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 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
• Publication:
  hal-03441914
• Contact:
  Claire Lemaitre
• Participants:
  Anne Guichard, Fabrice Legeai, Claire Lemaitre
• Partner:
  INRAE

7.1.2 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
• Contact:
  Pierre Peterlongo
• Participants:
  Teo Lemane, Rayan Chikhi, Pierre Peterlongo

7.1.3 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
• Contact:
  Jacques Nicolas
• Participants:
  Gregoire Siekaniec, Teo Lemane, Jacques Nicolas, Emeline Roux

7.1.4 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
• Contact:
  Kevin Da Silva

7.1.5 LRez

• Keywords:
  High throughput sequencing, Genome analysis, Indexation
• Functional Description:
  LRez is a C++ API and toolkit for analyzing and managing Linked-Reads sequencing data. Linked-Reads technologies, such as 10x Genomics, Haplotagging, stLFR and TELL-Seq, partition and tag high-molecular-weight DNA molecules with a barcode prior to classical short-read sequencing. This way, Linked-Reads manage to combine the high-quality of the short reads and a long-range information which can be inferred by identifying distant reads belonging to the same DNA molecule with the help of the barcodes. LRez provides various functionalities such as extracting, indexing and querying Linked-Reads barcodes, in BAM, FASTQ, and gzipped FASTQ files. The API is compiled as a shared library, helping its integration to external projects.
• URL:
  https://­github.­com/­morispi/­LRez
• Publications:
  hal-03421103, hal-03441917
• Contact:
  Claire Lemaitre
• Participants:
  Pierre Morisse, Fabrice Legeai, Claire Lemaitre

7.1.6 LEVIATHAN

• Keywords:
  High throughput sequencing, Structural Variation, Genome analysis
• Functional Description:
  LEVIATHAN is a structural variant calling tool dedicated to Linked-Reads sequencing data. Linked-Reads technologies combine the high quality and low cost of short-reads sequencing with a long-range information by adding barcodes that tag reads originating from the same long DNA fragment. The method relies on a barcode index, that allows to quickly compare the similarity of all possible pairs of regions in terms of amount of common barcodes. Region pairs sharing a sufficient number of barcodes are then considered as potential structural variants, and complementary, classical short reads methods are applied to further refine the breakpoint coordinates.
• URL:
  https://­github.­com/­morispi/­LEVIATHAN
• Publication:
  hal-03441874
• Contact:
  Claire Lemaitre
• Participants:
  Pierre Morisse, Fabrice Legeai, Claire Lemaitre

7.1.7 GraphUnzip

• Keywords:
  Genome assembly, Genome assembling, Haplotyping
• Functional Description:

  GraphUnzip untangles assembly graphs: GraphUnzip takes two input: 1) An assembly graph in GFA fromat, from an assembler 2) Data that can help untangling the graph: Hi-C, long reads or linked reads.

  GraphUnzip returns an untangled assembly graph, improving significantly the contiguity of the input assembly.

• URL:
  http://­github.­com/­nadegeGuiglielmoni/­GraphUnzip
• Contact:
  Roland Faure
• Partner:
  Université libre de Bruxelles

7.1.8 QuickDeconvolution

• Keywords:
  High throughput sequencing, Genomics
• Functional Description:
  QuickDeconvolution deconvolutes a set of linked reads: QuickDeconvolution takes as input a linked reads dataset and adds an extension (-1, -2, -3...) to the barcodes, such that two reads with the same barcode and the same extension comes from the same genomic region.
• URL:
  http://­github.­com/­RolandFaure/­QuickDeconvolution
• Contact:
  Roland Faure

7.1.9 findere

• Keywords:
  Indexation, Data structures, K-mer, Bloom filter, Genomic sequence
• Functional Description:
  findere is a simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure (AMQ). With no drawbacks (in particular no false positive), queries are two times faster with two orders of magnitude less false positive calls.
• Publication:
  hal-03243791
• Contact:
  Lucas Robidou

7.1.10 DnarXiv

• Name:
  dnarXiv project platform
• Keywords:
  Biological sequences, Simulator, Sequence alignment, Error Correction Code
• Functional Description:
  The objective of DnarXiv is to implement a complete system for storing, preserving and retrieving any type of digital document in DNA molecules. The modules include the conversion of the document into DNA sequences, the use of error-correcting codes, the simulation of the synthesis and assembly of DNA fragments, the simulation of the sequencing and basecalling of DNA molecules, and the overall supervision of the system.
• URL:
  https://­gitlab.­inria.­fr/­dnarxiv
• Contact:
  Olivier Boulle
• Partners:
  IMT Atlantique, Université de Rennes 1

7.1.11 SeqFaiLR

• Keywords:
  Long reads, Sequencing error, Sequence alignment
• Functional Description:
  SeqFaiLR analyses Nanopore long reads sequencing error profiles. The algorithms have been designed for Nanopore data, but can be applied for other long read data. From raw reads and reference genomes, these scripts perform alignment and compute several analysis (low-complexity regions sequencing accuracy, GC bias, links between error rates and quality scores, and so on).
• URL:
  https://­github.­com/­cdelahaye/­SeqFaiLR
• Contact:
  Clara Delahaye

8.1.1 Structural Variant detection with linked-reads

Participants: Fabrice Legeai, Claire Lemaitre, Pierre Morisse.

