2024Activity 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 enemies 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 significant 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 muset

• Keywords:
  Unitig matrices, De Bruijn graphs, K-mer
• Functional Description:
  MUSET is a software tool designed to generate an abundance unitig matrix from a collection of input samples in FASTA/Q format. It also offers a comprehensive suite of tools for manipulating k-mer matrices, along with a script for efficiently generating a presence-absence unitig matrix.
• URL:
  https://­github.­com/­camiladuitama/­muset
• Contact:
  Riccardo Vicedomini
• Participants:
  Riccardo Vicedomini, Francesco Andreace, Yoann Dufresne, Rayan Chikhi, Camila Duitama
• Partner:
  Sequence Bioinformatics

7.1.5 cdbgtricks

• Keywords:
  De Bruijn graphs, Short reads
• Functional Description:
  Cdbgtricks is a C++11 modular tool, mainly designed to update a compacted de Bruijn graph, when adding new sequences. In addition it indexes the graph, updates the index when adding sequences and so it enables querying sequences on the graph.
• URL:
  https://­github.­com/­khodor14/­Cdbgtricks
• Publication:
  hal-04765742
• Contact:
  Pierre Peterlongo

7.1.6 back to sequences

• Keywords:
  Kmers, Genomic sequence
• Functional Description:
  “back to sequences” is a software program that allows you to find the origin of words of size k (k-mers) in raw sequencing data. This is a common operation when analyzing this type of data.
• Contact:
  Pierre Peterlongo

7.1.7 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

7.1.8 Phylign

• Name:
  Phylign
• 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

7.1.9 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

7.1.10 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.
• Release Contributions:
  https://github.com/dubssieg/gfagraphs/commits/v0.3.0
• URL:
  https://­github.­com/­Tharos-ux/­gfagraphs
• Contact:
  Siegfried Dubois
• Participants:
  Siegfried Dubois, Claire Lemaitre
• Partner:
  INRAE

7.1.11 pancat

• Name:
  PANgenome Comparison and Analysis 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.
• Release Contributions:
  Changelog available here: https://github.com/dubssieg/pancat/compare/v0.2.0...v0.3.0
• Contact:
  Siegfried Dubois
• Participants:
  Siegfried Dubois, Claire Lemaitre
• Partner:
  INRAE

7.1.12 rs-pancat-compare

• Keyword:
  Pangenomics
• Functional Description:
  Program that calculates the distance between two GFA (Graphical Fragment Assembly) files. It takes in the file paths of the two GFA files. The program first identifies the common paths between the two graphs by finding the intersection of their path names. For each common path, the program reads those and output differences in segmentation in-between them. The purpose is to output the necessary operations (merges and splits) required to transform the graph represented by the first GFA file into the graph represented by the second GFA file.
• Contact:
  Siegfried Dubois
• Partner:
  INRAE

7.1.13 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

7.1.14 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

7.1.15 Alice

• Keywords:
  Genome assembly, Haplotyping, NGS
• Functional Description:
  Assemble DNA sequencing high-fidelity reads into full genomes. Based on the newly introduced MSR sketching
• URL:
  https://­github.­com/­rolandfaure/­alice-asm
• Contact:
  Roland Faure

7.1.16 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

8.1.1 FMSI k-mer indexing via masked superstrings

Participants: Karel Břinda.

The exponential growth of DNA sequencing data limits the searchable proportion of the data. In this context, tokenization of genomic data via their k-merization provides a path towards efficient algorithms for their compression and search. However, indexing even single k-mer sets still remains a significant bioinformatics challenge, especially if k-mer sets are sketched or subsampled. Here, we develop the FMSI index, a space-efficient data structure for unconstrained k-mer sets, based on approximated shortest superstrings and the Masked Burrows Wheeler Transform (MBWT), an adaptation of the BWT for masked superstrings. We implement this in a program called FMSI, and via extensive evaluations using prokaryotic pan-genomes, we show FMSI substantially improves space efficiency compared to the state of the art, while maintaining a competitive query time. Overall, our work demonstrates that superstring indexing is a highly general, parameter-free approach for modern k-mer sets, without imposing any constraints on their structure 47, 40.

8.1.2 Dynamic k-mer set operations via masked superstrings

Participants: Karel Břinda.

The design of efficient dynamic data structures for large k-mer sets belongs to central challenges of sequence bioinformatics. Recent advances in compact k-mer set representations via simplitigs/Spectrum-Preserving String Sets, culminating with the masked superstring framework, have provided data structures of remarkable space efficiency for wide ranges of k-mer sets. However, the possibility to perform set operations remained limited due to the static nature of the underlying compact representations. Here, we develop f-masked superstrings, a concept combining masked superstrings with custom demasking functions f to enable efficient k-mer set operations via string concatenation. Combined with the FMSI index for masked superstrings, we obtain a memory-efficient k-mer index supporting set operations via Burrows-Wheeler Transform merging. The framework provides a promising theoretical solution to a pressing bioinformatics problem and highlights the potential of f-masked superstrings to become an elementary data type for k-mer sets 47, 40, 48.

8.1.3 Optimized K-mer Matching For Million-Genome Collections On Laptops

Participants: Francesca Brunetti, Karel Břinda.

Bacteria are pivotal to human health, and their swift identification via genome sequencing technologies is critical, as evidenced by extensive bacterial genome databases like 661k and AllTheBacteria, which facilitate rapid diagnostics and epidemiological surveillance. However, real-time genome alignment on portable devices is hampered by existing technologies; while BLAST provides near-real-time searches, it requires substantial computational resources, and k-mer indexes, though advanced, fail to scale efficiently. Phylign offers a promising solution by managing alignments on a standard laptop efficiently, but its application is limited in critical point-of-care decisions due to dependencies like COBS, which are only optimal for short or near-exact matches. Our methodology utilizes phylogenetic compression to enhance k-mer matching across large genome collections on portable devices, aiming to significantly reduce processing times while retaining accuracy. We demonstrate through performance evaluations that replacing COBS with our phylogenetically compressed system can expedite k-mer matching by more than sixfold for extensive sequences, offering practical applications for biologists and epidemiologists using standard laptops 32.

8.1.4 Updating compacted de Bruijn graphs

Participants: Khodor Hannoush, Pierre Peterlongo.

We proposed Cdbgtricks, a new method for updating a compacted de Bruijn graph when adding novel sequences, such as full genomes. This method indexes the graph, enabling to identify in constant time the location (unitig and offset) of any queried k-mer. The update operation that is proposed also updates the index. Results show that Cdbgtricks is faster than main state-of-the-art tools, Bifrost and GGCAT. Cdbgtricks also benefits from the index of the graph to provide new functionalities, such as reporting the subgraph that shares a desired percentage of k-mers with a query sequence with the ability to query a set of reads. The open-source Cdbgtricks software is available at the following repository.

Cdbgtricks was presented during the Prague Stringology Conference and is published in the proceedings as well as in BiorXiv 27. This work is also a main contribution of the PhD of Khodor Hannoush 43 and was also presented during seqBIM2024 28.

8.1.5 The Backpack Quotient Filter: a dynamic and space-efficient data structure for querying k-mers with abundance

Participants: Victor Levallois, Pierre Peterlongo.

