical de novo metagenomics assemblers and it is able to recover large structural variations co-existing in a sample 23.

8.1.4 Genome gap-filling with linked-read data

Participants: Anne Guichard, Fabrice Legeai, Claire Lemaitre.

We developed a novel software, called MTG-link, for filling assembly gaps with linked-read data. In linked-read technologies, such as the 10X Genomics Chromium platform, reads that have been sequenced from the same long DNA molecule (30-50 Kb) can be identified by a small barcode sequence. Thus, these technologies have a great potential for filling the gaps as they provide long-range information while maintaining the power and accuracy of short-read sequencing. Our approach is based on local assembly using our tool MindTheGap 8, and takes advantage of barcode information to reduce the input read set in order to reduce the de Bruijn graph complexity. MTG-Link tests different parameters values for gap-filling, followed by an automatic qualitative evaluation of the assembly. Validation was performed on a set of simulated gaps from real datasets with various genome complexities. It showed that the read subsampling step of MTG-Link enables to get better genome assemblies than using MindTheGap alone. We applied MTG-Link on 12 individual genomes of a mimetic butterfly (H. numata), in the Supergene ANR project context. It significantly improved the contiguity of a 1.3 Mb locus of biological interest 42, 43.

8.2.1 A probabilistic data structure for indexing large sets of kmers

Participants: Lolita Lecompte, Pierre Peterlongo.

Indexing massive data sets is extremely expensive for large scale problems. This work proposes a probabilistic data structure based on a minimal perfect hash function for indexing large sets of keys. Our structure out-competes the hash table for construction and query times and for memory usage, in the case of the indexation of a static set. To illustrate the impact of these performances, we provide two applications based on similarity computation with kmers between collections of sequences, and for which this calculation is an expensive but required operation. In particular, we show a practical case in which other bioinformatics tools fail to scale up the tested data set or provide results of lower quality 26.

8.2.2 Large-scale kmer indexation

Participants: Téo Lemane, Pierre Peterlongo.

Our main contributions are 1/ an efficient method for jointly counting kmers across multiple samples, including a streamlined Bloom filter construction by directly counting hashes instead of kmers; 2/ a novel technique that takes advantage of joint counting to preserve rare kmers present in several samples, improving the recovery of non-erroneous kmers. In addition, our experimental results highlight that the usual yet crude filtering of rare kmers is inappropriate for complex data such as metagenomes.

The work had been done in 2020, the paper was written late 2020 and submitted early 2021.

8.2.3 SimkaMin: subsampling the kmer space for efficient comparative metagenomics

Participants: Claire Lemaitre, Pierre Peterlongo.

SimkaMin is a quick comparative metagenomics tool with low disk and memory footprints, thanks to an efficient data subsampling scheme used to estimate Bray-Curtis and Jaccard dissimilarities. One billion metagenomic reads can be analyzed in less than 3 minutes, with tiny memory (1.09 GB) and disk ($\sim$0.3 GB) requirements and without altering the quality of the downstream comparative analyses, making of SimkaMin a tool perfectly tailored for very large-scale metagenomic projects 12.

8.2.4 Indexing and querying pangenome graphs for strain-level profiling of metagenomic samples

Participants: Kevin Da Silva, Pierre Peterlongo.

In this context, our main contributions are 1/ a full pipeline for predicting genes from bacterial strains and for indexing them in a “variation graph”; 2/ a full pipeline for mapping unknown metagenomic reads on a so-created graph and for characterizing and evaluating the abundances of strains existing in the queried sample; 3/ a proof of concept that variation graphs may be used as a replacement of flat sequences for indexing closely related species or strains, and characterizing a sample at the strain level. These methods are implemented in the software StrainFLAIR.

The work had been done in 2020, the paper was written late 2020 and submitted early 2021.

8.3.1 Linked-read study

Participants: Dominique Lavenier.

Considering a set of intervals on the real line, an interval graph records these intervals as nodes and their intersections as edges. Identifying pairs of nodes in an interval graph results in a multiple-interval graph. Given only the nodes and the edges of the multiple-interval graph without knowing the underlying intervals, we are interested in the following questions. Can one determine how many intervals correspond to each node? Can one compute a walk over the multiple-interval graph nodes that reflects the ordering of the original intervals? These questions are closely related to linked-read DNA sequencing, where barcodes are assigned to long molecules whose intersection graph forms an interval graph. Each barcode may correspond to multiple molecules, which complicates downstream analysis, and corresponds to the identification of nodes of the corresponding interval graph. Resolving the above graph-theoretic problems would facilitate the analyses of linked-read sequencing data, through enabling the conceptual separation of barcodes into molecules and providing, through the molecule order, a skeleton for accurately assembling the genome. Here, we propose a framework that takes as input an arbitrary intersection graph and constructs a heuristic approximation of the ordering of the original intervals 30. This research is done in collaboration with R. Chikhi, Pasteur Institute.

8.3.2 Hardness results of some pattern mining problems

Participants: Garance Gourdel.

We initiate a study on the fundamental relationship between data sanitization (i.e., the process of hiding confidential information in a given dataset) and frequent pattern mining, in the context of sequential (string) data to address the fact that current methods introduce a number of spurious patterns that may harm the utility of frequent pattern mining. We present several hardness results on variants of the problem, essentially showing that these variants cannot be solved or even be approximated in polynomial time and we propose integer linear programming formulations to solve them, which work in polynomial time under certain realistic assumptions on the problem parameters. Those solutions have been implemented and tested 32.