Thanks to their long-range information, linked-reads are particularly useful for structural variant calling. As a result, multiple structural variant calling methods were developed within the last few years. However, these methods were mainly tested on human data, and do not run well on non-human organisms, they all require large amounts of computing resources. We present LEVIATHAN, a new structural variant calling tool that aims to address these issues, and especially better scale and apply to a wide variety of organisms. Our method relies on a barcode index, that allows to quickly compare the similarity of all possible pairs of regions in terms of amount of common barcodes. Region pairs sharing a sufficient number of barcodes are then considered as potential structural variants, and complementary, classical short reads methods are applied to further refine the breakpoint coordinates. Our experiments on simulated data underline that our method compares well to the state-of-the-art, both in terms of recall and precision, and also in terms of resource consumption. Moreover, LEVIATHAN was successfully applied to a real dataset from a non-model organism, while all other tools either failed to run or required unreasonable amounts of resources 31.

8.1.2 Structural Variation genotyping with variant graphs

Participants: Claire Lemaitre, Sandra Romain.

One of the problems in Structural Variant (SV) analysis is the genotyping of variants. It consists in estimating the presence or absence of a set of known variants in a newly sequenced individual. Our team previously released SVJedi, the first SV genotyper dedicated to long read data. The method is based on linear representations of the allelic sequences of each SV. While this is very efficient for distant SVs, the method fails to genotype some closely located or overlapping SVs.To overcome this limitation, we present a novel approach, SVJedi-graph, which uses sequence graphs instead of linear sequences to represent the SVs. Only the SV sequences and that of the SV flanking regions are represented in our graph, resulting in a variation graph composed of multiple connected components, each representing the possible alleles for a region of one, or several close SVs. Tests on simulated long-reads on the human chromosome 1, with 1,000 deletions from the dbVar database, show a similar precision compared to SVJedi (98.1 %, against 97.8 %). Importantly, when additional deletions are added progressively closer to the original 1,000 in the dataset, SVJedi-graph maintains a 100 % genotyping rate with a high precision, when SVJedi is not able to assign a genotype to 21 % of the deletions when they are too close to each other (0-50 bp apart). SVJedi-graph also supports other SV types such as insertions and inversions, for which similar performances were obtained 40.

8.1.3 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. This type of sequencing data has 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 30.

8.1.4 Linked-read deconvolution

Participants: Roland Faure, Dominique Lavenier.

Introduced recently, linked reads technologies, such as the 10X chromium system, use microfluidics to tag multiple short reads coming from the same long (50-200 kbp) fragment with a small sequence, called barcode. Such data are cheap and easy to prepare, combining the accuracy of short-read sequencing and long-range information from the barcodes. The fact that reads with the same barcode come from the same fragment of the genome is extremely rich in information and can be used in a myriad of software. However, the same barcode may be used several times for several different fragments, complicating the analyses. We have developed QuickDeconvolution (QD) a new software for deconvoluting a set of reads sharing a barcode, i.e. separating reads coming from the different fragments. This software takes as input only the sequencing data, without the need for a reference genome. We show that QuickDeconvolution outperforms existing software in terms of accuracy, speed and scalability, making it capable of deconvolving datasets inaccessible before. In particular, we demonstrate here the first example in the literature of a successfully deconvolved animal sequencing dataset, a Drosophila melanogaster dataset of 33 Gbp 38.

8.1.5 Unzipping assembly graphs with long reads and Hi-C

Participants: Roland Faure.

Long reads and Hi-C have revolutionized the field of genome assembly as they have made highly contiguous assemblies accessible even for challenging genomes. As haploid chromosome-level assemblies are now commonly achieved for all types of organisms, phasing assemblies has become the new frontier for genome reconstruction. Several tools have already been released using long reads and/or Hi-C to phase assemblies, but they all start from a set of linear sequences and are ill-suited for non-model organisms with high levels of heterozygosity. We designed GraphUnzip, a fast, memory-efficient and flexible tool to phase assembly graphs into their constituent haplotypes using long reads and/or Hi-C data. As GraphUnzip only connects sequences that already had a potential link in the assembly graph, it yields high-quality gap-less supercontigs. To demonstrate the efficiency of GraphUnzip, we tested it on the human HG00733 and the potato Solanum tuberosum. In both cases, GraphUnzip yielded phased assemblies with improved contiguity 29.