As databases storing genomic information, such as the European Nucleotide Archive, continue to grow exponentially, efficient solutions for data manipulation are imperative. One fundamental operation that remains challenging is querying these databases to determine the presence or absence of specific sequences and their abundance within datasets. We introduce a novel data structure indexing $k$-mers, the Backpack Quotient Filter (BQF), which serves as an alternative to the Counting Quotient Filter 57 (CQF). The BQF offers enhanced space efficiency compared to the CQF while retaining key properties, including abundance information and dynamicity, with a negligible false positive rate, below ${10}^{-5}$%. The approach involves a redefinition of how abundance information is handled within the structure, alongside with an independent strategy for space efficiency. We show that the BQF uses 4x less space than the CQF on some of the most complex data to index: sea-water metagenomics sequences. Furthermore, we show that space efficiency increases as the amount of data to be indexed increases, which is in line with the original objective of scaling to ever-larger datasets 22.

8.1.6 Improve the resizing of the Backpack Quotient Filter

Participants: Nicolas Buchin, Victor Levallois, Pierre Peterlongo.

The BQF, presented section 8.1.5 is dynamic in nature, allowing for the addition of new items at any time. Upon reaching saturation of the structure, its capacity can be doubled. However, in the initial version of the BQF, this operation was not optimized, offering opportunities for improvements. In the work 33 presented at seqBIM2024, we proposed a novel algorithm that optimizes the resizing operation of the BQF data structure. The approach capitalizes on precomputed metadata. This makes it possible to efficiently determine the location of previously inserted values in the new resized structure. This drastically limits the number of steps required during this resizing operation, thus limiting the computation time, and also limiting the memory impact. The results show that this new approach enables one to gain a factor 4× to 8× in term of computation time. Notably, these tests have shown that the proposed enhancements enable to gain a factor 10 in terms of memory usage. It should be noted that this algorithm is not limited to the BQF, but can also be adapted to the Rank and Select Quotient Filter 57 and its variations, including the Counting Quotient Filter.

8.1.7 Back to sequences: Find the origin of k-mers

Participants: Pierre Peterlongo.

A vast majority of bioinformatics tools dedicated to the treatment of raw sequencing data heavily use the concept of $k$-mers. This enables us to reduce the redundancy of data (and thus the memory pressure), to discard sequencing errors, and to dispose of objects of fixed size that can be easily manipulated and compared to each other. A drawback is that the link between each $k$-mer and the original set of sequences to which it belongs is lost. Given the volume of data considered in this context, recovering this association is costly. In the work published in JOSS 13, we presented “back to sequences”, a simple tool designed to index a set of $k$-mers of interest and to stream a set of sequences, extracting those containing at least one of the indexed $k$-mer. In addition, the occurrence positions of $k$-mers in the sequences can be provided. Our results show that “back to sequence” streams $\approx$ 200 short reads per millisecond, allowing us to search $k$-mers in hundreds of millions of reads in a matter of a few minutes.

8.1.8 Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets with kmindex and ORA.

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 presented kmindex, published in Nature Computation Science 21, 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. In this work, we also demonstrated 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.

8.1.9 Logan Search, a k-mer search engine for all Sequence Read Archive public accessions

Participants: Pierre Peterlongo.

There are 50 petabases of freely-available DNA sequencing data. We proposed the yet unpublished Logan Search server, built using and running thanks to kmindex 21. This server allows you to search for any DNA sequence in minutes, bringing Earth’s largest genomic resource to your fingertips. Under the hood, we built a 1 petabyte k-mer index for all 27 million sequencing datasets in the SRA up to 12-2023. Logan Search transforms any DNA query to its $k$-mers (with $k=31$), and it retrieves every dataset containing these $k$-mers. It’s the only service working at this scale. The output datasets are visualized with custom plots in Logan Search, which accesses a harmonized set of query and SRA meta-data including sequencing technology, type of molecule, geographic distribution, and sample origins. Fundamentally, Logan Search returns a list of SRA accessions. To bring closer to the data we also propose a microservice to instantly retrieve contigs matching the search, also visualisable thanks to a blast-like alignment.

8.1.10 k-mer matrix compression

Participants: Alix Regnier, Pierre Peterlongo.

In the work presented at seqBIM2024, we propose a block compression method on top of the kmtricks matrices 21, when used by kmindex for indexing and query purposes. The main objective is to achieve sensible compression ratios with a limited impact on the index creation time and on the query time. This method enables partial and targeted decompression, thus optimizing data processing. To improve the compressibility of matrices, we also exploit an idea inspired by phylogenetic compression 52. Hence, the impact of reordering matrix columns based on phylogenetic order is studied and used as a means of increasing their compressibility. Preliminary results show that this reordering significantly improves matrix compressibility. Finally, the approach we propose reduces the storage space required, with a limited impact on query time, making k-mer matrices more accessible and usable.

8.1.11 Towards space-efficient data structures for large genome-distance matrices with quick retrieval

Participants: Léo Ackermann, Karel Břinda, Pierre Peterlongo.

Many standard bioinformatics analyses lean on pairwise distances between genomes. As a result, the scalability of multiple downstream analyses relies on the efficient computation and storage of pairwise distance matrices. However, while the efficient computation of distances has been addressed by modern sketching-based methods such as Mash 56 and successors, the storage and indexing of the resulting matrices remain a significant challenge. In fact, due to their quadratic size in the number of genomes, these matrices already surpass most storage capacities and are thus heavily truncated when stored. This calls for a dedicated data structure that would be space-efficient and support near-constant-time distance retrieval queries. In the work 31 presented during seqBIM2024, we discussed ongoing work on subquadratic compression of distance matrices of large bacterial genome collections. This approach takes advantage of the peculiar structure of those collections, that can extensively be explained by their underlying phylogeny. We showed that theoretical collections of genomes model can be stored in linear space supporting constant-time queries. We then demonstrated our preliminary results on the tradeoffs that exist between metric distortion and storing cost in practical use cases. Overall, this work draws a path towards practical data structures that would be applicable to collections of millions of genomes with only negligible distance data distortion.

8.2.1 Haplotype assembly from long and noisy reads

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

Long-read assemblers face challenges in discerning closely related viral or bacterial strains, often collapsing similar strains into a single sequence. This limitation has been hampering metagenome analysis, as diverse strains may harbor crucial functional distinctions. We introduce a novel software, HairSplitter, designed to retrieve strains from a partially or totally collapsed assembly and long reads. The method uses a custom variant-calling process to operate with erroneous long reads and introduces a new read binning algorithm to recover an a priori unknown number of strains. On noisy long reads, HairSplitter recovers more strains while being faster than state-of-the-art tools, both in the cases of viruses and bacteria 1742

In 41 we innovate by approaching strain separation as an Integer Linear Programming (ILP) problem. 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 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.

8.2.2 Scaffolding step in genome assembly

Participants: Rumen Andonov, Victor Epain, 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 distance 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 distance 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 16.

8.2.3 Assessing assembly quality in metagenomes of increasing complexity sequenced with HiFi long reads

Participants: Claire Lemaitre, Nicolas Maurice, Riccardo Vicedomini.