8.4.1 Integer Linear Programming for de novo long read assembly

Participants: Rumen Andonov, Victor Epain, Dominique Lavenier.

Long read sequencing technologies offer the possibility to overcome the major issue in genome assembly, namely the genome’s repeated regions. But they suffer from a high error rate, with sequencing errors, like nucleotide insertions or deletions, called indels. Although some long-read assemblers already exist according to several methods, such as using De Bruijn graphs or correcting iteratively the reads for example, we propose here a different approach based on mixed integer linear programming (MILP). This is a two steps strategy : first, we attribute to the maximum of reads a position on a same position axis and then we produce a consensus sequence thanks to the positioning. At these aims, we propose a modelling for the positioning issue with the mixed integer linear programming (MILP), and we present the first ideas for the consensus sequence production and multiple sequences alignment from the positioning, with MILP too. As the final aim of this strategy is to formalize the genome assembly problem, we structured it according to the mathematical method, that permits to target methodological choices precisely, and then reducing the heuristic uses. Finally, we tested the strategy with bacteria genomes. Despite the fact that positioning results are positive, the consensus results are mitigated but do not remove the potentiality of the method association 41, 38.

8.4.2 Genome haplotyping with short paired reads

Participants: Rumen Andonov, Mohammed Amin Madoui, Pierre Peterlongo, Kerian Thuillier.

We propose two new approaches for haplotype-aware genome assembly based on Mixed Integer Linear Programming for multicommodity-flow problems. Unlike previous approaches, we use a global optimization scheme and avoid the use of heuristics. The input data are generated by the DiscoSNP algorithm 9. DiscoSNP is a de novo variant detection tool. Here, a variant is a single nucleotide difference between several haplotypes.

A comparison between both approaches is performed and the advantages and limits of each one are discussed. We also proposed an experimental protocol to test the quality of the solution 45.

8.5.1 Variant detection using a processing-in-memory technology

Participants: Dominique Lavenier.

The concept of Processing-In-Memory aims to dispatch the computer power near the data. Together with the UPMEM company (http://www.upmem.com/), which is currently developing a DRAM memory enhanced with computing units, we parallelized the detection of small mutations on the human genome. Traditionally, this process is split into 2 steps: a mapping step and a variant calling step. Here, thanks to the high processing power of this new type of memory, the mapping step can nearly be done at the disk transfer rate. We have defined an ad-hoc data structure allowing the variant calling step to be performed simultaneously on the host processor. Basically, the two steps are overlapped in such a way that reads are mapped by packet. When a packet is mapped, the mapping results of the previous one dynamically feed the variant calling data structure. Our variant calling implementation on real UPMEM systems show a large speed-up compared to a standard multithreaded software. Compared with other acclerators (GPU or FPGA), the execution time is identical, but energy consumption is strongly reduced 33.

8.6.1 Identification of bacterial strains

Participants: Téo Lemane, Jacques Nicolas, Emeline Roux, Grégoire Siekaniec.

Our aim is to provide rapid algorithms for the identification of bacteria at the finest taxonomic level. We have developed an expertise in the use of the MinION long read technology and have produced and assembled many genomes for lactic bacteria in cooperation with INRAE STLO, which have been made publicly available on the NCBI (https://www.ncbi.nlm.nih.gov/) and on the Microscope platform at Genoscope (http://www.genoscope.cns.fr/agc/microscope/). We have developed a first classifier called ORI (Oxford nanopore Reads Identification) that demonstrates the possibility to identify isolated strains with spaced seed indexing of the noisy long reads produced by the MinION. 44

8.6.2 Studying the sequencing error profile of the nanopore sequencer

Participants: Clara Delahaye, Jacques Nicolas.

We are working on assigning the reads of a sample to their native haplotype for organisms of known polyploidy, sequenced with long read technology (Oxford Nanopore's MinION). As a first step to separate true variants from sequencing errors, we have studied the profile of sequencing errors for bacterial and human datasets. We showed that GC content is a decisive factor linked to sequencing errors. In particular, low-GC reads have almost 2% fewer errors than high-GC reads. Our work highlighted that for repeated regions (homopolymers or regions with short repeats), being the source of about half of all sequencing errors, the error profile also depends on the GC content and shows mainly deletions, although there are some reads with long insertions. Another interesting finding is that the quality measure offers valuable information on the error rate as well as the abundance of reads 40.

8.6.3 Information archiving on DNA molecules

Participants: Olivier Boulle, Dominique Lavenier, Jacques Nicolas, Emeline Roux.

In 2020, we started a new research project, called dnarXiv, whose aim is to investigate the archiving of information on DNA molecules. Indeed, DNA offers the advantage of a storage density of 1,000 to 10,000 times greater than current technologies. The objective is to set up a prototype to demonstrate the feasibility of this new storage possibility based on the latest bio-technologies: enzymatic synthesis (writing information) and sequencing nanopore (reading information). From a numerical point of view, the research axes focus on how to encode information inside DNA molecules and on how to ensure their safety. This project is supported by the Labex CominLab (https://project.inria.fr/dnarxiv/) and by an Inria's Eploratory Action.

