2023Activity reportProject-TeamGENSCALE

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 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 command line tools, a C++ library, and a C++ plugin system to extend its features.
• URL:
  https://­github.­com/­tlemane/­kmtricks
• Publication:
  hal-03166007
• Contact:
  Pierre Peterlongo
• Participants:
  Teo Lemane, Rayan Chikhi, Pierre Peterlongo

7.1.2 kmindex

• Keywords:
  Kmer, Data structures, Indexing
• Functional Description:
  Given a databank D = {S1, ..., Sn}, with each Si being any genomic dataset (genome or raw reads), kmindex allows to compute the percentage of shared k-mers between a query Q and each S in D. It supports multiple datasets and allows searching for each sub-index Di in G = {D1, ..., Dm}. Queries benefit from the findere algorithm. In a few words, findere allows to reduce the false positive rate at query time by querying (s+z)-mers instead of s-mers, which are the indexed words, usually called k-mers. kmindex is a tool for querying sequencing samples indexed using kmtricks.
• URL:
  https://­github.­com/­tlemane/­kmindex
• Contact:
  Pierre Peterlongo

7.1.3 kmdiff

• Keywords:
  K-mer, K-mers matrix, GWAS
• Functional Description:
  Genome wide association studies elucidate links between genotypes and phenotypes. Recent studies point out the interest of conducting such experiments using k-mers as the base signal instead of single-nucleotide polymorphisms. kmdiff is a command line tool allowing efficient differential k-mer analyses on large sequencing cohorts.
• URL:
  https://­github.­com/­tlemane/­kmdiff
• Publication:
  hal-03885124
• Contact:
  Pierre Peterlongo
• Participants:
  Teo Lemane, Rayan Chikhi, Pierre Peterlongo

7.1.4 fimpera

• Keywords:
  Indexation, Data structures, K-mer, Bloom filter, Bioinfirmatics search sequence, Search Engine
• Functional Description:
  fimpera is a strategy for indexing and querying "hashtable-like" data structures named "AMQ" (for "Approximate Membership Query data structure"). When queried, those AMQs can yield false positives or overestimated calls. fimpera reduces their false positive rate by two orders of magnitude while reducing the overestimations. Without introducing any false negative, fimpera speeds up queries.
• URL:
  https://­github.­com/­lrobidou/­fimpera
• Publication:
  hal-03912993
• Contact:
  Lucas Robidou
• Participants:
  Lucas Robidou, Pierre Peterlongo

7.1.5 SVJedi-graph

• Keywords:
  Structural Variation, Genotyping, High throughput sequencing, Sequence alignment
• Functional Description:
  SVJedi-graph is a structural variation (SV) genotyper for long read data. It constructs a variation graph to represent all alleles of all SVs given as input. This graph-based approach allows to better represent close and overlapping SVs. Long reads are then aligned to this graph and the genotype of each variant is estimated based on allele-specific alignment counts. SVJedi-graph 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/­SandraLouise/­SVJedi-graph
• Contact:
  Claire Lemaitre
• Participants:
  Claire Lemaitre, Sandra Romain

7.1.6 MTG-link

• Keywords:
  Bioinformatics, Genome assembly, Barcode, Linked-reads, Gap-filling
• Functional Description:
  MTG-Link is a local assembly tool dedicated to linked-read sequencing data. It leverages barcode information from linked-reads to assemble specific loci. Notably, the sequence to be assembled can be totally unknown (contrary to targeted assembly tools). It takes as input a set of linked-reads, the target flanking sequences and coordinates in GFA format and an alignment file in BAM format. It outputs the results in a GFA file.
• Release Contributions:
  MTG-Link can now be used for various local assembly use cases, such as intra-scaffold and inter-scaffold gap-fillings, as well as the reconstruction of the alternative allele of large insertion variants. It is also directly compatible with the following linked-reads technologies, given that the barcodes are reported using the "BX:Z" tag: 10X Genomics, Haplotagging, stLFR and TELL-Seq.
• URL:
  https://­github.­com/­anne-gcd/­MTG-Link
• Publications:
  hal-03073966, hal-03074227, hal-03441914, hal-03886951
• Contact:
  Claire Lemaitre
• Participants:
  Anne Guichard, Fabrice Legeai, Claire Lemaitre
• Partner:
  INRAE

7.1.7 PyRevSymG

• Name:
  Python Reverse Symmetric Graph
• Keywords:
  Directed graphs, Graph algorithmics, DNA sequencing
• Functional Description:
  PyRevSymG is a Python3 API to store succession relationships between oriented fragments (in forward or reverse orientation) that have been sequenced from nucleotide sequence(s) in an oriented graph. For example, this API can be used for a genome assembly overlap-layout-consensus method.
• URL:
  https://­pypi.­org/­project/­revsymg/
• Contact:
  Victor Epain
• Participant:
  Victor Epain

7.1.8 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
• Participants:
  Olivier Boulle, Dominique Lavenier
• Partners:
  IMT Atlantique, Université de Rennes 1

7.1.9 MOF-SEARCH

• Name:
  MOF-SEARCH
• Keywords:
  Bioinformatics, Alignment, Genomic sequence, Data compression
• Functional Description:
  A tool for rapid BLAST-like search among 661k sequenced bacteria on personal computers.
• URL:
  http://­github.­com/­karel-brinda/­mof-search
• Contact:
  Karel Brinda
• Participant:
  Karel Brinda
• Partners:
  European Bioinformatics Institute, HARVARD Medical School

7.1.10 MiniPhy

• Name:
  MiniPhy
• Keywords:
  Compression, Bioinformatics, Genomic sequence, Data compression
• Functional Description:
  Phylogenetic compression of extremely large genome collections
• URL:
  https://­github.­com/­karel-brinda/­miniphy
• Contact:
  Karel Brinda

7.1.11 KmerCamel

• Name:
  KmerCamel
• Keywords:
  Bioinformatics, Compression
• Functional Description:
  KmerCamel provides implementations of several algorithms for efficiently representing a set of k-mers as a masked superstring.
• URL:
  https://­github.­com/­OndrejSladky/­kmercamel
• Contact:
  Karel Brinda