Evaluating the quality of metagenome assemblies can be a challenging task, especially when no reference genome is available and when comparing samples at various taxonomic complexity and sequencing depth. A high quality assembly is expected not only to produce high quality bins, but also to be representative of most of the read sequences, especially in complex samples where algorithms struggle reconstructing low-abundance genomes. Although recent studies showed a great improvement in number and quality of bins obtained with novel highly accurate long reads (HiFi), it remains to be assessed how much of the sample these bins represent, especially in highly complex environmental samples.

We designed and implemented Mapler 53, 37, a metagenomic assembly and evaluation pipeline with a primary focus on evaluating the quality of HiFi-based metagenome assemblies. It incorporates state-of-the-art tools for assembly, binning, and assembly evaluation. In addition to classifying assembly bins in classical quality categories according to their marker gene content and taxonomic assignment, Mapler analyzes the alignment of reads on contigs. To do so, it calculates the ratio of mapped reads and bases, and separately analyzes mapped and unmapped reads via their k-mer frequency, read quality, and taxonomic assignment. We compared three metagenomic datasets of increasing complexity, all sequenced using PacBio HiFi technology: a mock community of 21 populations, a human gut microbiome, and a soil microbiome. At equal sequencing depth, for low and medium complexity samples (mock community and human gut, respectively), more than 90% of read sequences are found in bins regardless of the assembler used. In the soil sample, however, not only does the proportion of high quality bins drastically drop, but 54% to 88% of sequenced bases also fail to map to the assembly.

We show that considering multiple metrics is important for accurately assessing assembly quality, as relying on a single metric can be misleading. In particular, taking into account both bin quality and read alignment is indispensable, especially in complex environments. Mapler is a pipeline that calculates these metrics with minimal user input, producing both textual and graphical outputs, making it well-suited for evaluating assembly methodologies across a wide range of sample complexities. Mapler is open source and publicly available.

8.2.4 Construction of unitig matrices with abundance information

Participants: Riccardo Vicedomini.

Unitigs are biological sequences that compactly and exhaustively represent sequencing data or assembled genomes. They are constructed from k-mers but, unlike k-mers, they avoid the redundancy problem of multiple overlapping sequences covering the same genomic locus. They have proven useful for analyzing genomic diversity across several sequencing datasets. A unitig matrix is a data structure representing sequence content across multiple experiments by recording a numerical value for each unitig across all samples. Unitig matrices extend the concept of k-mer matrices, which are gaining popularity for sequence-phenotype association studies, by merging overlapping k-mers that unambiguously belong to the same sequence. Moreover, they preserve variations between samples while reducing disk space and reducing the number of rows in comparison to k-mer matrices. We addressed the limitations of current software by developing MUSET, a method that integrates efficient k-mer counting and unitig extraction in order to generate unitig matrices containing abundance values across the input samples. MUSET overcomes the limitation of state-of-the-art methods which could only output presence-absence unitig matrices. We evaluated MUSET's performance using datasets derived from a 618-GB collection of ancient oral sequencing samples, producing a filtered unitig matrix that records abundances in less than 10 hours and 20 GB memory. MUSET is expected to facilitate the extraction of biologically significant sequences, making it a valuable contribution to downstream sequencing data analyses such as genome-wide (or metagenome-wide) association studies. MUSET has been presented at seqBIM2024 and Genome Informatics. A manuscript has been provisionally accepted in the Oxford Bioinformatics journal. A pre-print version has been made available in 49.

8.2.5 Pairwise graph edit distance characterizes the impact of the construction method on pangenome graphs

Participants: Siegfried Dubois, Claire Lemaitre.

Pangenome variation graphs are an increasingly used tool to perform genome analysis, aiming to replace a linear reference in a wide variety of genomic analyses. 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. The question that arises is to what extent the construction method influences the resulting graph, and the characterization of variability. We aim to characterize the differences between variation graphs derived from the same set of genomes with a metric which expresses and pinpoint differences. We designed a pairwise variation graph comparison algorithm, which establishes an edit distance between variation graphs, threading the genomes through both graphs. We applied our method to pangenome graphs built from yeast and human chromosome collections, and demonstrated that our method effectively characterizes discordances between pangenome graph construction methods and scales to real datasets 34, 46.

pancat compare is published as free Rust software under the AGPL3.0 open source license. Source code and documentation are publicly available.

8.2.6 Pangenome graph manipulation for local visualisation with pancat

Participants: Siegfried Dubois, Claire Lemaitre.

A pangenome graph is a data structure designed to represent variations among a collection of genomes, often from the same species. Pangenome graphs are enormous, with potentially billions of nodes and intricate connectivity patterns. Visualizing these data structures is a significant challenge, and needs to be addressed to facilitate pangenome graph analysis. Multiple visualization tools are currently available, each adopting different approaches: for example, some take paths into account, others focus on global topology, and some are based upon the representation of the embedded sequences. However, in many applications displaying the whole graph structure is not required and can be confusing. In this work, we propose several approaches to visualize local regions of a graph, along with a visualization tool. We introduce algorithms to aid in the local representation of pangenomes, compatible with state-of-the-art GFA visualizers. Our methods include subgraph extraction from positions in a genome, compression of small polymorphisms without loss inside a graph, and abstraction of parts of a graph to reduce the number of elements to render. Additionally, we propose a method allowing the simultaneous visualization of two pangenome graphs built from the same genomes. We tested these methods on real data, highlighting hotspots of differences between graphs made with different tools 35. Implementations are part of a Python tool, pancat, available as a open-source project.

8.2.7 Understanding the limits of pangenome graphs for the analysis of large inversions

Participants: Fabrice Legeai, Claire Lemaitre, Sandra Romain.

Among structural variants, inversions are of particular interest in ecology studies as they can introduce phenotypic diversity and have a lasting impact on population genetics by locally impairing recombination. Pangenome graphs (PG) are a way to represent all scales of genomic diversity in a species. Several tools have been developed to construct PG from genome alignments and to detect variants from the graph topology. PG were shown to be particularly efficient for identifying and genotyping deletions and insertions in model organisms. However, they have not yet been thoroughly assessed on inversion polymorphism. We propose here to evaluate the ability of PG tools to represent and detect inversions in the case of a complex of butterfly species (Coenonympha genus) for which we already detected a dozen of large ($>$ 100 kb) inversions. To compare the tools, we selected a chromosome with 2 large, 6 medium sized ($>$ 1 kb), and 9 small ($<$ 1 kb) inversions, and built PG with 4 state of the art tools. Minigraph and minigraph-Cactus failed to build an accurate PG for such a degree of sequence divergence, while PGGB and Cactus found at most two inversions. In order to understand how PG handle inversions, we simulated the 17 known inversions in several synthetic chromosomes with increasing levels of single nucleotide divergence. We found that in such simplified graphs, most large simulated inversions are well represented with most tools. However, for smaller inversions and when the sequence divergence is higher, there is a significant variability in how the inversions are represented between PG tools. We analyzed the various obtained topological motifs, leading to methodological avenues for improving the detection of inversions in PG 39.