8.1.6 CONSENT, long read correction and assembly polishing

Participants: Pierre Morisse.

Third-generation sequencing technologies allow to sequence long reads of tens of kbp, but with high error rates, currently capped around 10%. Self-correction is thus regularly used in long reads analysis projects. We introduce CONSENT, a new self-correction method that relies both on multiple sequence alignment and local de Bruijn graphs. To ensure scalability, multiple sequence alignment computation benefits from a new and efficient segmentation strategy, allowing a massive speedup. CONSENT compares well to the state-of-the-art, and performs better on real Oxford Nanopore data. Specifically, CONSENT is the only method that efficiently scales to ultra-long reads. Moreover, our experiments show that error correction with CONSENT improves the quality of genome assemblies. Additionally, CONSENT implements a polishing feature, allowing to correct raw assemblies. Our experiments show that CONSENT is 2-38x times faster than other polishing tools 17.

8.1.7 Efficient reads' overlaps data structure

Participants: Victor Epain, Rumen Andonov, Dominqiue Lavenier.

The below feature (called reverse symmetry), is an important reads’ characteristic. Denote by $𝒪$ the set of overlaps and let $(u,v)\in 𝒪$ be an overlap between read $u$ suffix and read $v$ prefix. It is known that $(\overline{v},\overline{u})\in 𝒪$, where $\overline{u}(\overline{v}$) denotes the reverse of $u$($v$) respectively. The reverse symmetry property is commonly used to not double the reads in database, and so to represent the set of overlaps in a bi-directed graph. However, this representation (largely used by the community in the domain) increases the number of iterations over overlaps (which are oriented couples between oriented reads), and slows down the corresponding algorithms. Taking advantage of the reverse symmetry we develop in this project a novel data structure that allows to efficiently store the reads’ overlaps. Iterations become faster, and it is not necessary to duplicate data any more. This new graph view permits to adapt the breadth-first search algorithm to identify inverted repeats in the sequenced genomes. This work has been presented at seqBIM2021 workshop.

8.1.8 Chloroplast scaffolding based on inverted repeat regions recovered with integer linear programming

Participants: Victor Epain, Rumen Andonov, Dominqiue Lavenier.

Chloroplasts are plastids in plants' cells known for photosynthesis metabolism. Their genomes are circular and usually form a quadripartite structure such that two unique genomic regions are separated by two inverted repeats. From a pre-assembled genome obtained witha De-Bruijn graph approach, we propose an integer linear programming strategy to extract the two inverted regions. Contigs are output with an estimated multiplicity, which corresponds to an upper bound of the number of occurrences of the contig or its reverse-complement in the solution. A contig and its reverse occurrence form an inverted pair. We model inverted repeats extraction as finding a circular path from and to a given contig, maximising the number of contiguous nested inverted pairs. We propose an integer linear programming formulation to solve this problem. Our preliminary results are very encouraging and we presented them at BiATA2021 conference 24.

In collaboration with Sven Schrinner and Gunnar Klau — respectively PhD student and Prof. at Heinrich Heine Universität, Düsseldorf, we are currently working on the NP-hardness complexity proof of this problem.

8.2.1 LRez, a C++ API and toolkit for analyzing and managing Linked-Reads data

Participants: Fabrice Legeai, Claire Lemaitre, Pierre Morisse.

Linked-Reads technologies combine both the high quality and low cost of short-reads sequencing and long-range information, through the use of barcodes tagging reads which originate from a common long DNA mol- ecule. This technology has been employed in a broad range of applications including genome assembly, phasing and scaffolding, as well as structural variant calling. However, to date, no tool or API dedicated to the manipulation of Linked-Reads data exist. We introduce LRez, a C++ API and toolkit that allows easy management of Linked-Reads data. LRez includes various functionalities, for computing numbers of common barcodes between genomic regions, extracting barcodes from BAM files, as well as indexing and querying BAM, FASTQ and gzipped FASTQ files to quickly fetch all reads or alignments containing a given barcode. LRez is compatible with a wide range of Linked-Reads sequencing technologies, and can thus be used in any tool or pipeline requiring barcode processing or indexing, in order to improve their performances. In this paper, we tested the index and query subcommands on multiple files from various organisms and sequencing technologies. We notably show that the running time to query all reads with a given barcode is improved by several orders of magnitude when using LRez indexing and query implementation compared to naive index-free approaches 16, 39.

8.2.2 Large-scale kmer indexing

Participants: Téo Lemane, Pierre Peterlongo.