7.1.12 gfagraphs

• Keywords:
  Pangenomics, Variation graphs
• Functional Description:
  This library aims to be an abstraction layer for the GFA file format, which is the standard file format for pangenome and variation graphs. It allows to load, save, modify and annotate a GFA file. Written in Python, it's goal is to provide an easy-to-use Graph object on which many operations can be performed.
• URL:
  https://­github.­com/­Tharos-ux/­gfagraphs
• Contact:
  Siegfried Dubois
• Participants:
  Siegfried Dubois, Claire Lemaitre
• Partner:
  INRAE

7.1.13 pancat

• Name:
  PANgenome Comparison and Anlaysis Toolkit
• Keywords:
  Pangenomics, Variation graphs
• Functional Description:
  PANCAT is a command-line tool which allows to go through, visualize and compare pangenome graphs. Pangenome graphs (or variation graphs) are sequence graphs, encoded in a textual format, which describe shared and unique parts between a set of genomes. The aim of this tool is to answer technical and biological questions on this particular data structure.
• URL:
  https://­github.­com/­Tharos-ux/­pancat
• Contact:
  Siegfried Dubois
• Participants:
  Siegfried Dubois, Claire Lemaitre
• Partner:
  INRAE

7.1.14 Mapler

• Name:
  Metagenome Assembly and Evaluation Pipeline for Long Reads
• Keywords:
  Metagenomics, Genome assembly, Benchmarking, Bioinformatics
• Functional Description:
  Mapler is a pipeline to compare the performances of long-read metagenomic assemblers. The pipeline is focused on assemblers for high fidelity long read sequencing data (e.g. pacBio HiFi), but it supports also assemblers for low-fidelity long reads (ONT, PacBio CLR) and hybrid assemblers. It currently compares metaMDBG, metaflye, Hifiasm-meta, opera-ms and miniasm as assembly tools, and uses reference-based, reference-free and binning-based evalutation metrics. It is implemented in Snakemake.
• URL:
  https://­gitlab.­inria.­fr/­mistic/­mapler
• Publication:
  hal-04142837
• Contact:
  Nicolas Maurice
• Participants:
  Nicolas Maurice, Claire Lemaitre, Riccardo Vicedomini, Clemence Frioux

7.1.15 HairSplitter

• Keywords:
  Bioinformatics, Genome assembly, Bacterial strains, Metagenomics
• Functional Description:
  HairSplitter takes as input a strain-oblivious assembly and sequencing reads and outputs a strain-separated assembly.
• URL:
  https://­github.­com/­RolandFaure/­Hairsplitter
• Contact:
  Roland Faure

8.1.1 Improvement of Approximate Membership Query data-structures with counts

Participants: Pierre Peterlongo, Lucas Robidou.

Approximate membership query (AMQ) data structures are widely used for indexing the presence of elements drawn from a large set. To represent the count of indexed elements, AMQ data structures can be generalized into "counting AMQ" data structures. This is for instance the case of the "counting Bloom filters". However, counting AMQ data structures suffer from false positive and overestimated calls. In this work we propose a novel strategy, called fimpera, that reduces the false-positive rate and overestimation rate of any counting AMQ data structure indexing k-mers (words of length k) from a set of sequences, along with their abundance. Applied on a counting Bloom filter, fimpera decreases its false-positive rate by an order of magnitude while reducing the number of overestimated calls. Furthermore, fimpera lowers the average difference between the overestimated calls and the ground truth. In addition, it slightly decreases the query time. fimpera does not require any modification of the original counting AMQ data structure, it does not generate false-negative calls, and causes no memory overhead. The unique drawback is that fimpera yields a negligible amount of a new kind of false positives and overestimated calls 18.

8.1.2 Indexing large sets of sequencing data

Participants: Pierre Peterlongo.

Public sequencing databases contain vast amounts of biological information, yet they are largely underutilized as it is challenging to efficiently search them for any sequence(s) of interest. We developped kmindex (7.1.2), an approach that can index thousands of metagenomes and perform sequence searches in a fraction of a second. The index construction is an order of magnitude faster than previous methods, while search times are two orders of magnitude faster. With negligible false positive rates below 0.01%, kmindex outperforms the precision of existing approaches by four orders of magnitude. We demonstrate the scalability of kmindex by successfully indexing 1,393 marine seawater metagenome samples from the Tara Oceans project. Additionally, we introduce the publicly accessible web server “Ocean Read Atlas”, which enables real-time queries on the Tara Oceans dataset 37.

8.1.3 Phylogenetic compression

Participants: Karel Břinda.

Comprehensive collections approaching millions of sequenced genomes have become central information sources in the life sciences. However, the rapid growth of these collections makes it effectively impossible to search these data using tools such as BLAST and its successors. To address this challenge, we developed a technique called phylogenetic compression, which uses evolutionary history to guide compression and efficiently search large collections of microbial genomes using existing algorithms and data structures 32. We showed that, when applied to modern diverse collections approaching millions of genomes, lossless phylogenetic compression improves the compression ratios of assemblies, de Bruijn graphs, and $k$-mer indexes by one to two orders of magnitude (implemented in a software tool called MiniPhy (7.1.10)). Additionally, we developed a pipeline called MOF-Search (7.1.9) for a BLAST-like search over these phylogeny-compressed reference data, and demonstrated it can align genes, plasmids, or entire sequencing experiments against all sequenced bacteria until 2019 on ordinary desktop computers within a few hours. Phylogenetic compression has broad applications in computational biology and may provide a fundamental design principle for future genomics infrastructure 32.

8.1.4 Processing compressed genomic data

Participants: Roland Faure, Baptiste Hilaire, Dominique Lavenier.

Efficiently managing large DNA datasets necessitates the development of highly effective sequence compression techniques to reduce storage and computational requirements. Here, we explore the potential of a lossy compression technique, Mapping-friendly Sequence Reductions (MSRs) which is a generalization of homopolymer compression to improve the accuracy of alignment tools. Essentially, MSRs deterministically transform sequences into shorter counterparts, in such a way that if an original query and a target sequence align, their reduced forms will align as well. While homopolymer compression is one example of an MSR, numerous others exist. These rapid computations yield lossy representations of the original sequences. Notably, the reduced sequences can be stored, aligned, assembled, and indexed much like regular sequences. MSRs could be used to improve the efficiency of taxonomic classification tools, by indexing and querying reduced sequences. Our experimentation with a mixture of 10 E. coli strains, demonstrates that this approach can yield greater precision than indexing and querying a reduced portion of k-mers. Other tasks could benefit from sequence reduction, such as mapping, genome assembly, and structural variant detection 26.