8.3.1 Design of long DNA molecules

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

Generating long double-stranded DNA molecules with predefined sequences without a DNA template is particularly challenging. The DNA synthesis step often poses a bottleneck for various applications, including DNA-based data storage. Here, we present a fully in vitro protocol for synthesizing very long double-stranded DNA molecules, starting from commercially available short DNA blocks and completing the process in under three days using Golden Gate assembly. This novel approach enabled the efficient production of a 24 kb-long DNA molecule encoding a portion of the Declaration of the Rights of Man and of the Citizen from 1789. The resulting DNA molecule can be seamlessly cloned into an appropriate host vector system for amplification and selection 19

8.3.2 Automation of biotechnology protocols

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

Biotechnology experiments are often lengthy and labor-intensive. When performed manually by humans, they are prone to errors, and the extended duration of such experiments hinders scalability. To address these challenges, we have developed an automated platform, called DNAmaker, that streamlines the process. The system features a pipetting robot, a robotic arm that moves along a sliding rail, and a refrigerated storage area. All components are controlled by a Raspberry Pi system and can be programmed using Python language, offering both precision and flexibility.

Video of the DNAmaker platform

8.3.3 Random access with optimized primers

Participants: Dominique Lavenier.

Accessing specific DNA molecules from a large pool is an extremely complex task. This process relies on primers - short DNA sequences that serve as unique identifiers. Efficient random access requires high-quality primers, which are limited by the structural constraints of DNA. We have developed a method for designing primers that meet stringent biochemical standards, avoiding sequences likely to form undesirable secondary structures. The proposed tool relies on computer models to predict primer binding affinity and specificity, enabling users to adjust parameters according to laboratory protocols. This approach improves data retrieval efficiency and optimizes experimental workflows 36

8.3.4 DNA caching

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

The concept of DNA caching aims to improve the latency of DNA-based storage, addressing one of its primary limitations. Latency is the main drawback of DNA storage, but by drawing inspiration from caching techniques in computer science, it is possible to significantly accelerate access times. Not all files are accessed equally—some are requested far more frequently than others. Prioritizing the reduction of latency for these high-demand files is therefore essential. This approach is particularly feasible with nanopore sequencing: by storing frequently accessed files at higher concentrations within the DNA database, their retrieval times can be shortened, as nanopore sequencing operates in real time.

8.4.1 Sorting

Many genomic algorithms rely on k-mer counting and, more broadly, on sorting algorithms. In this work, we explored the parallelization of several sorting algorithms, including Quick Sort, Heap Sort, and Radix Sort, on the UPMEM Processing-in-Memory (PiM) architecture. Our findings highlight the potential benefits and limitations of sorting on PiM. UPMEM PiM can significantly enhance applications that are bound by speculative execution, making it well-suited for integrating sorting tasks into larger, more complex DPU-based applications without incurring performance losses. However, PiM sorting is not ideal as a standalone solution for accelerating sorting tasks.

8.4.2 Alignment of long reads

Sequence alignment is a fundamental task in genomic analysis, often constrained by the memory-wall in processor-centric architectures. We have developed a PiM-optimized version of the Needleman-Wunsch (N&W) algorithm tailored for long-read alignment, leveraging PiM devices developed by UPMEM. On the UPMEM server, This N&W implementation achieves performance improvements of an order of magnitude compared to traditional multicore server-based alignment tools 29.

8.4.3 Genome compression

As Next-Generation Sequencing (NGS) technologies continue to improve in accuracy and become widely integrated into healthcare infrastructures, it is crucial to develop efficient reference-based compressors. We have developed a fast, parallel mapper for compressing short reads, enabling lossless reference-based compression of NGS datasets such as Illumina reads. The mapper employs a non-exhaustive mapping approach against a reference genome to accelerate this step. To further enhance speed and reduce the number of sequence comparisons, the mapper integrates a Bloom filter-based dispatcher, which predicts the genome regions most likely to match each read. Using real whole human sequencing datasets, we demonstrate that this approach achieves a speed-up of 1.2x to 2.7x compared to Genozip, the current leading state-of-the-art compressor, while maintaining a comparable compression ratio and reducing overall energy consumption 30

8.5.1 Unlocking the Soil Microbiome: Unraveling Soil Microbial Complexity Using Long-Read Metagenomics.

Participants: Claire Lemaitre, Nicolas Maurice, Riccardo Vicedomini.

The soil microbiome remains poorly understood, but unraveling its genetic diversity is essential, given the pivotal functions primarily mediated through their protein arsenal. Although short-read (SR) shotgun metagenomics provided interesting insights into microbiome gene diversity, it fell short in delivering comprehensive microbial genome reconstructions. Metagenome-assembled genomes (MAGs) obtained from SR often yield fragmented assemblies and incomplete gene sets (over 90% of contigs smaller than 1kb and thus unusable for gene prediction). Using PacBio HiFi sequencing, we obtained long-reads (N50: 6kb) from tunnel culture soil metagenomes, surpassing the contig length of publicly available short-read metagenomes (N50: 1kb). We benchmarked three long-read assemblers for HiFi reads, including the recent MetaMDBG software and compared the results. Even if a substantial part of the reads remain unassembled, we succeeded in reconstructing dozens of (MAGs), which further enhanced reads contiguity encompassing bacterial, archaeal, and viral genomes from terrestrial environments. HiFi LR sequencing exhibits significant potential in elucidating the complexity of bacterial genome reconstruction. However, several critical considerations remain to be addressed such as the comprehensive scope of biodiversity captured by the LR approach compared to deep sequencing with SR 51, 50.

8.5.2 Chromosome-Level Assembly and Annotation of the Pearly Heath Coenonympha arcania Butterfly Genome

Participants: Fabrice Legeai, Claire Lemaitre, Sandra Romain.

We present the first chromosome-level genome assembly and annotation of a representative of the Coenonympha genus, the pearly heath Coenonympha arcania 20. It is a relatively common and locally abundant species widely distributed in semi-open dry grasslands in Europe. Its genome was generated with a long-read PacBio HiFi sequencing approach, complemented with Hi-C scaffolding. We performed additional analyses on gene and repeat contents in comparison with 2 other high-quality Satyrinae genomes (Maniola jurtina and Pararge aegeria) and another Coenonympha species, the chestnut heath C. glycerion, all available from the Darwin Tree of Life project. Our genome comparative analysis revealed a high degree of chromosomal synteny between these genomes, suggesting very few large-scale chromosomal rearrangements between these 4 Satyrinae taxa.