8.7.1 Evaluation of error correction tools for long read data

Participants: Lolita Lecompte, Pierre Peterlongo.

8.7.2 Evaluation of insertion variant callers on real human data

Participants: Wesley Delage, Claire Lemaitre.

Insertion variants are one of the most common types of structural variation. Although such variants have many biological impacts on species evolution and health, they have been understudied because they are very difficult to detect with short read re-sequencing data. Recently, with the commercialization of novel long read technologies, insertion variants are finally being discovered and referenced in human populations. Thanks to several international efforts, some gold standard call sets have been produced in 2019, referencing tens of thousands insertions. On these datasets, all existing short-read insertion variant callers, including our own method MindTheGap 8, can reach at most 5 to 10 % of the referenced insertion variants. In this work, we propose a precise characterization of the different types of insertion variants, based on the nature and size of their inserted sequence, the genomic context of their insertion site and the complexity at their breakpoints. In a detailed benchmark, we then analyze which of these features impact most the recall of existing methods. By simulating the identified factors of difficulty, we investigate the causes of low recall and how these can be bypassed or improved in existing algorithms. 14, 31

8.7.3 Artificial Intelligence and Bioinformatics

Participants: Jacques Nicolas.

We took part in the writing of an ambitious book on artificial intelligence in 3 volumes, "A Guided Tour of Artificial Intelligence Research", edited by P. Marquis, O. Papini, and H. Prade. The chapter we wrote shines a light on the strong links shared by Artificial intelligence and Bioinformatics since many years. We introduced a selection of the challenging problems for Artificial intelligence offered by Bioinformatics as well as an extensive bibliography of the state-of-the-art. Together with the framing of questions, we point to several achievements and progresses made in the literature with the hope it can help the bioinformatician, bioanalyst or biologist to have access to state of the art methods 34.

8.8.1 Genome assembly and analysis of a parasitic wasp and its integrated viral genome

Participants: Fabrice Legeai, Claire Lemaitre.

We used Illumina paired-end and mate-pair reads to assemble the genome of a parasitic wasp, Hyposoter didymator, carrying integrated viral particules, and we annotated it with RNA-Seq data. We identified all the viral insertions in the genome and located precisely the excision sites of a set of circularized viral sequences by developing a specific method called DrjBreakpointFinder and freely distributed at http://github.com/stephanierobin/DrjBreakpointFinder/. Finally we studied the colinearity between Hyposoter didymator and the genome of another ichnovirus carrying wasp (Campoletis sonorensis). These analyses and comparisons of the viral insertion structures in these two wasps helped elucidating the mechanisms that have facilitated viral domestication in ichneumonid wasps. Their genomic architectures clearly differ from the organization of viral insertions in braconid wasps, revealing different evolutionary trajectories that have led to virus domestication in the two wasp lineages 25.

8.8.2 Genomics of agro-ecosystems insects

Participants: Fabrice Legeai.

Through its long term collaboration with INRAE IGEPP, and its support to the BioInformatics of Agroecosystems Arthropods platform (http://bipaa.genouest.org), GenScale is involved in various genomic projects in the field of agricultural research. In particular, we participated in the genome assembly and analyses of some major agricultural pests or their natural ennemies such as parasitoids. In most cases, the genomes and their annotations were hosted in the BIPAA information system, allowing collaborative curation of various set of genes and leading to novel biological findings.

Concerning aphid organisms, we conducted various analyses including differential gene expression, genomics diversity from populations, and epigenomics variability among morphs of pea aphids to study the effect of selection of genes duplications all along the pea aphid genome 18. We participated to the characterization of secreted KQY proteins, a very peculiar family of genes including a very high number of small exons sparsely located on the aphids genomes and transcribed in multiple isoforms 17. We also participated in the annotation of the aphid Aphis glycines, a major pest of the soybean 22.

The fall armyworm (Spodoptera frugiperda) is one of the most damaging pest insects of different classes of crop plants. We produced a new release of its genome using novel data (long reads and Hi-C data) and migrated the annotation from the previous release. It allows our collaborators to annotate structural variants (CNVs, mainly duplications and deletions) among broad populations of moths and to study the effect of the selection on the CNV harboring detoxifaction genes related to insecticide resistance 21.

We assembled the genome of Aphidius ervi a parasitic wasp of aphids. We used a hybrid approach mixing short reads (Illumina paired-end and mate-pair reads) and long PacBio reads. Then we identified the genes and transposable elements in the resulting scaffolds and performed the same analyses to the genome of Lysiphlebus fabarum, assembled by collaborators. These two genomes have provided insights into adaptive evolution in parasitoids that infect aphids 15.

We reported the genome of Daktulosphaira vitifoliae, the grape phylloxera. We used a similar hybrid approach mixing short and long PacBio reads for genome assembly and we annotated the genes and transposable elements. We identified an interesting expansion of a family of effector genes. Various population genomics were also performed, allowing us to test various demographic scenarios for the introduction of phylloxera in Europe. We conclude that phylloxera populations of the upper Mississippi River basin, feeding on the wild species Vitis riparia, are likely to be the principal source of the invasion to Europe 28.

8.8.3 Structural genome analysis of S. pyogenes strains

Participants: Dominique Lavenier, Thomas Rigole, Emeline Roux.