8.1.5 K-mer-based methods for ancient oral metagenomics

Participants: Riccardo Vicedomini.

Despite the implementation of strict laboratory protocols for controlling ancient DNA contamination, samples still remain highly vulnerable to environmental contamination. Such contamination can significantly alter microbial composition, leading to inaccurate conclusions in downstream analyses. Within the co-supervision of a PhD student at the Institut Pasteur (Paris, France), we contributed to the work related to two $k$-mer-based methods for addressing the following two challenges in ancient metagenomics: (i) microbial source tracking for contamination assessment 13 and (ii) read-level contamination removal 12.

The first method is based on the construction of a k-mer matrix 8 which stores the presence/absence of k-mers across multiple samples of different well-characterised sources. Such a matrix is then used to predict the proportion of each source in unknown input samples.

The second method allows to retain, from an input contaminated set, reads likely to belong to a specific source of interest. On synthetic data, it achieves over 89.53% sensitivity and 94.00% specificity. On real datasets, aKmerBroom shows higher read retainment (+60% on average) than competing methods.

8.2.1 Optimal Square Detection Over General Alphabets

Participants: Garance Gourdel.

Squares (fragments of the form $xx$, for some string $x$) are arguably the most natural type of repetition in strings. The basic algorithmic question concerning squares is to check if a given string of length n is square-free, that is, does not contain a fragment of such form. We show that testing square-freeness of a length-n string over general alphabet of size $\sigma$ can be done with $O(nlog\sigma )$ comparisons, and cannot be done with $o(nlog\sigma )$ comparisons. This result comes with an $O(nlog\sigma )$ time algorithm in the Word RAM model 21.

8.2.2 Compressed Indexing for Consecutive Occurrences

Participants: Garance Gourdel.

The fundamental question considered in algorithms on strings is that of indexing, that is, preprocessing a given string for specific queries. By now we have a number of efficient solutions for this problem when the queries ask for an exact occurrence of a given pattern $P$. However, practical applications motivate the necessity of considering more complex queries, for example concerning near occurrences of two patterns.

Recently, Bille et al.  45 introduced a variant of such queries, called gapped consecutive occurrences, in which a query consists of two patterns $P_1$ and $P_2$ and a range $[a,b]$, and one must find all consecutive occurrences ($q_1,q_2$) of $P_1$ and $P_2$ such that $q_2-q_1\in [a,b]$. By their results, we cannot hope for a very efficient indexing structure for such queries, even if $a=0$ is fixed (although at the same time they provided a non-trivial upper bound). Motivated by this, we focus on a text given as a straight-line program (SLP) and design an index taking space polynomial in the size of the grammar that answers such queries in time optimal up to polylog factors 22, 35.

8.2.3 Masked superstrings

Participants: Karel Břinda.

The popularity of $k$-mer-based methods has recently led to the development of compact k-mer-set representations, such as simplitigs/Spectrum-Preserving String Sets (SPSS), matchtigs, and eulertigs. These aim to represent $k$-mer sets via strings that contain individual $k$-mers as substrings more efficiently than the traditional unitigs. We demonstrated that all such representations can be viewed as superstrings of input $k$-mers, and as such can be generalized into a unified framework that we call the masked superstring of $k$-mers 39. We studied the complexity of masked superstring computation and proved NP-hardness for both $k$-mer superstrings and their masks 39. We then designed local and global greedy heuristics for efficient computation of masked superstrings, implemented them in a program called KmerCamel (7.1.11), and evaluate their performance using selected genomes and pan-genomes 39. Overall, masked superstrings unify the theory and practice of textual $k$-mer set representations and provide a useful framework for optimizing representations for specific bioinformatics applications.

8.3.1 Scaffolding step in genome assembly

Participants: Victor Epain, Rumen Andonov, Dominique Lavenier.

Scaffolding is an intermediate stage of fragment assembly. It consists in orienting and ordering the contigs obtained by the assembly of the sequencing reads. In the general case, the problem has been largely studied with the use of distances data between the contigs. Here we focus on a dedicated scaffolding for the chloroplast genomes. As these genomes are small, circular and with few repeats, numerous approaches have been proposed to assemble them. However, their specificities have not been sufficiently exploited. We give a new formulation for the scaffolding in the case of chloroplast genomes as a discrete optimisation problem, that we prove to be NP-Complete. It does not require distance information. It is focused on a genomic regions view, with the priority on scaffolding the repeats first. In this way, we encode the multimeric forms issue in order to retrieve several genome forms that can exist in the same chloroplast cell. In addition, we provide an Integer Linear Program (ILP) to obtain exact solutions that we implement in Python3 package khloraascaf. We test it on synthetic data to investigate its performance behaviour and its robustness against several chosen difficulties. While the scaffolding problem is traditionally defined with distances data, we show it is possible to avoid them in the case of the well-studied circular chloroplast genomes. The presented results show that the regions view seems to be sufficient to scaffold the repeats 34.

8.3.2 Local assembly with linked-read data

Participants: Anne Guichard, Fabrice Legeai, Claire Lemaitre.

Local assembly consists in reconstructing a sequence of interest from a sample of sequencing reads without having to assemble the entire genome, which is time and labor intensive. This is particularly useful when studying a locus of interest, for gap-filling in draft assemblies, as well as for alternative allele reconstruction of large insertion variants. Whereas linked-read technologies have a great potential to assemble specific loci as they provide long-range information, while maintaining the power and accuracy of short-read sequencing, there is a lack of local assembly tools for linked-read data.

We present MTG-Link (7.1.6), a novel local assembly tool dedicated to linked-reads. The originality of the method lies in its read subsampling step which takes advantage of the barcode information contained in linked-reads mapped in flanking regions of each targeted locus. Our approach relies then on our tool MindTheGap  10 to perform local assembly of each locus with the read subsets. MTG-Link tests different parameters values for gap-filling, followed by an automatic qualitative evaluation of the assembly.