This reference genome will enable future population genomics studies with Coenonympha butterflies, a species-rich genus that encompasses some of the most highly endangered butterfly taxa in Europe

8.5.3 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, and participated in the genome assembly and analyses of some major agricultural pests or their natural enemies. In particular, we analyzed the complete genome of the endosymbiotic bacteria Spiroplasma ixodetis that induces pea aphid (Acyrthosiphon pisum) male hosts killing during development. We performed comparative genomics analyses of Spiroplasma strains from A. pisum biotypes adapted to different host plants in order to reveal their phylogenetic associations and the diversity of putative virulence factors 12. We also helped to process transcriptomics data in order to reveal four target genes that responded differently to photoperiod in two pea aphid lineages and putatively involved in the transitions from sexual to asexual reproduction 18. We generated a chromosome-scale genome assembly for Hyposoter didymator that harbors a domesticated endogenous ichnovirus (HdIV), and showed that the virion is processed from 67 loci that are specifically amplified in the calyx cells during the wasp pupal stage. We also showed that this amplification required an ancestral virus gene 23. We also produced the annotations of protein coding genes of a few Lepidoptera species and inspected the nuclear colinearity (synteny) of their genomes 25. We characterized the genetic variations among a large population of virulent and avirulent strains of the oomycete Plasmopara viticola, which is responsible for the grapevine downy mildew. This work helped to identify a locus associated with resistant-breaking phenotypes 24. We helped to identify important genes of oilseed rape (Brassica napus) that are related to drought stress during the seed maturity with regards to its infection by the telluric pathogen Plasmodiophora brassicae14. Finally, in strong collaboration with the Dyliss team, we developed and tested a data model able to store and query a large panel of data from genomics, transcriptomics or epigenomics experiments 26.

8.5.4 Rapid diagnostics of antibiotic resistance

Participants: Karel Břinda.

Timely diagnostic tools are needed to improve antibiotic treatment. Pairing metagenomic sequencing with genomic neighbor typing algorithms may support rapid clinically actionable results. We created resistance-associated sequence elements (RASE) databases for Escherichia coli and Klebsiella spp. and used them to predict antibiotic susceptibility in directly sequenced (Oxford Nanopore) urine specimens from critically ill patients. RASE analysis was performed on pathogen-specific reads from metagenomic sequencing. We evaluated the ability to predict (i) multi-locus sequence type (MLST) and (ii) susceptibility profiles. We used neighbor typing to predict MLST and susceptibility phenotype of E. coli (64/80) and Klebsiella spp. (16/80) from urine samples. When optimized by lineage score, MLST predictions were concordant for 73% of samples. Similarly, a RASE-susceptible prediction for a given isolate was associated with a specificity and a positive likelihood ratio (LR+) for susceptibility of 0.65 (95% CI, 0.54–0.76) and 2.26 (95% CI, 1.75–2.92), respectively, with an increase in the probability of susceptibility of 10%. A RASE-non-susceptible prediction was associated with a sensitivity and a negative likelihood ratio (LR-) for susceptibility of 0.79 (95% CI, 0.74–0.84) and 0.32 (95% CI, 0.24–0.43) respectively, with a decrease in the probability of susceptibility of 20%. Numerous antibiotic classes could reasonably be reconsidered empiric therapy by shifting empiric probabilities of susceptibility across relevant treatment thresholds. Moreover, these predictions can be available within 6 h. Metagenomic sequencing of urine specimens with neighbor typing provides rapid and informative predictions of lineage and antibiotic susceptibility with the potential to impact clinical decision-making 15.

10.1.1 Visits of international scientists

10.2.1 H2020 projects

10.3.1 PEPR

10.3.2 ANR

10.3.3 Inria Exploratory Action

Labex CominLabs: dnarXiv project

Participants: Guillermo Barroso, Olivier Boullé, Dominique Lavenier, Julien Leblanc, Jacques Nicolas.

• Coordinator: Dominique Lavenier
• Duration: 60 months (2021-2026)
• Description: The dnarXiv project aims at exploring data storage on DNA molecules. This kind of storage has the potential to become a major archive solution in the mid-to long term. In this project, two key promising biotechnologies are considered: enzymatic DNA synthesis and DNA nanopore sequencing. The objective is to propose advanced solutions in terms of coding schemes (i.e., source and channel coding) and data security (i.e., data confidentiality/integrity and DNA storage authenticity), that consider the constraints and advantages of the chemical processes and biotechnologies involved in DNA storage.

Rennes Metropole: Allocation d'installation scientifique

Participants: Karel Břinda.

• Coordinator: Karel Břinda
• Duration: 20 months (2024-2026)
• Description: Fast DNA sequence data search is crucial for our ability to control the spread of infectious diseases. However, it represents a significant challenge in data science: exponentially growing sequencing data exceeds the development of computing capabilities. The central goal of this project is to develop sublinear algorithms for searching within collections of bacterial genomes, with the ultimate aim of enabling searches on all sequenced bacteria on standard desktop and laptop computers. This will be achieved through advancements in phylogenetic compression and entropy scale algorithms.

Défi scientifique RECHERCHE TRANSDISCIPLINAIRE INTERPÔLES, Université de Rennes

Participants: Emeline Roux.

• Coordinator: Emeline Roux
• Duration: 24 months (January 2023 - December 2024)

• Partners: Inria teams: Dyliss.

  External partner is Institut NuMeCan.

• Description: The role of the microbiota, particularly the intestinal microbiota, in the physiology and pathophysiology of numerous diseases has been recognized for over 15 years now. The trans-disciplinary research project initiated the development of a pipeline for the analysis of long-read sequencing data from intestinal microbiota. The aim is to determine the precise meta-genome (at strain level) and consequently predict the meta-metabolome of the intestinal microbiota. The method developed will provide a better understanding of food-microbiota-host interactions, with a view to proposing innovative solutions in the future for restoring an intestinal microbiota with complete metabolic functions.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• D. Lavenier, "DNA Storage", CORESA 2024, COmpresseion et REpresentation des Signaux Audiovisuels, Rennes, Nov. 2024
• D. Lavenier, "Information Storage on DNA", Journée scientifique du L2TI, Paris, Dec. 2024
• P. Peterlongo, "Index the planet: index SRA unitigs with kmindex", Genome Informatics, Hixton UK, Nov. 2024
• P. Peterlongo, "Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets", Labri Seminar, Bordeaux, May 2024

11.1.5 Leadership within the scientific community

• Members of the Scientific Advisory Board of the GDR BIMMM (National Research Group in Molecular Bioinformatics) [P. Peterlongo, C. Lemaitre]
• Animator of the Sequence Algorithms axis (seqBIM GT) of the GDRs BIMMMM and IFM (National Research Groups in Molecular Bioinformatics and Informatics and Mathematics respectively) (350 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]

11.1.6 Scientific expertise

• Expert at DAEI (Pole expertise international de la Délégation au Affaire Européennes et Internationales), MESR [D. Lavenier]

11.1.7 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]
• 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]

11.2.1 Teaching administration

• Head of the master's degree "Nutrition Sciences des Aliments" (NSA) of University of Rennes (75 students) [E. Roux]

11.2.2 Teaching

• Licence: R. Andonov, Linear Programming, 30h, L3 Miage, Univ. Rennes, France.
• Licence : E. Roux, Biochemistry, 90h, L1 and L3, Univ. Rennes, France.
• Licence: N. Maurice, Object Oriented Programming, 38h, L2, INSA Rennes, France.
• Licence: S. Dubois, Introduction to Java, 24h, L1 Informatics, Univ. Rennes, France.
• Master: S. Dubois, Object-Oriented Programming, 32h, M2 Bioinformatics, Univ. Rennes, France.
• Master: J. Nicolas, Logic, ASP and CSP, 36h, M1 Artificial Intelligence, Univ. Rennes, France.
• Master: C. Lemaitre, P. Peterlongo, V. Levallois, Sequence Bioinformatics, 42h, M1 Informatics, Univ. Rennes, France.
• Master: C. Lemaitre, P. Peterlongo, Algorithms on Sequences, 52h, M2 Bioinformatics, Univ. Rennes, France.
• Master : R. Vicedomini, K. Břinda, Experimental Bioinformatics, 21h, M1, ENS Rennes, France.
• Master : E. Roux, biochemistry and microbiology, 130h, M1 and M2, Univ. Rennes, France.

