The backpack quotient filter: A dynamic and space-efficient data structure for querying -mers with abundance.

Genomic data sequencing is crucial for understanding biological systems. As genomic databases like the European Nucleotide Archive expand exponentially, efficient data manipulation is essential. A key challenge is querying these databases to determine the presence or absence of specific sequences and their abundance within datasets.

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

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 present kmindex, 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.

fimpera: drastic improvement of Approximate Membership Query data-structures with counts.

Motivation: High throughput sequencing technologies generate massive amounts of biological sequence datasets as costs fall. One of the current algorithmic challenges for exploiting these data on a global scale consists in providing efficient query engines on these petabyte-scale datasets. Most methods indexing those datasets rely on indexing words of fixed length k, called k-mers.

HaploBlocks: Efficient Detection of Positive Selection in Large Population Genomic Datasets.

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.

kmtricks: efficient and flexible construction of Bloom filters for large sequencing data collections.

Summary: When indexing large collections of short-read sequencing data, a common operation that has now been implemented in several tools (Sequence Bloom Trees and variants, BIGSI) is to construct a collection of Bloom filters, one per sample. Each Bloom filter is used to represent a set of -mers which approximates the desired set of all the non-erroneous -mers present in the sample. However, this approximation is imperfect, especially in the case of metagenomics data.

k mdiff, large-scale and user-friendly differential k-mer analyses.

Summary: 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. We propose a tool, kmdiff, that performs differential k-mer analyses on large sequencing cohorts in an order of magnitude less time and memory than previously possible.

Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems.

Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. Here we assessed the global structure of plankton geography and its relation to the biological, chemical, and physical context of the ocean (the 'seascape') by analyzing metagenomes of plankton communities sampled across oceans during the Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity.

The K-mer File Format: a standardized and compact disk representation of sets of k-mers.

Summary: Bioinformatics applications increasingly rely on ad hoc disk storage of k-mer sets, e.g. for de Bruijn graphs or alignment indexes.

StrainFLAIR: strain-level profiling of metagenomic samples using variation graphs.

Current studies are shifting from the use of single linear references to representation of multiple genomes organised in pangenome graphs or variation graphs. Meanwhile, in metagenomic samples, resolving strain-level abundances is a major step in microbiome studies, as associations between strain variants and phenotype are of great interest for diagnostic and therapeutic purposes. We developed StrainFLAIR with the aim of showing the feasibility of using variation graphs for indexing highly similar genomic sequences up to the strain level, and for characterizing a set of unknown sequenced genomes by querying this graph.

metaVaR: Introducing metavariant species models for reference-free metagenomic-based population genomics.

The availability of large metagenomic data offers great opportunities for the population genomic analysis of uncultured organisms, which represent a large part of the unexplored biosphere and play a key ecological role. However, the majority of these organisms lack a reference genome or transcriptome, which constitutes a technical obstacle for classical population genomic analyses. We introduce the metavariant species (MVS) model, in which a species is represented only by intra-species nucleotide polymorphism.

Investigating population-scale allelic differential expression in wild populations of (Cyclopoida, Claus, 1866).

Acclimation allowed by variation in gene or allele expression in natural populations is increasingly understood as a decisive mechanism, as much as adaptation, for species evolution. However, for small eukaryotic organisms, as species from zooplankton, classical methods face numerous challenges. Here, we propose the concept of allelic differential expression at the population-scale (psADE) to investigate the variation in allele expression in natural populations.

DiscoSnp-RAD: de novo detection of small variants for RAD-Seq population genomics.

Restriction site Associated DNA Sequencing (RAD-Seq) is a technique characterized by the sequencing of specific loci along the genome that is widely employed in the field of evolutionary biology since it allows to exploit variants (mainly Single Nucleotide Polymorphism-SNPs) information from entire populations at a reduced cost. Common RAD dedicated tools, such as or , are based on all-vs-all read alignments, which require consequent time and computing resources. We present an original method, DiscoSnp-RAD, that avoids this pitfall since variants are detected by exploiting specific parts of the assembly graph built from the reads, hence preventing all-vs-all read alignments.

SVJedi: genotyping structural variations with long reads.

Motivation: Studies on structural variants (SVs) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, the number of discovered SVs is increasing, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it is important to genotype newly sequenced individuals on well-defined and characterized SVs.

Cytosine methylation of mature microRNAs inhibits their functions and is associated with poor prognosis in glioblastoma multiforme.

Background: Literature reports that mature microRNA (miRNA) can be methylated at adenosine, guanosine and cytosine. However, the molecular mechanisms involved in cytosine methylation of miRNAs have not yet been fully elucidated. Here we investigated the biological role and underlying mechanism of cytosine methylation in miRNAs in glioblastoma multiforme (GBM).

Finding all maximal perfect haplotype blocks in linear time.

Recent large-scale community sequencing efforts allow at an unprecedented level of detail the identification of genomic regions that show signatures of natural selection. Traditional methods for identifying such regions from individuals' haplotype data, however, require excessive computing times and therefore are not applicable to current datasets. In 2019, Cunha et al.

ELECTOR: evaluator for long reads correction methods.

The error rates of third-generation sequencing data have been capped >5%, mainly containing insertions and deletions. Thereby, an increasing number of diverse long reads correction methods have been proposed. The quality of the correction has huge impacts on downstream processes.

SimkaMin: fast and resource frugal de novo comparative metagenomics.

Motivation: De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities.

Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs.

Motivation: Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large datasets or consider reads as mere suites of k-mers, without taking into account their full-length sequence information.

Results: We propose a new method to correct short reads using de Bruijn graphs and implement it as a tool called Bcool.

Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes.

Background: Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes.

Discovering millions of plankton genomic markers from the Atlantic Ocean and the Mediterranean Sea.

Comparison of the molecular diversity in all plankton populations present in geographically distant water columns may allow for a holistic view of the connectivity, isolation and adaptation of organisms in the marine environment. In this context, a large-scale detection and analysis of genomic variants directly in metagenomic data appeared as a powerful strategy for the identification of genetic structures and genes under natural selection in plankton. Here, we used discosnp++, a reference-free variant caller, to produce genetic variants from large-scale metagenomic data and assessed its accuracy on the copepod Oithona nana in terms of variant calling, allele frequency estimation and population genomic statistics by comparing it to the state-of-the-art method.

De novo clustering of long reads by gene from transcriptomics data.

Long-read sequencing currently provides sequences of several thousand base pairs. It is therefore possible to obtain complete transcripts, offering an unprecedented vision of the cellular transcriptome. However the literature lacks tools for de novo clustering of such data, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads.

A de novo approach to disentangle partner identity and function in holobiont systems.

Background: Study of meta-transcriptomic datasets involving non-model organisms represents bioinformatic challenges. The production of chimeric sequences and our inability to distinguish the taxonomic origins of the sequences produced are inherent and recurrent difficulties in de novo assembly analyses. As the study of holobiont meta-transcriptomes is affected by challenges invoked above, we propose an innovative bioinformatic approach to tackle such difficulties and tested it on marine models as a proof of concept.