We validated our approach on several datasets from different linked-read technologies. We show that MTG-Link is able to successfully assemble large sequences, up to dozens of Kb. We also demonstrate that the read subsampling step of MTG-Link considerably improves the local assembly of specific loci compared to other existing short-read local assembly tools. Furthermore, MTG-Link was able to fully characterize large insertion variants in a human genome and improved the contiguity of a 1.3 Mb locus of biological interest in several individual genomes of the mimetic butterfly Heliconius numata 15.

8.3.3 Separating strains in metagenome assemblies with long reads

Participants: Roland Faure, Dominique Lavenier, Rumen Andonov, Tam Khac Minh Truong.

Long read assemblers struggle to distinguish closely related strains of the same species and collapse them into a single sequence. This is very limiting when analysing a metagenome, as different strains can have important functional differences. We have designed a new methodology supported by a software called HairSplitter (7.1.15), which recovers the strains from a strain-oblivious assembly and long reads. The originality of the method lies in a custom variant calling step that works with erroneous reads and separates an unknown number of haplotypes. On simulated datasets, we show that HairSplitter significantly outperforms the state of the art when dealing with metagenomes containing many strains of the same species 25, 40

We also propose an alternative approach for the strain separation problem using Integer Linear Programming (ILP). We introduce a strain-separation module, strainMiner, and integrate it into an established pipeline to create strain-separated assemblies from sequencing data. Across simulated and real experiments encompassing a wide range of sequencing error rates (5-12%), our tool consistently compared favorably to the state-of-the-art in terms of assembly quality and strain reconstruction. Moreover, strainMiner substantially cuts down the computational burden of strain-level assembly compared to published software by leveraging the powerful Gurobi solver. We think the new methodological ideas presented in this paper will help democratizing strain-separated assembly 27.

8.4.1 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, one of the first SV genotypers 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 (7.1.5), which uses a sequence graph instead of linear sequences to represent the SVs.

In our method, we build a variation graph to represent in a single data structure all alleles of a set of SVs. The long reads are mapped on the variation graph and the resulting alignments that cover allele-specific edges in the graph are used to estimate the most likely genotype for each SV. Running SVJedi-graph on simulated sets of close and overlapping deletions showed that this graph model prevents the bias toward the reference alleles and allows maintaining high genotyping accuracy whatever the SV proximity, contrary to other state of the art genotypers. On the human gold standard HG002 dataset, SVJedi-graph obtained the best performances, genotyping 99.5% of the high confidence SV callset with an accuracy of 95% in less than 30 min 20.

8.4.2 Towards an edit distance between pangenome graphs

Participants: Siegfried Dubois, Claire Lemaitre.

A variation graph is a data structure that aims to represent variations among a collection of genomes. It is a sequence graph where each genome is embedded as a path in the graph with the successive nodes, along the path, corresponding to successive segments on the associated genome sequence. Shared subpaths correspond to shared genomic regions between the genomes and divergent path to variations: this structure features inversions, insertions, deletions and substitutions. The construction of a variation graph from a collection of chromosome-size genome sequences is a difficult task that is generally addressed using a number of heuristics such as those implemented in the state-of-the-art pangenome graph builders minigraph-cactus and pggb. The question that arises is to what extent the construction method influences the resulting graph and therefore to what extent the resulting graph reflects genuine genomic variations. We propose to address this question by constructing an edition script between two variation graphs built from the same set of genomes which provides a measure of similarity, and more importantly that enables to identify discordant regions between the two graphs. We proceed by comparing, for each genome, the two corresponding paths in the two graphs which correspond to two possibly different segmentations of the same genomic sequence. As such, for each interval defined by the nodes of the path of the genome in the first graph, we define a set of relations with the nodes of the second graph, such as equalities, prefix and suffix overlaps... which allows for a calculation of how many elementary operations, such as fusions and divisions of nodes, are required to go from one graph to another. We tested our method on variation graphs constructed using both simulated dataset as well as a real dataset made of 15 yeast telomere-to-telomere phased genome assemblies, with minigraph-cactus and pggb as the graph construction tools. We showed that two graphs built with the same tool, minigraph-cactus, but with different incorporation orders of genomes can be more different from one another than two graphs built with the two different tools. We also showed thar our distance allows to pinpoint and vizualize the specific areas of the graph and genomes that are impacted by the changes in segmentation. The method is implemented in a Python tool named Pancat (7.1.13) 33, 24.

8.4.3 Efficient detection of positive selection in large population genomic datasets

Participants: Pierre Peterlongo.

Genomic regions under positive selection harbor variation linked for example to adaptation. Most tools for detecting positively selected variants have computational resource requirements rendering them impractical on population genomic datasets with hundreds of thousands of individuals or more. We have developed and implemented an efficient haplotype-based approach able to scan large datasets and accurately detect positive selection. We achieve this by combining a pattern matching approach based on the positional Burrows–Wheeler transform with model-based inference which only requires the evaluation of closed-form expressions. We evaluate our approach with simulations, and find it to be both sensitive and specific. The computational resource requirements quantified using UK Biobank data indicate that our implementation is scalable to population genomic datasets with millions of individuals. Our approach may serve as an algorithmic blueprint for the era of “big data” genomics: a combinatorial core coupled with statistical inference in closed form 16.

8.5.1 Encoding data under biological and indexing constraints

Participants: Dominique Lavenier.

We have developed a method based on a dynamic sliding window encoding (DSWE) for storing encrypted data in a DNA form taking into account biological constraints and prohibited nucleotide motifs used for data indexing. Its originality is twofold. First, it takes advantage of variable length DNA codewords to avoid homopolymers longer than $N$ bases when encoding binary data. Second, it relies on a sliding window to prevent the creation of prohibited motifs of nucleotides, adding non-coding bases when necessary. Contrarily to existing schemes, scaling DSWE to high values of $N$ and of numbers of prohibited motifs is extremely simple. It is furthermore independent of the cryptosystem. We provide the theoretical information rate of our proposal for a given number of prohibited motifs and a maximum homopolymer length. Experiments show that in general, it offers much higher performances than existing schemes. 23.