11.2.3 HDR defense

• HDR: E. Roux, Food and Functionalities 45, defended:29/11/2024.

11.2.4 Supervision

• PhD: S. Romain, Identification, genotyping and representation of structural variants in pangenomes 44, Inria (ANR Divalps), defended: 08/07/2024, C. Lemaitre, F. Legeai.
• PhD: K. Hannoush, Dynamic pan-genome graphs 43, Inria (ITN Alpaca), defended: 13/12/20204, P. Peterlongo, C. Marchet.
• PhD: R. Faure, Haplotype assembly from long reads 42, Univ Rennes, defended: 27/11/2024, D. Lavenier, J-F. Flot.
• PhD: C. Duitama (Sequence Bioinformatics group, Institut Pasteur, Paris), Algorithms based on k-mers for ancient oral metagenomics : Tools for contamination removal and assessment in palaeometagenomics [duitamagonzalez:tel-04560480v2], defended: 30/01/2024, R. Chikhi, H. Richard, R. Vicedomini.
• PhD in progress: M. Mognol, Processing-in-Memory, CIFRE, since 01/04/2022, D. Lavenier.
• PhD in progress: S. Dubois, Characterizing structural variation in pangenome graphs, Inria (PEPR-ANR Agrodiv), 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, Inria (PEPR-ANR Mistic), since 01/10/2023, C. Lemaitre, C. Frioux, R. Vicedomini.
• PhD in progress: V. Levallois, Indexing genomic data, Défi Inria OmicFinder, since 01/10/2023, P. Peterlongo.
• PhD in progress: Y. Tirlet (IRISA Dyliss team), Univ Rennes (ANR), Integrative method for multi-omics data analysis with application to the activation and regulation of an endogeneized viral genome in a parasitoid wasp, since 01/05/2023, E. Becker, O. Dameron, F. Legeai.
• PhD in progress: A. Regnier, Limiting the size of data structures indexing genomic sequences, Inria (PEPR-ANR Agrodiv), since 01/10/2024, P. Peterlongo.
• PhD in progress: L. Ackermann, Developing efficient algorithms for sublinear search in large genome databases, Inria, since 01/10/2024, K. Břinda, P. Peterlongo.
• PhD in progress: T. Truong, Computational methods for phylogenetic compression, Univ Rennes, since 01/11/2024, K. Břinda, P. Peterlongo, D. Lavenier.
• PhD in progress: L. Angevin, Fine characterization of the intestinal microbiota and prediction of metabolite production, Univ Rennes, since 01/10/2024, E. Roux, R. Vicedomini, P. Peterlongo.
• PhD in progress: M. Temperville, Methods for characterizing structural variations in genomes with linked-read data, Univ Rennes, since since 01/10/2024, F. Legeai, C. Lemaitre, C. Mérot.

11.2.5 Juries

• Reviewer of HDR thesis
  • Sylvain Foissac, Univ. Toulouse, June 2024 [C. Lemaitre]
• Member of HDR thesis jury
  • Loïs Maignien, Univ. Brest, Dec. 2024 [C. Lemaitre]
• President of PhD thesis jury
  • C. Duitama, Sorbonne University, Jan 2024 [P. Peterlongo]
  • S. Romain, Univ. Rennes, Nov 2024 [P. Peterlongo]
  • K. Hannoush, Univ. Rennes, Dec 2024 [C. Lemaitre]
  • X. Pic, Univ. Nice, Sep 2024 [D. Lavenier]
• Reviewer of PhD thesis
  • C. Duitama, Sorbonne University, Jan 2024 [P. Peterlongo]
  • M. Leinonen, Univ. Helsinki (Finland), Feb 2024 [R. Vicedomini]
  • D. Martincigh, Univ. Udine (Italy), Sep 2024 [R. Vicedomini]
• Member of PhD thesis jury
  • Berton, IMT-A, Univ. Brest [D. Lavenier]
  • D. Martincigh, Univ. Udine (Italy), Sep 2024 [R. Vicedomini]
• Member of PhD thesis committee
  • T. Baudeau, Univ. Lille [C. Lemaitre]
  • Léa Nicolas, Univ Rennes [C. Lemaitre]
  • Dimple Adiwal, Univ Rennes [C. Lemaitre]
  • Jules Burgat, Univ Rennes [C. Lemaitre]
  • Florent Couturier, Univ Bordeaux [C. Lemaitre]
  • L. Vandame, Univ. Lille [P. Peterlongo]
  • O. Weppe, Univ. Rennes [P. Peterlongo]
  • A. Dequay, Univ. Rennes [D. Lavenier]
  • A. Ezzeddine, IMT-A, Univ. Brest [D. Lavenier]
  • H. Gasnier, IMT-A, Univ. Brest [D. Lavenier]
  • L. de la Fuenté, Univ. Rennes [D. Lavenier]
  • R. Jaafar, Univ. Rennes [R. Vicedomini]

11.3.1 Specific official responsibilities in science outreach structures

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

11.3.2 Productions (articles, videos, podcasts, serious games, ...)

• The dnarXiv project: Video [D. Lavenier]
• Article Interstices 54: Indexer les données génomiques : un défi de taille à relever. [P. Peterlongo]
• Article Interstices 55: Indexer des milliards d'éléments avec les filtres de Bloom. [P. Peterlongo]

11.3.3 Participation in Live events

• Chiche! Interventions in high school classes to make high school students aware of research careers in the digital sector. Two interventions made in 2024. [P. Peterlongo]
• Maison Pour La Science: Training for school teachers on sport, nutrition and health. Two interventions made in 2024. [E. Roux]
• Les Champs Libres, Espace des Sciences: Sugar, between pleasure and harm. One intervention made in 2024. [E. Roux]
• Programming sessions LCLC for Middle school female students [L. Ackermann]