Our main contributions, published in 36, 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.

With our approach, we were able to index the Tara Ocean bacterial metagenomic dataset which is a difficult dataset, both in terms of size and diversity with 266 billions of distinct kmers. Such an index enables to query these raw and unassembled data with sequences of arbitrary size and thus allowing new biological analyses. In addition, our experimental results highlight that the usual yet crude filtering of rare kmers is inappropriate for this type of complex dataset.

8.2.3 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 12.

8.2.4 A novel compressed full-text index

Participants: Garance Gourdel.

8.2.5 Minimizing the size of kmer indexes for Approximate Membership Queries

Participants: Lucas Robidou, Pierre Peterlongo.

8.2.6 Sensible hashing techniques

Participants: Pierre Peterlongo.

8.3.1 Identification of bacterial strains

Participants: Téo Lemane, Jacques Nicolas, Rania Ouazahrou, 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 for the lactic acid bacteria Streptococcus thermophilus22 in cooperation with INRAE STLO, which have been made publicly available on the NCBI and on the Microscope platform at Genoscope.

We propose a new method of bacterial strain identification based on the assumption that a nanopore read is long enough to distinguish one strain (or group of strains) from others. This method uses a particularly compact indexing technique of a known genome database based on a tree structure of Bloom filters. It also relies on the use of spaced seeds in order to search for sequences in the index while being less sensitive to long read substitution errors. Identification is treated as an optimization problem on a strain X kmers presence matrix and solved exactly with an ASP solver. The method is implemented in a software called ORI (Oxford nanopore Reads Identification).It has shown robust bacterial identification results on real data of Streptococcus thermophilus41, 20.

ORI was further used to identify reference genomes in a complex whole piglet intestinal metagenome and to best represent meta-metagenomes. This program was initiated as a new collaboration with NuMeCan, an INRAe-INSERM-University of Rennes1 team. More than 20 bacterial species were selected (representing an abundance of more than 0.5% of the metagenome) and were described by 34 genomes selected with ORI. This work is still in progress.

8.3.2 Haplotype phasing of long reads for polyploid species

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. 13. We are now working on haplotype phasing of reads for di- and polyploid species. We address this problem as an optimization problem, and use Answer Set Programming to solve it. Our work focuses on reasoning on the set of possible solutions, and integrating user preferences, possibly leading to several alternative answers.

8.4.1 Error correcting code targeting nanopore sequencing

Participants: Dominique Lavenier.

We proposed a novel statistical model for DNA storage, which takes into account the memory within DNA storage error events, and follows the way nanopore sequencing works. Compared to existing channel models, the proposed model represents more accurate experimental datasets. We also proposed a full error-correction scheme for DNA storage, based on a consensus algorithm 35 and non-binary LDPC codes. Especially, we introduce a novel synchronization method which allows to eliminate remaining deletion errors after the consensus, before applying a belief-propagation LDPC decoding algorithm to correct substitution errors. This method exploits the LDPC code structure to correct deletions, and does not require adding any extra redundancy 27.

8.4.2 dnarXiv platform

Participants: Olivier Boulle, Dominique Lavenier.

We have developed an experimental platform to test or emulate the full process of writing and reading data on DNA molecules. It is composed of the following main modules : encoding, synthesis, molecule design, sequencing, DNA data processing, decoding. It is based on a flexible software architecture where real or in-silico experimentation can be performed to test and evaluate different DNA archiving strategies.

8.4.3 Molecule design

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

One of the original features of the dnarXiv project is the use of the 3rd sequencing generation developed by Oxford Nanopore Technologies. Its main characteristic is the ability to sequence long DNA molecules. To take advantage of this technology, long DNA molecules must be used as storage support. But current synthesis technologies provide only small oligo-nucleotides (max 300nt). Thus, we are currently developing a method to assemble small synthetic DNA fragments into long molecules. The proof of concept was obtained by successfully assembling 20 single-stranded synthetic DNA fragments into a 600 bp double-stranded molecule.

8.5.1 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, 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 21, 10, 15, 23, 19, 18, 11, 14.

10.1.1 Visits to international teams

10.2.1 Other european programs/initiatives

10.3.1 ANR

10.3.2 Inria Exploratory Action

10.4.1 Labex Cominlabs

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• D. Lavenier, "Stockage d'information sur ADN", Institut Brestois du Numérique et des Mathématique, Nov. 2021
• C. Lemaitre, "Local assembly approaches for variant calling and genome assembly", Seminar of DGMI UMR, Montpellier, July 2021.