8.5.2 In vitro construction of long artificial DNA molecule for encoding numerical data

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

In absence of DNA template, the ab initio production of long double-stranded DNA molecules of predefined sequences is particularly challenging. The DNA synthesis step remains a bottleneck for many applications such as functional assessment of ancestral genes, analysis of alternative splicing or DNA-based data storage. We worked on a fully in vitro protocol to generate very long double-stranded DNA molecule starting from commercially available short DNA blocks in less than 3 days. This innovative application of Golden Gate assembly allowed us to streamline the assembly process to produce a 24 kb long DNA molecule storing part of the Universal Declaration of Human rights and citizens. The DNA molecule produced can be readily cloned into suitable host/vector system for amplification and selection. 36.

8.6.1 Data structures

In the framework of the European BioPIM project, we have studied (from a parallelism point of view) a number of data structures used extensively in genomics software to assess the benefits of implementing them on PIM architectures. The following data structures have been studied: bloom filters, Burrows–Wheeler transform and hash tables. A detailed report on the evaluations is currently available on the GenoPIM project website.

8.6.2 Programing Environment

The programming model for processing-in-memory is not yet well defined. The question of automatic (or semi-automatic) parallelization remains open. The tools available for programming a complete application on a PIM-equipped architecture are relatively low-level. We are working on the design of a C++ programming environment to unify the programming of CPU and PIM memory processing.

8.6.3 Applications

Memory components based on PIM principles have been developed by UPMEM, a young startup founded in 2015. The company has designed an innovative DRAM processing unit (DPU), a RISC processor integrated directly into the memory chip, on the DRAM die. We are using a UPMEM PIM server equipped with 160 GB of PIM memory and 256 GB of legacy memory to carry out full-scale experiments on the implementation of several genomic software for long DNA comparison, bacterial genome comparison, protein sequence alignment, data compression and sorting algorithms.

Initial experiments show that, for certain applications, it is possible to achieve a 20-fold acceleration compared with standard multicore platforms.

8.7.1 Benchmarking metagenome assemblers for long reads and application to soil microbiomes

Participants: Claire Lemaitre, Nicolas Maurice, Riccardo Vicedomini.

We developed an automated pipeline, Mapler (7.1.14), to assess the performances of long read metagenome assemblers, with a special focus on tools dedicated to high fidelity long reads such as PacBio HiFi reads. It compares five assembly tools, namely metaMDBG, metaflye, Hifiasm-meta, opera-ms and miniasm, and uses various evaluation metrics such as reference-based, reference-free and binning-based evalutation metrics. We applied this pipeline on several real metagenome datasets of various complexities, including publicly available mock communities with well characterized species contents and tunnel culture soil metagenomes with high and unknown species complexities. We showed that the different evaluation metrics are complementary and that high fidelity long read data allows drastic improvements in the number of obtained good quality Metagenome Assembled Genomes (MAGs) with respect to low-fidelity long reads or short read data, with MetaMDBG out-performing other HiFi-dedicated assemblers. However, for soil metagenomes most reconstructed MAGs remain of low quality because of the very large number of species in these microbiomes and the probable under-sampling of this diversity by this type of sequencing 41.

8.7.2 Introduction to bioinformatics methods for metagenomic and metatranscriptomic analyses

Participants: Claire Lemaitre.

In this book chapter, we review the different bioinformatics analyses that can be performed on metagenomics and metatranscriptomics data. We present the differences of this type of data compared to standard genomics data and highlight the methodological challenges that arise from it. We then present an overview of the different methodological approaches and tools to perform various analyses such as taxonomic annotation, genome assembly and binning and de novo comparative genomics 28.

8.8.1 Genomics and transcriptomics of Brassicaceae plants and agro-ecosystem 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 and transcriptomics projects in the field of agricultural research. We participated in the genome assembly and analyses of some major agricultural pests or their natural ennemies. In particular, we performed a genome-wide identification of lncRNAs associated with viral infection in the lepidopteran pest Spodoptera frugiperda 19 and participated in a detailed study of a genomic region linked to reproductive mode variation in the pea aphid 17.

In most cases, the genomes and their annotations were hosted in the BIPAA information system, allowing collaborative curation of various sets of genes and leading to novel biological findings 42.

8.8.2 First chromosome scale genomes of ithomiine butterflies

Participants: Fabrice Legeai, Claire Lemaitre.

In the framework of a former ANR project (SpecRep 2014-2019), we worked on de novo genome assembly of several ithomiine butterflies. Due to their high heterozygosity level and to sequencing data of various quality, this was a challenging task and we tested numerous assembly tools. Finally, this work led to the generation of high-quality, chromosome-scale genome assemblies for two Melinaea species, M. marsaeus and M. menophilus, and a draft genome of the species Ithomia salapia. We obtained genomes with a size ranging from 396 Mb to 503 Mb across the three species and scaffold N50 of 40.5 Mb and 23.2 Mb for the two chromosome-scale assemblies. Various genomics and comparative genomics analyses were performed and revealed notably independent gene expansions in ithomiines and particularly in gustatory receptor genes.

These three genomes constitute the first reference genomes for the ithomiine butterflies (Nymphalidae: Danainae), which represent the largest known radiation of Müllerian mimetic butterflies and dominate by number the mimetic butterfly communities. This is therefore a valuable addition and a welcome comparison to existing biological models such as Heliconius, and will enable further understanding of the mechanisms of mimetism and adaptation in butterflies 14.

8.8.3 The Silene latifolia genome and its giant Y chromosome

Participants: Claire Lemaitre.

In some species, the Y is a tiny chromosome but the dioecious plant Silene latifolia has a giant 550 Mb Y chromosome, which has remained unsequenced so far. We participated in a collaborative project that sequenced and obtained a high-quality male S. latifolia genome. We participated in particular in the comparative analysis of the sex chromosomes with outgroups, that showed that the Y is surprisingly rearranged and degenerated for a 11 MY-old system. Recombination suppression between X and Y extended in a stepwise process, and triggered a massive accumulation of repeats on the Y, as well as in the non-recombining pericentromeric region of the X, leading to giant sex chromosomes 38.