International journals

• 12 articleH.Hiroshi Arai, F.Fabrice Legeai, D.Daisuke Kageyama, A.Akiko Sugio and J.-C.Jean-Christophe Simon. Genomic insights into Spiroplasma endosymbionts that induce male-killing and protective phenotypes in the pea aphid.FEMS Microbiology Letters371January 2024HALDOIback to text
• 13 articleA.Anthony Baire, P.Pierre Marijon, F.Francesco Andreace and P.Pierre Peterlongo. Back to sequences: Find the origin of k-mers.Journal of Open Source Software9101September 2024, 7066HALDOIback to text
• 14 articleG.Grégoire Bianchetti, V.Vanessa Clouet, F.Fabrice Legeai, C.Cécile Baron, K.Kévin Gazengel, B.Benoît Ly Vu, S.Sébastien Baud, A.Alexandra To, M. J.Maria J Manzanares-Dauleux, J.Julia Buitink and N.Nathalie Nesi. Identification of transcriptional modules linked to the drought response of Brassica napus during seed development and their mitigation by early biotic stress.Physiologia Plantarum17612024, e14130HALDOIback to text
• 15 articleA. C.Amanda C Carroll, L.Leanne Mortimer, H.Hiren Ghosh, S.Sandra Reuter, H.Hajo Grundmann, K.Karel Brinda, W. P.William P Hanage, A.Angel Li, A.Aimee Paterson, A.Andrew Purssell, A.Ashley Rooney, N. R.Noelle R Yee, B.Bryan Coburn, S.Shola Able-Thomas, M.Martin Antonio, A.Allison Mcgeer and D. R.Derek R Macfadden. Rapid inference of antibiotic susceptibility phenotype of uropathogens using metagenomic sequencing with neighbor typing.Microbiology SpectrumNovember 2024, 1-16HALDOIback to text
• 16 articleV.Victor Epain and R.Rumen Andonov. Global exact optimisations for chloroplast structural haplotype scaffolding.Algorithms for Molecular Biology195February 2024, 1-35HALDOIback to text
• 17 articleR.Roland Faure, D.Dominique Lavenier and J.-F.Jean-François Flot. HairSplitter: haplotype assembly from long, noisy reads.Peer Community In Mathematical and Computational BiologyFebruary 2024, 1-16HALDOIback to text
• 18 articleM. D.M D Huguet, S.Stéphanie Robin, S.Sylvie Hudaverdian, S.Sylvie Tanguy, N.Nathalie Prunier-Leterme, R.Romuald Cloteau, S.Sylvain Baulande, P.Patricia Legoix-Né, F.Fabrice Legeai, J.-C.Jean-Christophe Simon, J.Julie Jaquiéry, D.Denis Tagu and G.Gaël Le Trionnaire. Transcriptomic basis of sex loss in the pea aphid.BMC Genomics251February 2024, 202HALDOIback to text
• 19 articleJ.Julien Leblanc, O.Olivier Boulle, E.Emeline Roux, J.Jacques Nicolas, D.Dominique Lavenier and Y.Yann Audic. Fully in vitro iterative construction of a 24 kb-long artificial DNA sequence to store digital information.Biotechniques7652024, 203-215HALDOIback to text
• 20 articleF.Fabrice Legeai, S.Sandra Romain, T.Thibaut Capblancq, P.Paul Doniol-Valcroze, M.Mathieu Joron, C.Claire Lemaitre and L.Laurence Després. Chromosome-Level Assembly and Annotation of the Pearly Heath Coenonympha arcania Butterfly Genome.Genome Biology and Evolution163March 2024, evae055HALDOIback to text
• 21 articleT.Téo Lemane, N.Nolan Lezzoche, J.Julien Lecubin, E.Eric Pelletier, M.Magali Lescot, R.Rayan Chikhi and P.Pierre Peterlongo. Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets with kmindex and ORA.Nature Computational Science42February 2024, 104-109HALDOIback to textback to textback to text
• 22 articleV.Victor Levallois, F.Francesco Andreace, B.Bertrand Le Gal, Y.Yoann Dufresne and P.Pierre Peterlongo. The Backpack Quotient Filter: a dynamic and space-efficient data structure for querying k -mers with abundance.iScienceFebruary 2024, 111435HALDOIback to text
• 23 articleA.Ange Lorenzi, F.Fabrice Legeai, V.Véronique Jouan, P.-A.Pierre-Alain Girard, M. R.Michael R Strand, M.Marc Ravallec, M.Magali Eychenne, A.Anthony Bretaudeau, S.Stéphanie Robin, J.Jeanne Rochefort, M.Mathilde Villegas, G. R.Gaelen R Burke, R.Rita Rebollo, N.Nicolas Nègre and A.-N.Anne-Nathalie Volkoff. Identification of a viral gene essential for the genome replication of a domesticated endogenous virus in ichneumonid parasitoid wasps.PLoS Pathogens204April 2024, e1011980HALDOIback to text
• 24 articleM.Manon Paineau, A.Andrea Minio, P.Pere Mestre, F.Frédéric Fabre, I. D.Isabelle D Mazet, C.Carole Couture, F.Fabrice Legeai, T.Thomas Dumartinet, D.Dario Cantu and F.François Delmotte. Multiple deletions of candidate effector genes lead to the breakdown of partial grapevine resistance to downy mildew.New Phytologist2434June 2024, 1490-1505HALDOIback to text
• 25 articleC.Charles Perrier, R.Rémi Allio, F.Fabrice Legeai, M.Mathieu Gautier, F.Frédéric Bénéluz, W.William Marande, A.Anthony Théron, N.Nathalie Rodde, M.Melfran Herrera, L.Laure Saune, H.Hugues Parrinello, M.Mélanie Mcclure and M.Mónica Arias. Transposable element accumulation drives genome size increase in Hylesia metabus (Lepidoptera: Saturniidae), an urticating moth species from South America.Journal of Heredity2025, esae069In press. HALDOIback to text
• 26 articleY.Yael Tirlet, M.Matéo Boudet, E.Emmanuelle Becker, F.Fabrice Legeai and O.Olivier Dameron. Generic and queryable data integration schema for transcriptomics and epigenomics studies.Computational and Structural Biotechnology Journal23December 2024, 4232-4241HALDOIback to text

International peer-reviewed conferences

• 27 inproceedingsK.Khodor Hannoush, C.Camille Marchet and P.Pierre Peterlongo. Cdbgtricks: Strategies to update a compacted de Bruijn graph.Prague Stringology Conference 2024PSC 2024 - Prague Stringology ConferencePrague (CZ), Czech RepublicAugust 2024, 1-13HALDOIback to text
• 28 inproceedingsK.Khodor Hannoush, C.Camille Marchet and P.Pierre Peterlongo. Strategies to update a compacted de Bruijn graph.SeqBIM 2024SeqBIM 2024 - Journées annuelles du groupe de travail SeqBIM (Séquences en Bioinformatique, Informatique et Mathématiques)Rennes, FranceNovember 2024, 1-2HALback to text
• 29 inproceedingsM.Meven Mognol, D.Dominique Lavenier and J.Julien Legriel. Parallelization of the Banded Needleman &amp; Wunsch Algorithm on UPMEM PiM Architecture for Long DNA Sequence Alignment.ICPP 2024 - International Conference on Parallel ProcessingGotland, SwedenACM2024, 1-10HALback to text
• 30 inproceedingsF.Florestan de Moor, M.Meven Mognol, C.Charles Deltel, E.Erwan Drezen, J.Julien Legriel and D.Dominique Lavenier. MiMyCS: A Processing-in-Memory Read Mapper for Compressing Next-Gen Sequencing Datasets.IEEEXploreBIBM 2024 - IEEE International Conference on Bioinformatics and BiomedicineBIBM20241Lisbonne, PortugalIEEEDecember 2024, 7176HALback to text