The S. pyogenes bacteria is responsible for many human infections. With the increase in the prevalence of infections (750 million infections per year worldwide and 4th in terms of mortality from bacterial infection), a better understanding of adaptive and evolutionary mechanisms at play in this bacteria is essential. The molecular characterization of the different strains is done by the emm gene. A statistical analysis of the different types of emm on the Brittany population shows 3 main dynamics: sporadic types, endemic types or epidemic types. The last case was observed in Brittany for the type emm75 between 2009 and 2017. Two hypotheses can be considered: (1) the emergence of a new subtype or winning clone in an unimmunized population; (2) increased pathogenicity through genetic evolution of the strains, including the acquisition of new virulence factors. In collaboration with the microbiology department of the Rennes Hospital, we sequenced more than 30 S. pyogenesemm75 strains (Oxford Nanopore MinION sequencing) in order to study the dynamic of the epidemic thought their structural genomic variations 16.

8.8.4 Logical reasoning to study metabolic pathways

Participants: Jacques Nicolas.

Inferring genome-scale metabolic networks in emerging model organisms is challenged by incomplete biochemical knowledge and partial conservation of biochemical pathways during evolution. A given phenotype can be conserved even if the underlying molecular mechanisms are changing. We formalized in a logical framework (Answer Set Programming) the inference of new reactions and molecular structures, based on previous biochemical knowledge. We have developed for this purpose a form of analogical reasoning that has allowed us to abstract and extend the known molecular transformations. This method was applied to study the metabolic pathway drift in the red algal model Chondrus crispus 11.

8.8.5 Detection of cytosine methylation of mature microRNAs

Participants: Pierre Peterlongo.

Literature reports that mature microRNA (miRNA) can be methylated at adenosine, guanosine and cytosine. However, the molecular mechanisms involved in cytosine methylation of miRNAs have not yet been fully elucidated. Here, we investigated the biological role and underlying mechanism of cytosine methylation in miRNAs in glioblastoma multiforme (GBM). The GenScale team provided the algorithmic solution and implementation for the detection of methylated cytosine on mature microRNAs 13.

8.8.6 Comparing seawater metagenomes from the Tara ocean project

Participants: Claire Lemaitre, Pierre Peterlongo.

GenScale team members have participated to the developement of algorithms that enable such large scale sequencing data comparisons, and they provided their expertise regarding the results and their analyses.

10.1.1 Visits of international scientists

• Visit of Zeyuan Chen from Ludwig-Maximilians-Universitat, Munich, Germany, January 2020

10.2.1 Collaborations in European programs, except FP7 and H2020

10.2.2 Collaborations with major European organizations

• Two years France-Bulgaria bilateral Partnership Hubert Curien (PHC) RILA 2019 (project code : 43196Q). The topic of this project is "Integer Programming Approaches for Long-Reads Genome Assembly". Start year: 2019.

10.3.1 ANR

10.3.2 PIA: Programme Investissement d’Avenir

10.3.3 Programs from research institutions

Labex CominLabs: Project DNA-Store: Advanced error correction scheme for DNA-based data storage using nanopore technology

Participants: Dominique Lavenier, Emeline Roux.

• Coordinator: L. Conde-Canencia (UBS, Lab-STCC, IAS)
• Duration: 12 months (Feb. 2019 - Feb. 2020)
• Partners: UBS (Lab-STCC, IAS, L. Conde-Canencia)
• Description: The DNA-Store project is funded by the Labex CominLabs. The goal is to explore the possibility to store information on DNA molecules. As DNA sequencing (the reading process) is performed with the Oxford Nanopore technology, powerful error correcting codes need to be developed together with dedicated genomic data processing.

Labex CominLabs: dnarXiv, archiving information on DNA molecules

Participants: Olivier Boulle, Dominique Lavenier, Jacques Nicolas, Emeline Roux.

• Coordinator : D. Lavenier
• Duration : 39 months (Oct. 2020, dec. 2023)
• Description: This CominLabs project is the follow-up of the DNA-Store CominLabs project, and it has the same objective as the Inria's Exploratory Action. This goal is to develop a large-scale multi-user DNA-based data storage system that is reliable, secure, efficient, affordable and with random access. For this, two key promising biotechnologies are considered: enzymatic DNA synthesis and DNA nanopore sequencing. Advanced solutions will be proposed in terms of coding schemes (i.e., source and channel coding) and data security (i.e., data confidentiality/integrity and DNA storage authenticity), considering the constraints and advantages of the chemical processes and biotechnologies involved in DNA storage.

Project Thermin: Differential characterization of strains of a bacterial species, Streptococcus thermophilus, with a Nanopore MinION

Participants: Dominique Lavenier, Jacques Nicolas, Emeline Roux, Grégoire Siekaniec.

• Coordinator: J. Nicolas (Inria/Irisa, GenScale, Rennes)
• Duration: 36 months (Oct. 2018 – Sept. 2021)
• Partners: INRAE (STLO, Agrocampus Rennes, E. Guédon and Y. Le Loir).
• Description: The Thermin project aims at exploring the capacities of a low cost third generation sequencing device, the Oxford Nanopore MinION, for rapid and robust pan-genome discrimination of bacterial strains and their phenotypes. It started with the recruitments of E. Roux (délégation INRIA, Oct, 2018), a biochemist from Lorraine University, and G. Siekaniec (INRAE -INRIA collaboration, INRAE grant), a new PhD student. We study pan-genomic representations of multiple genomes and the production of characteristic signatures of each genome in this context.