10.1.1 Visits of international scientists

10.2.1 H2020 projects

10.2.2 Other european programs/initiatives

10.3.1 PEPR

10.3.2 ANR

10.3.3 Inria Exploratory Action

10.4.1 LABEX CominLabs

11.1.1 Scientific events: selection

11.1.2 Journal

11.1.3 Invited talks

• D. Lavenier, "Exploring genomic algorithms on Processing-in-Memory Architecture", 5th workshop on Accelerator Architecture for Computational Biology and Bioinformatics, ISCA 2023, Orlando, Florida, June 2023
• P. Peterlongo, Keynote, "Indexing Large Metagenomic Projects. Application to the Tara Oceans Datasets", WCTA 2023, Pisa Italy, September 2023.
• K. Břinda, "Rapid inference of antibiotic resistance and susceptibility by Genomic Neighbor Typing", Institute of Medical Microbiology, University Hospital of Düsseldorf, Germany, June 2023.
• K. Břinda, "Under the hood: The role of string algorithms in contemporary biological research", CPM 2023 Summer School, ENS Paris, France, June 2023.
• K. Břinda, "Efficient and robust search of microbial genomes via phylogenetic compression", joint Alpaca/Pangaia consortia seminar, online, October 2023.
• K. Břinda, "K-mers and their graphs in computational biology", Graphs and Bioinformatics 2023, Sorbonne University, Paris, France, November 2023.
• K. Břinda, "Efficient search of microbial genomes via phylogenetic compression", Bioinformatics seminar, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic, December 2023.
• K. Břinda, "Towards diagnostics of antibiotic resistance within an hour", DIANA Biotechnologies, Prague, Czech Republic, December 2023.

11.1.4 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) (170 french participants) [C. Lemaitre]
• Animator of the INRAE Center for Computerized Information Treatment "BARIC" [F. Legeai]
• Member of the PEPR MoleculArxiv Executive Committee [D. Lavenier]

11.1.5 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]

11.1.6 Research administration

• Corresponding member of COERLE (Inria Operational Committee for the assessment of Legal and Ethical risks) [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) [P. Peterlongo]
• Chair of the committee in charge of all the temporary recruitments (“Commission Personnel”) at Inria Rennes-Bretagne Atlantique and IRISA [D. Lavenier]
• Recruitment committees: member of the CR/ISFP recruitment committee of the Rennes Inria center [C. Lemaitre], president of the recruitment jury of an INRAe engineer [F. Legeai]
• Members of the board of the Brittany doctoral students' association Nicomaque [V. Epain, G. Gourdel]

11.2.1 Teaching administration

• In charge of the master's degree "Nutrition Sciences des Aliments" (NSA) of University of Rennes (72 students) [E. Roux]

11.2.2 Teaching

• Licence : R. Andonov, Models and Algorithms in Graphs, 100h, L3, Univ. Rennes, France.
• Licence : E. Roux, Biochemistry, 90h, L1 and L3, Univ. Rennes, France.
• Licence : S. Dubois, Java, 21h, L1 ISTN, Univ. Rennes, France.
• Master : R. Andonov, Modeling and Discrete Optimisation (MOD), 82h, M1 Miage, Univ. Rennes, France.
• Master : R. Faure, Object Oriented Programming (OOP), 32h, M1, Univ. Rennes, France
• Master : C. Lemaitre, P. Peterlongo, Algorithms on Sequences, 52h, M2, Univ. Rennes, France.
• Master : R. Vicedomini, K. Břinda, 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 : E. Roux, biochemistry, 120h, M1 and M2, Univ. Rennes, France.

11.2.3 PhD Supervision

• PhD: G. Gourdel, Sketch-based approaches to process massive string data 30, defended: 26/10/2023, P. Peterlongo.
• PhD: V. Epain, Assemblage de fragments ADN : structures de graphes et scaffolding de génomes de chloroplastes, 29 defended: 27/11/2023, R. Andonov, D. Lavenier, JF Gibrat.
• PhD: L. Robidou, Search engine for genomic sequencing data , defended 21/09/2023, P. Peterlongo. 31
• PhD in progress: S. Romain, Genome graph data structures for Structural Variation analyses in butterfly genomes, since 01/09/2021, C. Lemaitre, F. Legeai.
• PhD in progress: K. Hannoush, Pan-genome graph update strategies, since 01/09/2021, P. Peterlongo, C. Marchet.
• PhD in Progress: R. Faure, Recovering end-to-end phased genomes, since 01/10/2021, D. Lavenier, J-F. Flot.
• PhD in progress: M. Mognol, Processing-in-Memory, since 01/04/2022, D. Lavenier.
• PhD in progress: S. Dubois, Characterizing structural variation in pangenome graphs, since 15/09/2023, C. Lemaitre, T. Faraut, M. Zynticki.
• PhD in progress: N. Maurice, Sequence algorithmics for de novo genome assembly from complex metagenomic data, since 01/10/2023, C. Lemaitre, C. Frioux, R. Vicedomini.
• PhD in progress: V. Levallois, Indexing genomic data, since 01/10/2023, P. Peterlongo.
• PhD in progress: C. Duitama (Sequence Bioinformatics group, Institut Pasteur, Paris), Algorithms based on k-mers for ancient oral metagenomics, since 07/10/2020, R. Chikhi, H. Richard, R. Vicedomini.

11.2.4 Juries

• Member of HDR thesis jury
  • Mikael Salson, Univ. Lille, Nov. 2023 [D. Lavenier]
• President of PhD thesis jury
  • L. Robidou, Univ. Rennes, Sept 2023 [J. Nicolas]
• Member of PhD thesis jury
  • Eva Gil San Antonio, Univ. Nice, March 2023 [D. Lavenier]
  • Sebastian Schmidt, Univ. Helsinki, Finland, August 2023 [P. Peterlongo]
• Member of PhD thesis committee
  • Francesco Andreace, Pasteur Paris, [P. Peterlongo]
  • Thomas Baudeau, Univ. Lille [C. Lemaitre]
  • Antoine Dequay, Univ. Rennes [D. Lavenier]
  • Aref Ezzeddine, IMT-A, [D. Lavenier]
  • Khodor Hannoush, Univ. Rennes [K. Břinda]
  • Léo de La Fuente, Univ. Rennes [D. Lavenier]
  • Maël Lefeuvre, MNHN [J. Nicolas]
  • Xavier Pic, Univ. Nice [D. Lavenier]
  • Léa Vandamme, Univ. Lille, [P. Peterlongo]