Conferences without proceedings

• 31 inproceedingsL.Léo Ackermann, P.Pierre Peterlongo and K.Karel Břinda. Towards space-efficient data structures for large genome-distance matrices with quick retrieval.SeqBim 2024 - Journées sur les Séquences en Bioinformatique, Informatique et. MathématiquesRennes, France2024HALback to text
• 32 inproceedingsF.Francesca Brunetti and K.Karel Břinda. Optimized K-mer Matching For Million-Genome Collections On Laptops.SeqBIM 2024 - Journées sur les Séquences en Bioinformatique, Informatique et. Mathématiques,Rennes, France2024, 1-2HALback to text
• 33 inproceedingsN.Nicolas Buchin, V.Victor Levallois and P.Pierre Peterlongo. Improve the resizing of the Backpack Quotient Filter.SeqBim 2024 - Journées annuelles du groupe de travail SeqBIM (Séquences en Bioinformatique, Informatique et Mathématiques)Rennes, France2024HALback to text
• 34 inproceedingsS.Siegfried Dubois, Z.Zytnicki Matthias, C.Claire Lemaitre and F.Faraut Thomas. Towards an edit distance between pangenome graphs.DSB 2024 - Workshop Data Structures in BioinformaticsMontpellier, FranceMarch 2024, 1-1HALback to text
• 35 inproceedingsS.Siegfried Dubois, M.Matthias Zytnicki, T.Thomas Faraut and C.Claire Lemaitre. Pangenome graph manipulation for local visualisation with pancat.MIGGS1 2024 - 1st symposium on Methods for Interfacing with Graphs of Genomic SequencesLille, France2024, 1-1HALback to text
• 36 inproceedingsJ.Jeremy Mateos, D.Dominique Lavenier, M.Melpomeni Dimopoulou, A.Anthony Genot and M.Marc Antonini. Primer design for DNA storage random access.CORESA 2024 - 23ème conférence sur COmpression et REprésentation des Signaux AudiovisuelsRennes, France2024, 1-3HALback to text
• 37 inproceedingsN.Nicolas Maurice, C.Clémence Frioux, C.Claire Lemaitre and R.Riccardo Vicedomini. Assessing assembly quality in metagenomes of increasing complexity sequenced with HiFi long reads.2024 - 24th Genome Informatics meetingHinxton, United KingdomNovember 2024, 1-21HALback to text
• 38 inproceedingsA.Alix Regnier and P.Pierre Peterlongo. k-mer matrix compression.SeqBIM 2024 - Journées annuelles du groupe de travail SeqBIM (Séquences en Bioinformatique, Informatique et Mathématiques)Rennes, France2024, 1-1HAL
• 39 inproceedingsS.Sandra Romain, F.Fabrice Legeai and C.Claire Lemaitre. Understanding the limits of pangenome graphs for the analysis of large inversions in a complex of butterfly species.2024 - International Environmental and Agronomical Genomics symposiumToulouse, France2024, 1-1HALback to text
• 40 inproceedingsO.Ondřej Sladký, P.Pavel Veselý and K.Karel Břinda. Masked superstrings as a compact, indexable and dynamic representation of k-mer sets.SeqBim 2024 - Journées sur les Séquences en Bioinformatique, Informatique et. MathématiquesRennes, FranceNovember 2024, 1-3HALback to textback to text
• 41 inproceedingsT. K.Tam Khac Minh Truong, R.Roland Faure and R.Rumen Andonov. Assembling close strains in metagenome assemblies using discrete optimization.BIOINFORMATICS 2024 - 15th International conference on bioinformatics models, methods and algorithmsRome, Italy2024, 1-10HALback to textback to text

Doctoral dissertations and habilitation theses

• 42 thesisR.Roland Faure. Haplotype assembly from long reads.Université de Rennes; Université libre de BruxellesNovember 2024HALback to textback to text
• 43 thesisK.Khodor Hannoush. Dynamic Pan-genome Graphs.Univ Rennes, Inria, CNRS, IRISA, FranceDecember 2024HALback to textback to text
• 44 thesisS.Sandra Romain. Identification, genotyping and representation of structural variants in pangenomes.Université de RennesNovember 2024, i270 - i278HALback to text
• 45 thesisE.Emeline Roux. Food and Functionalities.Université de RennesNovember 2024HALback to text

Reports & preprints

• 46 miscS.Siegfried Dubois, M.Matthias Zytnicki, C.Claire Lemaitre and T.Thomas Faraut. Pairwise graph edit distance characterizes the impact of the construction method on pangenome graphs.December 2024HALDOIback to text
• 47 miscO.Ondřej Sladký, P.Pavel Veselý and K.Karel Břinda. FroM Superstring to Indexing: a space-efficient index for unconstrained k-mer sets using the Masked Burrows-Wheeler Transform (MBWT).2024HALback to textback to text
• 48 miscO.Ondřej Sladký, P.Pavel Veselý and K.Karel Břinda. Towards Efficient k-Mer Set Operations via Function-Assigned Masked Superstrings.March 2024HALDOIback to text
• 49 miscR.Riccardo Vicedomini, F.Francesco Andreace, Y.Yoann Dufresne, R.Rayan Chikhi and C.Camila Duitama González. MUSET: Set of utilities for the construction of abundance unitig matrices from sequencing data.December 2024HALback to text

Other scientific publications

• 50 inproceedingsC.Carole Belliardo, N.Nicolas Maurice, C.Clémence Frioux, C.Claire Lemaitre, R.Riccardo Vicedomini, S.Samuel Mondy, A.Arthur Péré, M.Marc Bailly-Bechet and E.Etienne Danchin. Unlocking the Soil Microbiome: Unraveling Soil Microbial Complexity Using Long-Read Metagenomics.EAGS 2024 - The International Environmental and Agronomical Genomics symposiumToulouse, France2024, 1-1HALback to text
• 51 inproceedingsC.Carole Belliardo, S.Samuel Mondy, A.Arthur Pere, C.Claire Lemaitre, R.Riccardo Vicedomini, C.Clémence Frioux, D. J.David James Sherman, P.Pierre Abad, M.Marc Bailly-Bechet and E.Etienne Danchin. Exploring and quantifying the soil genetic diversity captured by long and short-read shotgun metagenomic sequencing.Journées 2024 du programme Agroécologie et NumériqueRennes, France2024, 1-1HALback to text
• 52 inproceedingsK.Karel Břinda, Z.Zamin Iqbal and M.Michael Baym. Phylogenetic compression as a core compression technique for extremely large microbial genome collections.Genome Informatics 2024Hinxton, Cambridge, United KingdomNovember 2024HALback to text
• 53 inproceedingsN.Nicolas Maurice, C.Claire Lemaitre, C.Clémence Frioux and R.Riccardo Vicedomini. Metagenomic assembly of complex ecosystems with highly accurate long-reads.Journées 2024 du PEPR Agroécologie et NumériqueRennes, France2024, 1-1HALback to text

Scientific popularization

• 54 articleT.Téo Lemane, M.Magali Lescot and P.Pierre Peterlongo. Indexer les données génomiques : un défi de taille à relever.IntersticesFebruary 2024, 1-5HALback to text
• 55 articleP.Pierre Peterlongo and L.Lucas Robidou. Indexer des milliards d’éléments avec les filtres de Bloom.IntersticesFebruary 2024, 1-6HALback to text