11.1.5 Leadership within the scientific community

• Members of the Scientific Advisory Board of the GDR BIM (National Research Group in Molecular Bioinformatics) [P. Peterlongo, C. Lemaitre]
• Animator of the Sequence Algorithms axis (seqBIM GT) of the BIM and IM GDRs (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

• Scientific expert for the DGRI (Direction générale pour la recherche et l’innovation) from the Ministère de l’Enseignement Supérieur, de la Recherche et de l’Innovation (MESRI) [D. Lavenier]

11.1.7 Research administration

• Member of the CoNRS, section 06, until Aug. 2021 [D. Lavenier]
• Member of the CoNRS, section 51, until Aug. 2021[D. Lavenier]
• Corresponding member of COERLE (Inria Operational Committee for the assessment of Legal and Ethical risks). Participation to the ethical group of IFB (French Elixir node, Institut Français de Bioinformatique) [J. Nicolas]
• Member of the steering committee of the INRAE BIPAA Platform (BioInformatics Platform for Agro-ecosystems Arthropods) [P. Peterlongo]
• Institutional delegate representative of INRIA in the GIS BioGenOuest regrouping all public research platforms in Life Science in the west of France (régions Bretagne/ Pays de Loire) [J. Nicolas]
• Scientific Advisor of The GenOuest Platform (Bioinformatics Resource Center of BioGenOuest) [J. Nicolas]
• Representative of the environmental axis of the IRISA UMR [C. Lemaitre]
• Chair of the committee in charge of all the temporary recruitments (“Commission Personnel”) at Inria Rennes-Bretagne Atlantique and IRISA [D. Lavenier]
• Member of the Selection Committee for Lecturer Position "Maitre de Conférence" at Laboratoire IBISC (University Evry, section 27 (Informatique) [R. Andonov]

11.2.1 Teaching

• Licence : R. Andonov, V. Epain, Models and Algorithms in Graphs, 100h, L3, Univ. Rennes 1, France.
• Licence : G. Gourdel, Python, 48h, L2 MIASH, Univ. Paris 1, France.
• Licence : E. Roux, biochemistry, 50h, L1 and L3, Univ. Rennes 1, France.
• Master : R. Andonov, V. Epain, Operations Research (OR), 82h, M1 Miage, Univ. Rennes 1, France.
• Master : R. Andonov, Optimisation Techniques in Bioinformatics, 18h, M2, Univ. Rennes 1, France.
• Master : V. Epain, 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 : D. Lavenier, Memory Efficient Algorithms for Big Data, 24h, Engineering School, ESIR, Rennes.
• Master : D. Lavenier, Colloquium, 15h, research master degree in computer science, Univ Rennes 1
• Master : E. Roux, biochemistry, 50h, M1 and M2, Univ. Rennes 1, France.
• Aggreg: D. Lavenier, Computer Architecture, 10h, ENS Rennes
• Ecole Jeunes Chercheurs : C. Lemaitre, Genome assembly, 5h, Ecole JC2BIM du GDR BIM, Rennes

11.2.2 Defenses

• HDR: C. Lemaitre, Bioinformatics methods for studying Structural Variations with sequencing data, Université de Rennes 1, 02/12/2021 32.
• PhD: G. Siekaniec, Identification of strains of a bacterial species from long reads, Université de Rennes 1, 10/12/2021 33.

11.2.3 Supervision

• PhD: G. Siekaniec, Identification of strains of a bacterial species from long reads, J. Nicolas (co-supervised with E. Guédon, E. Roux).
• PhD in progress: K. da Silva, Metacatalogue : a new framework for intestinal microbiota sequencing data mining, 01/10/2018, P. Peterlongo (co-supervised with M. Berland, N. Pons).
• 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 R. Andonov, D. Lavenier, (co-supervised with JF Gibrat, INRAE).
• PhD in progress: G. Gourdel, Sketch-based approaches to processing massive string data, 01/09/2020, P. Peterlongo (co-supervised with T. Starikovskaya).
• PhD in progress: L. Robidou, Search engine for genomic sequencing data, 01/10/2020, P. Peterlongo
• PhD in progress: S. Romain, Genome graph data structures for Structural Variation analyses in butterfly genomes, 01/09/2021, D. Lavenier, C. Lemaitre.
• PhD in progress: K. Hannoush, Pan-genome graph update strategies, 01/09/2021, P. Peterlongo (co-supervised with C. Marchet).
• PhD in Progress: R. Faure, Recovering end-to-end phased genomes, 01/10/2021, D. Lavenier (co-supervised with J-F. Flot).