11.3.1 Internal or external Inria responsibilities

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

11.3.2 Articles and contents

• Popularization article in Interstices "Le SARS-CoV-2 : une expérience inédite de surveillance génomique mondiale" (link) 44 [C. Lemaitre]
• Popularization article in Interstices "Stocker les données : la piste prometteuse de l’ADN" (link) 43 [D. Lavenier]

11.3.3 Education

• Conferences IUT Genie Biologique IUT Quimper, "Stockage de données sur ADN" [D. Lavenier]

11.3.4 Interventions

• Chiche! Interventions in high school classes to make high school students aware of research careers in the digital sector. Three interventions made in 2023 [P. Peterlongo]
• Organisation and animation of the Inria 2023 Ethics Workshop by Nicomaque [V. Epain]

International journals

• 12 articleC.Camila Duitama González, S.Samarth Rangavittal, R.Riccardo Vicedomini, R.Rayan Chikhi and H.Hugues Richard. aKmerBroom: Ancient oral DNA decontamination using Bloom filters on k-mer sets.iScience2611November 2023, 108057HALDOIback to text
• 13 articleC.Camila Duitama González, R.Riccardo Vicedomini, T.Téo Lemane, N.Nicolas Rascovan, H.Hugues Richard and R.Rayan Chikhi. decOM: similarity-based microbial source tracking of ancient oral samples using k-mer-based methods.Microbiome111November 2023, 1-12HALDOIback to text
• 14 articleJ.Jérémy Gauthier, J.Joana Meier, F.Fabrice Legeai, M.Melanie McClure, A.Annabel Whibley, A.Anthony Bretaudeau, H.Hélène Boulain, H.Hugues Parrinello, S. T.Sam T. Mugford, R.Richard Durbin, C.Chenxi Zhou, S.Shane McCarthy, C. W.Christopher W. Wheat, F.Florence Piron-Prunier, C.Christelle Monsempes, M.Marie‐Christine François, P.Paul Jay, C.Camille Noûs, E.Emma Persyn, E.Emmanuelle Jacquin-Joly, C.Camille Meslin, N.Nicolas Montagné, M.Marianne Elias and C.Claire Lemaitre. First chromosome scale genomes of ithomiine butterflies (Nymphalidae: Ithomiini): comparative models for mimicry genetic studies.Molecular Ecology Resources2023, 1-14HALDOIback to text
• 15 articleA.Anne Guichard, F.Fabrice Legeai, D.Denis Tagu and C.Claire Lemaitre. MTG-Link: leveraging barcode information from linked-reads to assemble specific loci.BMC Bioinformatics241July 2023, 284HALDOIback to text
• 16 articleB.Benedikt Kirsch-Gerweck, L.Leonard Bohnenkämper, M. T.Michel T Henrichs, J. N.Jarno N Alanko, H.Hideo Bannai, B.Bastien Cazaux, P.Pierre Peterlongo, J.Joachim Burger, J.Jens Stoye and Y.Yoan Diekmann. HaploBlocks: Efficient Detection of Positive Selection in Large Population Genomic Datasets.Molecular Biology and Evolution403March 2023, 1-12HALDOIback to text
• 17 articleM.Maud Rimbault, F.Fabrice Legeai, J.Jean Peccoud, L.Lucie Mieuzet, E.Elsa Call, P.Pierre Nouhaud, H.Hélène Defendini, F.Frédérique Mahéo, W.William Marande, N.Nicolas Théron, D.Denis Tagu, G.Gaël Le Trionnaire, J.-C.Jean-Christophe Simon and J.Julie Jaquiéry. Contrasting Evolutionary Patterns Between Sexual and Asexual Lineages in a Genomic Region Linked to Reproductive Mode Variation in the pea aphid.Genome Biology and Evolution159September 2023, evad168HALDOIback to text
• 18 articleL.Lucas Robidou and P.Pierre Peterlongo. fimpera: drastic improvement of Approximate Membership Query data-structures with counts.Bioinformatics395May 2023, 1-16HALDOIback to text
• 19 articleS.Stéphanie Robin, F.Fabrice Legeai, V.Véronique Jouan, M.Mylène Ogliastro and I.Isabelle Darboux. Genome-wide identification of lncRNAs associated with viral infection in Spodoptera frugiperda.Journal of General Virology1042February 2023, 1-19HALDOIback to text
• 20 articleS.Sandra Romain and C.Claire Lemaitre. SVJedi-graph: improving the genotyping of close and overlapping structural variants with long reads using a variation graph.Bioinformatics39Supplement_1June 2023, 270 - 278HALDOIback to textback to text

International peer-reviewed conferences

• 21 inproceedingsJ.Jonas Ellert, P.Paweł Gawrychowski and G.Garance Gourdel. Optimal Square Detection Over General Alphabets.Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)SODA - 2023 Annual ACM-SIAM Symposium on Discrete AlgorithmsFlorence, ItalySociety for Industrial and Applied MathematicsFebruary 2023, 5220-5242HALDOIback to text
• 22 inproceedingsP.Paweł Gawrychowski, G.Garance Gourdel, T.Tatiana Starikovskaya and T. A.Teresa Anna Steiner. Compressed Indexing for Consecutive Occurrences.34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023)CPM 2023 - 34th Annual Symposium on Combinatorial Pattern MatchingParis, FranceSchloss Dagstuhl - Leibniz-Zentrum für Informatik2023HALDOIback to text

Conferences without proceedings

• 23 inproceedingsC.Chloé Berton, G.Gouenou Coatrieux, D.Dominique Lavenier and H.Haddad S.. Dynamic sliding window encoding for data storageon DNA under biological and indexing constraints.EUSIPCO 2023 - 31st European Signal Processing ConferenceHelsiinki, FinlandSeptember 2023, 1-5HALDOIback to text
• 24 inproceedingsS.Siegfried Dubois, B.Benjamin Linard, M.Matthias Zytnicki, C.Claire Lemaitre and T.Thomas Faraut. Towards an edit distance between pangenome graphs.SeqBIM 2023 - Journées sur les Séquences en Bioinformatique, Informatique et MathématiquesLille, France2023, 1-2HALback to text
• 25 inproceedingsR.Roland Faure, J.-F.Jean-François Flot and D.Dominique Lavenier. HairSplitter: separating strains in metagenome assemblies with long reads.JOBIM 2023 - Journées Ouvertes en Biologie, Informatique et MathématiquesPlouzané, FranceJuly 2023, 1-8HALback to text
• 26 inproceedingsR.Roland Faure, B.Baptiste Hilaire and D.Dominique Lavenier. Mapping-friendly Sequence Reductions to process compressed genomic data.SeqBIM 2023Lille, France2023, 1-1HALback to text
• 27 inproceedingsT. K.Tam Khac Minh Truong, R.Roland Faure and R.Rumen Andonov. Assembling close strains in metagenome assemblies using discrete optimization.BIOINFORMATICS 2024Rome, ItalyFebruary 2024HALback to text