11.1.1 Scientific events: organisation

• The GenScale team organized and hosted the international workshop DSB 2020: "Data Structure in Bioinformatics".

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Rencontres transdiscipliaires Technologies et Santé : ADN, polymères et bigdata, oct. 2020, Paris: "Archivage d’information sur ADN" [D. Lavenier]
• Sémimaire équipe Bonsai, Cristal, Lille, mars 2020: "Post Processing of Minion Data" [D. Lavenier]
• Microbiomes Day, Oct 1st 2020, Rennes: "Identification and quantification of strains in a strain mixture using variation graphs" [K. Da Silva]
• International conference Bioinformatics: from Algorithms to Applications: Biata jul 2020, St Petersburg. Keynote Speaker: "Indexing large and numerous sequencing datasets" [P. Peterlongo]

11.1.5 Leadership within the scientific community

• Members of the Scientific Advisory Board of the CNRS Research group GDR BIM (National Research Group in Molecular Bioinformatics) [P. Peterlongo, C. Lemaitre]
• Animator of the Sequence Algorithms axis (GT seqBIM) of the CNRS Research groups GDRs BIM and IM (National Research Groups in Molecular Bioinformatics and Informatics and Mathematics respectively) [C. Lemaitre]
• Animator of the INRAE Center for Computerized Information Treatment "BARIC" [F. Legeai]

11.1.6 Scientific expertise

• Expert for the MEI (International Expertise Mission), French Research Ministry [D. Lavenier]
• Member of the Scientific Council of BioGenOuest [D. Lavenier]
• Member of the Scientific Council of Agrocampus Ouest (Institute for life, food and horticultural sciences and landscaping) [J. Nicolas]

11.1.7 Research administration

• Member of the CoNRS, section 06, [D. Lavenier]
• Member of the CoNRS, section 51, [D. Lavenier]
• Corresponding member of COERLE (Inria Operational Committee for the assesment of Legal and Ethical risks). Participation to the ethical group of IFB (French Elixir node, Institut Français de Bioinformatique) [J. Nicolas]
• Scientific Advisor of The GenOuest Platform (Bioinformatics Resource Center BioGenOuest) and deputy representative of Inria for the grouping council that defines Biogenouest's strategy and ensures the consistency of its actions. [J. Nicolas]
• Representative of the environnemental axis of the IRISA UMR [C. Lemaitre]
• In charge of the bachelor's degree in the computer science department of University of Rennes 1 (120 students) [R. Andonov]

11.2.1 Teaching

• Licence : C. Delahaye, Python, 12 h, L1, Univ. Rennes 1, France.
• Licence : R. Andonov, V. Epain, Graph Algorithms, 100h, L3, Univ. Rennes 1, France.
• License : G. Gourdel, Python, 48h, L2 MIASH, Univ. Paris 1, France.
• Master : R. Andonov, V. Epain, Operational research, 82h, M1 Miage, Univ. Rennes 1, France.
• Master : G. Siekaniec, Python, 24 h, M1, Univ. Rennes 1, France.
• Master : C. Delahaye, C. Lemaitre, P. Peterlongo, Algorithms on Sequences, 52h, M2, Univ. Rennes 1, France.
• Master : C. Lemaitre, T. Lemane, Bioinformatics of Sequences, 40h, M1, Univ. Rennes 1, France.
• Master : P. Peterlongo, Experimental Bioinformactics, 24h, M1, ENS Rennes, France.
• Master : F. Legeai, RNA-Seq, Metagenomics and Variant discovery, 10h, M2, National Superior School Of Agronomy, Rennes, France.
• Master : R. Andonov, Advanced Algorithmics, 25h, Univ. Rennes 1, France.
• Master : D. Lavenier, Memory Efficient Algorithms for Big Data, 24h, Engineering School, ESIR, Rennes.

11.2.2 Supervision

• PhD : L. Lecompte, Structural Variant genotyping with long-read sequencing data, Université de Rennes, 04/12/2020 36.
• PhD : W. Delage, Characterization and detection of large constitutional insertions for medical use, Université de Rennes, 11/12/2020 35.
• PhD in progress: K. da Silva, Metacatalogue : a new framework for intestinal microbiota sequencing data mining, 01/10/2018, M. Berland, N. Pons and P. Peterlongo.
• PhD in progress: G. Siekaniec, Differential characterization of strains of bacterial species, 01/10/2018, E. Guédon, E. Roux and J. Nicolas.
• PhD in progress: C. Delahaye, Robust interactive reconstruction of polyploid haplotypes, 01/10/2019, J. Nicolas
• PhD in progress: T. Lemane, unbiased detection of neurodegenerative structural variants using k-mer matrices, 01/10/2019, P. Peterlongo
• PhD in progress: V. Epain, Genome assembly with long reads, 01/10/2020 D. Lavenier, R. Andonov, JF Gibrat
• PhD in progress: G. Gourdel, Sketch-based approaches to processing massive string data, 01/09/2020, P. Peterlongo, T. Starikovskaya
• PhD in progress: L. Robidou, Search engine for genomic sequencing data, 01/10/2020, P. Peterlongo