11.2.4 Juries

• Member of Habilitation thesis jury: C. Lemaitre [D. Lavenier, president]
• Referee of Ph-D thesis jury: Vincent Sater, Univ Rouen [P. Peterlongo], Y. Mansour, Univ. Montpellier [D. Lavenier]
• Member of PhD thesis jury: Quentin Delorme, Univ Montpellier [C. Lemaitre], Camille Sessegolo, Univ Lyon [P. Peterlongo], Chi Nguyen Lam, UBO [D. Lavenier].
• Member of PhD thesis committee: Benoit Goutorbe, Univ Paris-Saclay [C. Lemaitre] Benjamin Churcheward, Univ. Nantes [D. Lavenier], Belaid Hamoum, UBS, Lorient [D. Lavenier], Nguyen Dang, Univ. Montpellier [D. Lavenier], Rick Wertenbroek, Univ. Lausanne [D. Lavenier], Xavier Pic, Univ. Nice [D. Lavenier].

11.3.1 Internal or external Inria responsibilities

• Member of the Interstice editorial board [P. Peterlongo]
• Organization of Sciences en cour[t]s events, Nicomaque association (link) [C. Delahaye, T. Lemane]

11.3.2 Articles and contents

• Short Movie "Cocktails de bio-informatique", presented at Sciences en Courts, a local contest of popularization short movies made by PhD students (link) [G. Gourdel, V. Epain, L. Robidou]
• Popularization report from the GDR BIM, "SARS-CoV-2 Through the Lens of Computational Biology: How bioinformatics is playing a key role in the study of the virus and its origins" 34 [C. Lemaitre]