Scientific book chapters

• 28 inbookC.Cervin Guyomar and C.Claire Lemaitre. Métagénomique et métatranscriptomique.Des séquences aux graphesMéthodes et structures discrètes pour la bioinformatiqueISTEJuly 2023, 1-36HALback to text

Doctoral dissertations and habilitation theses

• 29 thesisV.Victor Epain. DNA fragment assembly: graph structures and chloroplast genome scaffolding: Comparative analyses, formulations and implementations.Université de RennesDecember 2023HALback to text
• 30 thesisG.Garance Gourdel. Sketch-based approaches to process massive string data.Université de RennesOctober 2023HALback to text
• 31 thesisL.Lucas Robidou. Search engine for genomic sequencing data.InriaSeptember 2023HALback to text

Reports & preprints

• 32 miscK.Karel Břinda, L.Leandro Lima, S.Simone Pignotti, N.Natalia Quinones-Olvera, K.Kamil Salikhov, R.Rayan Chikhi, G.Gregory Kucherov, Z.Zamin Iqbal and M.Michael Baym. Efficient and Robust Search of Microbial Genomes via Phylogenetic Compression.April 2023HALDOIback to textback to text
• 33 reportS.Siegfried Dubois, C.Claire Lemaitre, T.Thomas Faraut and M.Matthias Zytnicki. Comparaison et visualisation de graphes de pangénomes.Université de rennesJune 2023, 1-38HALback to text
• 34 miscV.Victor Epain and R.Rumen Andonov. Global exact optimisations for chloroplast genome multimeric forms scaffolding.June 2023HALback to text
• 35 miscP.Paweł Gawrychowski, G.Garance Gourdel, T.Tatiana Starikovskaya and T. A.Teresa Anna Steiner. Compressed Consecutive Pattern Matching.October 2023HALback to text
• 36 miscJ.Julien Leblanc, O.Olivier Boulle, E.Emeline Roux, J.Jacques Nicolas, D.Dominique Lavenier and Y.Yann Audic. In vitro construction and long read sequencing analysis of a 24 kb long artificial DNA sequence encoding the Universal Declaration of the Rights of Man and of the Citizen.June 2023HALDOIback to text
• 37 miscT.Téo Lemane, N.Nolan Lezzoche, J.Julien Lecubin, E.Eric Pelletier, M.Magali Lescot, R.Rayan Chikhi and P.Pierre Peterlongo. kmindex and ORA: indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets.June 2023HALDOIback to text
• 38 miscC.Carol Moraga, C.Catarina Branco, Q.Quentin Rougemont, P.Paris Veltsos, P.Pavel Jedlička, A.Aline Muyle, M.Melissa Hanique, E.Eric Tannier, X.Xiaodong Liu, E.Eddy Mendoza-Galindo, C.Claire Lemaitre, P.Peter Fields, C.Corinne Cruaud, K.Karine Labadie, C.Caroline Belser, J.Jerome Briolay, S.Sylvain Santoni, R.Radim Cegan, R.Raquel Linheiro, R.Ricardo de la Vega, G.Gabriele Adam, A. E.Adil El Filali, V.Vinciane Mossion, A.Adnane Boualem, R.Raquel Tavares, A.Amine Chebbi, R.Richard Cordaux, C.Cécile Fruchard, D.Djivan Prentout, A.Amandine Velt, B.Bruno Spataro, S.Stephane Delmotte, L.Laura Weingartner, H.Helena Toegelová, Z.Zuzana Tulpová, P.Petr Cápal, H.Hana Šimková, H.Helena Štorchová, M.Manuela Krüger, O.Oushadee Abeyawardana, D.Douglas Taylor, M.Matthew Olson, D.Daniel Sloan, S.Sophie Karrenberg, L.Lynda Delph, D.Deborah Charlesworth, T.Tatiana Giraud, A.Abdelhafid Bendahmane, A. D.Alex Di Genova, A.Amin Madoui, R.Roman Hobza and G.Gabriel Marais. The Silene latifolia genome and its giant Y chromosome.September 2023HALDOIback to text
• 39 miscO.Ondřej Sladký, P.Pavel Veselý and K.Karel Břinda. Masked superstrings as a unified framework for textual k-mer set representations.February 2023HALDOIback to textback to textback to text

Other scientific publications

• 40 inproceedingsR.Roland Faure, J.-F.Jean-François Flot and D.Dominique Lavenier. HairSplitter: separating similar strains in metagenome assembly.ISMB/ECCB 2023 - 31st Annual Intelligent Systems For Molecular Biology and the 22nd Annual European Conference on Computational BiologyLyon, France2023, 1-1HALback to text
• 41 thesisN.Nicolas Maurice. Assemblage métagénomique d'écosystèmes complexes avec différentes technologies de séquençage de 3ème génération.Université de BordeauxJune 2023, 33HALback to text
• 42 inproceedingsS.Stéphanie Robin, A.Anthony Bretaudeau and F.Fabrice Legeai. BIPAA, Bioinformatics Platform for the Agroecosystems Arthropods..JOBIM 2023 - Journées Ouvertes en Biologie, Informatique et MathématiquesPlouzané (Brest Métropole), FranceJune 2023HALback to text

Scientific popularization

• 43 articleD.Dominique Lavenier, M.Marc Antonini, Y.Yannick Rondelez and A.A.J. Genot. Stocker les données : la piste prometteuse de l’ADN.IntersticesFebruary 2023HALback to text
• 44 articleH.Hélène Touzet, M.Mikaël Salson, C.Claire Lemaitre and F.Florence Débarre. Le SARS-CoV-2 : une expérience inédite de surveillance génomique mondiale.IntersticesMay 2023HALback to text