11.2.3 Juries

• Referee of Habilitation thesis jury.
  • D. Vallenet, Univ Evry-Val-d'Essonne [P. Peterlongo]
• Member of PhD thesis jury.
  • Guillaume Gautreau, Univ Paris-Saclay [C. Lemaitre],
  • Lolita Lecompte, Univ Rennes [D. Lavenier, C. Lemaitre],
  • Wesley Delage, Univ Rennes [C. Lemaitre],
  • Nicolas Bloyet, Univ Bretagne Sud [R. Andonov]
• Member of PhD thesis committee.
  • Benoit Goutorbe, Univ Paris-Saclay [C. Lemaitre],
  • Hugo Talibart, Univ Rennes [J. Nicolas],
  • B. Hamoum, Univ Bretagne Sud [D. Lavenier]
  • B. Churchward, Univ Nantes [D. Lavenier]
  • C. Nguyen Lam, Univ Bretagne Occidentale [D. Lavenier]
  • N. Dang, Univ Montpellier [D. Lavenier]

11.3.1 Internal or external Inria responsibilities

• Member of the Interstice editorial board [P. Peterlongo]

11.3.2 Articles and contents

• Contribution to a small book of the CNRS research group in artificial intelligence (GDR IA) providing an elementary introduction to artificial intelligence, the diversity of its techniques and the variety of simulated intelligent capabilities. Our own contribution focuses on the analysis of the links between bioinformatics and AI 46.

11.3.3 Interventions

• "Chaplotype", presented at Sciences en Courts, a local contest of popularization movies made by PhD students (http://sciences-en-courts.fr/) [C. Delahaye, T. Lemane].
• L Codent L Créent : promoting programming for secondary schoolgirls during workshops supervised by female PhD students (http://lclc-rennes.irisa.fr/) [C. Delahaye].
• Fête de la science 2020, presentation of algorithmic solutions proposed for comparing seawater metagenomes. "Sciences en Direct, un ocean de richesses" [P. Peterlongo].

11.3.4 Internal actions

• Organization of Sciences en cour[t]s events, Nicomaque association (http://sciences-en-courts.fr/) [W. Delage, G.Siekaniec]