International journals

• 10 articleG.Grégoire Bianchetti, V.Vanessa Clouet, F.Fabrice Legeai, C.Cécile Baron, K.Kévin Gazengel, A.Aurélien Carrillo, M. M.Maria M. Manzanares-Dauleux, J. J.Julia J Buitink and N.Nathalie Nesi. RNA sequencing data for responses to drought stress and/or clubroot infection in developing seeds of Brassica napus.Data in Brief38October 2021, 1-11
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• 11 articleA.Antonino Cusumano, S.Serge Urbach, F.Fabrice Legeai, M.Marc Ravallec, M.Marcel Dicke, E.Erik Poelman and A.Anne‐Nathalie Volkoff. Plant‐phenotypic changes induced by parasitoid ichnoviruses enhance the performance of both unparasitized and parasitized caterpillars.Molecular Ecology3018September 2021, 4567-4583
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• 12 articleK.Kévin Da Silva, N.Nicolas Pons, M.Magali Berland, F.Florian Plaza Oñate, M.Mathieu Almeida and P.Pierre Peterlongo. StrainFLAIR: strain-level profiling of metagenomic samples using variation graphs.PeerJAugust 2021
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• 13 articleC.Clara Delahaye and J.Jacques Nicolas. Sequencing DNA with nanopores: Troubles and biases.PLoS ONEOctober 2021, 1-29
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• 14 articleJ.-L.Jean-Luc Gatti, M.Maya Belghazi, F.Fabrice Legeai, M.Marc Ravallec, M.Marie FRAYSSINET, S.Stéphanie Robin, D.Djibril Aboubakar-Souna, R.Ramasamy Srinivasan, M.Manuele Tamò, M.Marylène Poirié and A.-N.Anne-Nathalie Volkoff. Proteo-Trancriptomic Analyses Reveal a Large Expansion of Metalloprotease-Like Proteins in Atypical Venom Vesicles of the Wasp Meteorus pulchricornis (Braconidae).Toxins137July 2021, 1-36
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• 15 articleJ.Jérémy Gauthier, H.Hélène Boulain, J. J.Joke J F A van Vugt, L.Lyam Baudry, E.Emma Persyn, J.-M.Jean-Marc Aury, B.Benjamin Noel, A.Anthony Bretaudeau, F.Fabrice Legeai, S.Sven Warris, M. A.Mohamed A Chebbi, G.Géraldine Dubreuil, B.Bernard Duvic, N.Natacha Kremer, P.Philippe Gayral, K.Karine Musset, T.Thibaut Josse, D.Diane Bigot, C.Christophe Bressac, S.Sébastien Moreau, G.Georges Périquet, M.Myriam Harry, N.Nicolas Montagne, I.Isabelle Boulogne, M.Mahnaz Sabeti-Azad, M.Martine Maïbèche, T.Thomas Chertemps, F.Frédérique Hilliou, D.David Siaussat, J.Joëlle Amselem, I.Isabelle Luyten, C.Claire Capdevielle-Dulac, K.Karine Labadie, B. L.Bruna Laís Merlin, V.Valérie Barbe, J. G.Jetske G de Boer, M.Martial Marbouty, F. L.Fernando Luis Cônsoli, S.Stéphane Dupas, A.Aurélie Hua-Van, G.Gaelle Le Goff, A.Annie Bézier, E.Emmanuelle Jacquin-Joly, J. B.James B Whitfield, L. E.Louise E M Vet, H. M.Hans M Smid, L.Laure Kaiser, R.Romain Koszul, E.Elisabeth Huguet, E. A.Elisabeth A. Herniou and J.-M.Jean-Michel Drezen. Chromosomal scale assembly of parasitic wasp genome reveals symbiotic virus colonization.Communications Biology41January 2021, 1-15
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• 16 articleP.Pierre Morisse, C.Claire Lemaitre and F.Fabrice Legeai. LRez: C ++ API and toolkit for analyzing and managing Linked-Reads data.Bioinformatics Advances11June 2021, 1-4
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• 17 articleP.Pierre Morisse, C.Camille Marchet, A.Antoine Limasset, T.Thierry Lecroq and A.Arnaud Lefebvre. Scalable long read self-correction and assembly polishing with multiple sequence alignment.Scientific Reports111December 2021, 1-13
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• 18 articleF.Florence Piron‐Prunier, E.Emma Persyn, F.Fabrice Legeai, M.Melanie McClure, C.Camille Meslin, S.Stéphanie Robin, S.Susete Alves‐carvalho, A.Ammara Mohammad, C.Corinne Blugeon, E.Emmanuelle Jacquin‐joly, N.Nicolas Montagné, M.Marianne Elias and J.Jérémy Gauthier. Comparative transcriptome analysis at the onset of speciation in a mimetic butterfly—The Ithomiini Melinaea marsaeus.Journal of Evolutionary Biology3411November 2021, 1704-1721
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• 19 articleE.Erwan Poivet, A.Aurore Gallot, N.Nicolas Montagné, P.Pavel Senin, C.Christelle Monsempès, F.Fabrice Legeai and E.Emmanuelle Jacquin-Joly. Transcriptome Profiling of Starvation in the Peripheral Chemosensory Organs of the Crop Pest Spodoptera littoralis Caterpillars.Insects127June 2021, 573
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• 20 articleG.Grégoire Siekaniec, E.Emeline Roux, T.Téo Lemane, E.Eric Guédon and J.Jacques Nicolas. Identification of isolated or mixed strains from long reads: a challenge met on Streptococcus thermophilus using a MinION sequencer.Microbial Genomics7112021, 1-14
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• 21 articleK. S.Kumar Saurabh Singh, E.Erick Cordeiro, B.Bartlomiej Troczka, A.Adam Pym, J.Joanna Mackisack, T.Thomas Mathers, A.Ana Duarte, F.Fabrice Legeai, S.Stéphanie Robin, P.Pablo Bielza, H.Hannah Burrack, K.Kamel Charaabi, I.Ian Denholm, C.Christian Figueroa, R.Richard ffrench-Constant, G.Georg Jander, J.John Margaritopoulos, E.Emanuele Mazzoni, R.Ralf Nauen, C.Claudio Ramírez, G.Guangwei Ren, I.Ilona Stepanyan, P.Paul Umina, N.Nina Voronova, J.John Vontas, M.Martin Williamson, A.Alex Wilson, G.Gao Xi-Wu, Y.-N.Young-Nam Youn, C.Christoph Zimmer, J.-C.Jean-Christophe Simon, A.Alex Hayward and C.Chris Bass. Global patterns in genomic diversity underpinning the evolution of insecticide resistance in the aphid crop pest Myzus persicae.Communications Biology41December 2021, 847
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• 22 articleO.Ophélie Uriot, M.Mounira Kebouchi, E.Emilie Lorson, W.Wessam Galia, S.Sylvain Denis, S.Sandrine Chalancon, Z.Zeeshan Hafeez, E.Emeline Roux, M.Magali Genay, S.Stéphanie BLANQUET-DIOT and A.Annie Dary-Mourot. Identification of <i>Streptococcus thermophilus</i> Genes Specifically Expressed under Simulated Human Digestive Conditions Using R-IVET Technology.Microorganisms96May 2021, 1-26
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International peer-reviewed conferences