International journals

• 10 articleJ. Alanko, H. Bannai, B. Cazaux, P. Peterlongo and J. Stoye. Finding all maximal perfect haplotype blocks in linear timeAlgorithms for Molecular BiologyFebruary 2020, 1-9
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• 11 articleA. Belcour, J. Girard, M. Aite, L. Delage, C. Trottier, C. Marteau, C.-J. Leroux, S. Dittami, P. Sauleau, E. Corre, J. Nicolas, C. Boyen, C. Leblanc, J. Collén, A. Siegel and G. Markov. Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway DriftiScience232February 2020, 100849
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• 12 articleG. Benoit, M. Mariadassou, S. Robin, S. Schbath, P. Peterlongo and C. Lemaitre. SimkaMin: fast and resource frugal de novo comparative metagenomicsBioinformatics364February 2020, 1-2
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• 13 articleM. Cheray, A. Etcheverry, C. Jacques, R. Pacaud, G. Bougras-Cartron, M. Aubry, F. Denoual, P. Peterlongo, A. Nadaradjane, J. Briand, F. Akcha, D. Heymann, F. Vallette, J. Mosser, B. Ory and P.-F. Cartron. Cytosine methylation of mature microRNAs inhibits their functions and is associated with poor prognosis in glioblastoma multiformeMolecular Cancer1912020, 1-36
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• 14 article W. Delage, J. Thevenon and C. Lemaitre. Towards a better understanding of the low recall of insertion variants with short-read based variant callers BMC Genomics 21 762 November 2020
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• 15 articleA. Dennis, G. Ballesteros, S. Robin, L. Schrader, J. Bast, J. Berghöfer, L. Beukeboom, M. Belghazi, A. Bretaudeau, J. Buellesbach, E. Cash, D. Colinet, Z. Dumas, M. Errbii, P. Falabella, J.-L. Gatti, E. Geuverink, J. Gibson, C. Hertaeg, S. Hartmann, E. Jacquin-Joly, M. Lammers, B. Lavandero, I. Lindenbaum, L. Massardier-Galatà, C. Meslin, N. Montagné, N. Pak, M. Poirié, R. Salvia, C. Smith, D. Tagu, S. Tares, H. Vogel, T. Schwander, J.-C. Simon, C. Figueroa, C. Vorburger, F. Legeai and J. Gadau. Functional insights from the GC-poor genomes of two aphid parasitoids, "Aphidius ervi" and "Lysiphlebus fabarum"BMC Genomics21Art. 376December 2020, 1-27
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• 16 articleM. Devaere, S. Boukthir, S. Moullec, E. Roux, D. Lavenier, A. Faili and S. Kayal. Complete Genome Sequences of Two Strains of Streptococcus pyogenes Belonging to an Emergent Clade of the Genotype emm89 in Brittany, FranceMicrobiology Resource Announcements9112020, e00129-20
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• 17 article M. Dommel, J. Oh, J. Huguet-Tapia, E. Guy, H. Boulain, A. Sugio, M. Murugan, F. Legeai, M. Heck, M. Smith and F. White. Big Genes, Small Effectors: Pea Aphid Cassette Effector Families Composed From Miniature Exons Frontiers in Plant Science 11 September 2020
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• 18 articleR. Fernández, M. Marcet-Houben, F. Legeai, G. Richard, S. Robin, V. Wucher, C. Pegueroles, T. Gabaldón and D. Tagu. Selection following Gene Duplication Shapes Recent Genome Evolution in the Pea Aphid Acyrthosiphon pisumMolecular Biology and Evolution379September 2020, 2601-2615
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• 19 articleJ. Gauthier, D. De‐Silva, Z. Gompert, A. Whibley, C. Houssin, Y. Le Poul, M. McClure, C. Lemaitre, F. Legeai, J. Mallet and M. Elias. Contrasting genomic and phenotypic outcomes of hybridization between pairs of mimetic butterfly taxa across a suture zoneMolecular Ecology297April 2020, 1328-1343
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• 20 articleJ. Gauthier, C. Mouden, T. Suchan, N. Alvarez, N. Arrigo, C. Riou, C. Lemaitre and P. Peterlongo. DiscoSnp-RAD: de novo detection of small variants for RAD-Seq population genomicsPeerJJune 2020, 1-20
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• 21 articleS. Gimenez, H. Abdelgaffar, G. Goff, F. Hilliou, C. Blanco, S. Hänniger, A. Bretaudeau, F. Legeai, N. Nègre, J. Jurat-Fuentes, E. D'Alençon and K. Nam. Adaptation by copy number variation increases insecticide resistance in the fall armywormCommunications Biology31December 2020, 664
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• 22 articleR. Giordano, R. Donthu, A. Zimin, I. Julca Chavez, T. Gabaldon, M. Van Munster, L. Hon, R. Hall, J. Badger, M. Nguyen, A. Flores, B. Potter, T. Giray, F. Soto-Adames, E. Weber, J. Marcelino, C. Fields, D. Voegtlin, C. Hill, G. Hartman, T. Akraiko, A. Aschwanden, A. Avalos, M. Band, B. Bonning, A. Bretaudeau, O. Chiesa, A. Chirumamilla, B. Coates, G. Cocuzza, E. Cullen, P. Desborough, B. Diers, C. DiFonzo, G. Heimpel, T. Herman, Y. Huanga, J. Knodel, C.-C. Ko, G. Labrie, D. Lagos-Kutz, J.-H. Lee, S. Lee, F. Legeai, M. Mandrioli, G. Manicardi, E. Mazzoni, G. Melchiori, A. Micijevic, N. Miller, A. Nasuddin, B. Nault, M. O'Neal, M. Panini, M. Pessino, D. Prischmann-Voldseth, H. Robertson, H. Song, K. Tilmon, J. Tooker, K. Wu and S. Zhan. Soybean aphid biotype 1 genome: Insights into the invasive biology and adaptive evolution of a major agricultural pestInsect Biochemistry and Molecular Biology120May 2020, 103334
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• 23 articleC. Guyomar, W. Delage, F. Legeai, C. Mougel, J.-C. Simon and C. Lemaitre. MinYS: mine your symbiont by targeted genome assembly in symbiotic communitiesNAR Genomics and Bioinformatics23September 2020, 1-11
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• 24 articleL. Lecompte, P. Peterlongo, D. Lavenier and C. Lemaitre. SVJedi: genotyping structural variations with long readsBioinformatics3617September 2020, 4568-4575
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• 25 articleF. Legeai, B. Santos, S. Robin, A. Bretaudeau, R. Dikow, C. Lemaitre, V. Jouan, M. Ravallec, J.-M. Drezen, D. Tagu, F. Baudat, G. Gyapay, X. Zhou, S. Liu, B. Webb, S. Brady and A.-N. Volkoff. Genomic architecture of endogenous ichnoviruses reveals distinct evolutionary pathways leading to virus domestication in parasitic waspsBMC Biology181December 2020, 1-23
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• 26 article C. Marchet, L. Lecompte, A. Limasset, L. Bittner and P. Peterlongo. A resource-frugal probabilistic dictionary and applications in bioinformatics Discrete Applied Mathematics 92-102 Volume 274 March 2020
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• 27 articleC. Marchet, P. Morisse, L. Lecompte, A. Lefebvre, T. Lecroq, P. Peterlongo and A. Limasset. ELECTOR: evaluator for long reads correction methodsNAR Genomics and Bioinformatics21March 2020, 1-12
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• 28 articleC. Rispe, F. Legeai, P. Nabity, R. Fernández, A. Arora, P. Baa-Puyoulet, C. Banfill, L. Bao, M. Barberà, M. Bouallegue, A. Bretaudeau, J. Brisson, F. Calevro, P. Capy, O. Catrice, T. Chertemps, C. Couture, L. Deliere, A. Douglas, K. Dufault-Thompson, P. Escuer, H. Feng, A. Forneck, T. Gabaldón, R. Guigó, F. Hilliou, S. Hinojosa-Alvarez, Y.-m. Hsiao, S. Hudaverdian, E. Jacquin-Joly, E. James, S. Johnston, B. Joubard, G. Le Goff, G. Le Trionnaire, P. Librado, S. Liu, E. Lombaert, H.-l. Lu, M. Maïbèche, M. Makni, M. Marcet-Houben, D. Martinez-Torres, C. Meslin, N. Montagné, N. Moran, D. Papura, N. Parisot, Y. Rahbé, M. Lopes, A. Ripoll-Cladellas, S. Robin, C. Roques, C. Lopez-Roques, P. Roux, J. Rozas, A. Sanchez-Gracia, J. Sánchez-Herrero, D. Santesmasses, I. Scatoni, R.-F. Serre, M. Tang, W. Tian, P. Umina, M. Van Munster, C. Vincent-Monégat, J. Wemmer, A. Wilson, Y. Zhang, C. Zhao, J. Zhao, S. Zhao, X. Zhou, F. Delmotte and D. Tagu. The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pestBMC Biology181December 2020, 90
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• 29 articleJ. Wenger, B. Cassone, F. Legeai, J. Johnston, R. Bansal, A. Yates, B. Coates, V. Pavinato and A. Michel. Whole genome sequence of the soybean aphid, Aphis glycines.Insect Biochemistry and Molecular Biology123August 2020, 102917
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International peer-reviewed conferences