• 23 inproceedingsS.Susete Alves Carvalho, K.Kévin Gazengel, A.Anthony Bretaudeau, S.Stéphanie Robin, S.Stéphanie Daval and F.Fabrice Legeai. AskoR, A R Package for Easy RNASeq Data Analysis.IECE 2021 - 1st International Electronic Conference on EntomologyVirtual, France2021, 1-8
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• 24 inproceedingsR.Rumen Andonov, V.Victor Epain and D.Dominique Lavenier. Optimal de novo assemblies for chloroplast genomes based on inverted repeats patterns.Bioinformatics: from Algorithms to Applications 2021St. Petersbourg, Russia, FranceJuly 2021
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• 25 inproceedingsG.Guy Arbitman, S. T.Shmuel T Klein, P.Pierre Peterlongo and D.Dana Shapira. Approximate Hashing for Bioinformatics.LNCSCIAA 2021 - 25th International Conference on Implementation and Application of Automata1280325th International Conference on Implementation and Application of AutomataBremen, GermanyJuly 2021, 1-12
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• 26 inproceedingsT.Travis Gagie, G.Garance Gourdel and G.Giovanni Manzini. Compressing and Indexing Aligned Readsets.WABI 2021 - Workshop on Algorithms in BioinformaticsOnline conference, FranceAugust 2021, 1-21
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• 27 inproceedingsB.Belaid Hamoum, E.Elsa Dupraz, L.Laura Conde-Canencia and D.Dominique Lavenier. Channel Model with Memory for DNA Data Storage with Nanopore Sequencing.ISTC 2021 - 11th International Symposium on Topics in CodingMontreal, CanadaIEEEAugust 2021, 1-5
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• 28 inproceedingsL.Lucas Robidou and P.Pierre Peterlongo. findere: fast and precise approximate membership query.SPIRE 2021 - The 28th annual Symposium on String Processing and Information RetrievalLille / Virtual, FranceOctober 2021
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Conferences without proceedings

• 29 inproceedingsR.Roland Faure, N.Nadège Guiglielmoni and J.-F.Jean-François Flot. GraphUnzip: unzipping assembly graphs with long reads and Hi-C.JOBIM 2021 - Journées Ouvertes en Biologie, Informatique et MathématiquesParis, FranceJuly 2021, 1-7
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• 30 inproceedingsA.Anne Guichard, F.Fabrice Legeai, D.Denis Tagu and C.Claire Lemaitre. MTG-Link: filling gaps in draft genome assemblies with linked read data.JOBIM 2021 - Journées Ouvertes Biologie, Informatique et MathématiquesParis, FranceJuly 2021, 1-8
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• 31 inproceedingsP.Pierre Morisse, F.Fabrice Legeai and C.Claire Lemaitre. LEVIATHAN: efficient discovery of large structural variants by leveraging long-range information from Linked-Reads data.JOBIM 2021 - Journées Ouvertes en Biologie, Informatique et MathématiquesParis, FranceJuly 2021, 1-8
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Doctoral dissertations and habilitation theses

• 32 thesisC.Claire Lemaitre. Méthodes bioinformatiques pour l'étude des Variants de Structure avec des données de séquençages génomiques.Université Rennes 1December 2021
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• 33 thesisG. R.Grégoire Romain Siekaniec. Identification of strains of a bacterial species from long reads.MathSTICDecember 2021
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Reports & preprints

• 34 reportS.Samuel Alizon, F.Frédéric Cazals, S.Stéphane Guindon, C.Claire Lemaitre, T.Tristan Mary-Huard, A.Anna Niarakis, M.Mikaël Salson, C.Celine Scornavacca and H.Hélène Touzet. SARS-CoV-2 Through the Lens of Computational Biology:How bioinformatics is playing a key role in the study of the virus and its origins.CNRSMarch 2021, 1-35
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• 35 reportD.Dominique Lavenier. Constrained Consensus Sequence Algorithm for DNA Archiving.CNRS IRISAMay 2021
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• 36 miscT.Téo Lemane, P.Paul Medvedev, R.Rayan Chikhi and P.Pierre Peterlongo. kmtricks: Efficient construction of Bloom filters for large sequencing data collections.March 2021
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Other scientific publications

• 37 inproceedingsG.Guy Arbitman, S. T.Shmuel T Klein, P.Pierre Peterlongo and D.Dana Shapira. Approximate Hashing for Bioinformatics.DCC 2021 - Data Compression ConferenceVirtual, United StatesMarch 2021
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• 38 thesisR.Roland Faure. QuickDeconvolution: fast and scalable deconvolution of linked-reads sequencing data.Sorbonne universitésSeptember 2021
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• 39 inproceedingsP.Pierre Morisse, C.Claire Lemaitre and F.Fabrice Legeai. LRez: C++ API and toolkit for analyzing and managing Linked-Reads data.JOBIM 2021 - Journées Ouvertes en Biologie, Informatique et MathématiquesParis, FranceJuly 2021, 1
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• 40 inproceedingsS.Sandra Romain and C.Claire Lemaitre. SVJedi-graph: Structural Variant genotyping with long-reads using a variation graph.JOBIM 2021 - Journées Ouvertes en Biologie, Informatique et MathématiquesParis, FranceJuly 2021, 1
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• 41 inproceedingsG.Grégoire Siekaniec, R.Rania Ouazahrou, G.Gaëlle Boudry, E.Eric Guédon, E.Emeline Roux and J.Jacques Nicolas. Identification of bacterial strains using ORI (Oxford nanopore Reads Identification).Microbes 2021 - Société Française de MicrobiologieNantes, FranceSeptember 2021
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