• 30 inproceedingsY. Dufresne, C. Sun, P. Marijon, D. Lavenier, C. Chauve and R. Chikhi. A Graph-Theoretic Barcode Ordering Model for Linked-ReadsWABI 2020 - 20th Workshop on Algorithms in BioinformaticsPisa, ItalyAugust 2020, 11 - 12
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National peer-reviewed Conferences

• 31 inproceedings W. Delage, J. Thevenon and C. Lemaitre. Towards a better understanding of the low discovery rate of short-read based insertion variant callers JOBIM 2020 - Journées Ouvertes Biologie, Informatique et Mathématiques Montpellier, France June 2020
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Conferences without proceedings

• 32 inproceedings G. Bernardini, A. Conte, G. Gourdel, R. Grossi, G. Loukides, N. Pisanti, S. P. Pissis, G. Punzi, L. Stougie and M. Sweering. Hide and Mine in Strings: Hardness and Algorithms International Conference on Data Mining (ICDM) Sorrento, Italy November 2020
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• 33 inproceedingsD. Lavenier, R. Cimadomo and R. Jodin. Variant Calling Parallelization on Processor-in-Memory ArchitectureBIBM 2020 - IEEE International Conference on Bioinformatics and BiomedicineVirtual, South KoreaDecember 2020, 1-4
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Scientific book chapters

• 34 inbookJ. Nicolas. Artificial Intelligence and BioinformaticsIIIA Guided Tour of Artificial Intelligence ResearchInterfaces and Applications of Artificial Intelligence2020, 575
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Doctoral dissertations and habilitation theses

• 35 thesis W. Delage. Characterization and detection of large constitutional insertions for medical use Université Rennes 1 December 2020
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• 36 thesis L. Lecompte. Structural variant genotyping with long read data Université de Rennes 1 (UR1) December 2020
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Reports & preprints

• 37 misc K. Da Silva, N. Pons, M. Berland, F. Plaza Oñate, M. Almeida and P. Peterlongo. StrainFLAIR: Strain-level profiling of metagenomic samples using variation graphs February 2021
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• 38 misc V. Epain, R. Andonov, H. Djidjev and D. Lavenier. Long Reads Assembly Using Integer Linear Programming February 2020
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• 39 misc D. Richter, R. Watteaux, T. Vannier, J. Leconte, P. Frémont, G. Reygondeau, N. Maillet, N. Henry, G. Benoit, A. Fernandez-Guerra, S. Suweis, R. Narci, C. Berney, D. Eveillard, F. Gavory, L. Guidi, K. Labadie, E. Mahieu, J. Poulain, S. Romac, S. Roux, C. Dimier, S. Kandels, M. Picheral, S. Searson, S. Pesant, J.-M. Aury, J. Brum, C. Lemaitre, E. Pelletier, P. Bork, S. Sunagawa, L. Karp-Boss, C. Bowler, M. Sullivan, E. Karsenti, M. Mariadassou, I. Probert, P. Peterlongo, P. Wincker, C. de Vargas, M. Ribera D'Alcalà, D. Iudicone and O. Jaillon. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems February 2020
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Other scientific publications

• 40 misc C. Delahaye and J. Nicolas. Nanopore MinION long read sequencer: an overview of its error landscape November 2020
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• 41 thesis V. Epain. DNA fragments positioning improvement to realise consensus sequences in the de novo long reads assembly context Université de Rennes 1 [UR1] June 2020
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• 42 misc A. Guichard, F. Legeai, A. Le Bars, P. Jay, M. Joron, D. Tagu and C. Lemaitre. MTG-Link: filling gaps in draft genome assemblies with linked read data Montpellier, France June 2020
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• 43 misc A. Guichard, F. Legeai, A. Le Bars, P. Jay, M. Joron, D. Tagu and C. Lemaitre. MTG-Link: filling gaps in draft genome assemblies with linked read data Virtual, France October 2020
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• 44 misc G. Siekaniec, E. Roux, E. Guédon and J. Nicolas. Bacterial strains identification using Oxford Nanopore sequencing Montpellier, France June 2020
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• 45 thesis K. Thuillier. AlPha: A Mixed Integer Linear Programming Approach for Genome Haplotyping University of Rennes 1 August 2020
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Scientific popularization

• 46 inbookJ. Nicolas. IA et BioinformatiqueL’intelligence artificielle: De quoi s’agit-il vraiment ?July 2020, 101
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