Biology subjects

Salson, M.

Publications and source records attributed to Salson, M..

10 recordsLinked to original sources

bioRxiv2025

REINDEER2: practical abundance index at scale

Recent advances in biological sequence indexing have enabled the efficient querying of sequence presence across massive genomic data repositories. While presence queries have become tractable at petabyte scale, retrieving quantitative information such as sequence abundances remains a significant algorithmic challenge. Existing abundance-aware indexes are mostly static, difficult to scale, and often trade off completeness, precision, or updatability. We describe a novel discrete abundance index designed for scalability, dynamic updates, and tunable precision. We combine an inverted index with probabilistic and exact structures to support fast, memory-efficient construction and precise high-throughput queries across thousands of RNA datasets. Our experiments demonstrate that our method REINDEER2 achieves one to two orders of magnitude speedup in construction compared to existing methods, while maintaining comparable or better memory use. Despite using approximate structures for scalability, REINDEER2 achieves sub-1% error on abundance recovery and correlates strongly with reference quantifiers like Kallisto. It also supports sequence-level queries in seconds over thousands of datasets. Code and experiments github.com/Yohan-HernandezCourbevoie/REINDEER2

bioinformatics↗

bioRxiv2024

Assessing Long-Read Mappers for Viral Genomics

Long-read sequencing technologies from Pacific Biosciences and Oxford Nanopore Technologies (ONT) have advanced genomic research, producing reads over 10 kilobases and enabling rapid field-based viral surveillance. This study evaluates eight long-read mapping tools on viral genomic data, including modern and legacy methods. We assessed their performance on ONT reads and their impact on variant calling using bcftools and medaka. Using simulated and real datasets under varying conditions, reflecting different experimental and biological conditions, such as variable read lengths, error rates and the presence of multiple viral variants, we found that the majority of tools had great difficulty in correctly managing read edges. In addition, it was found that with a default setting, the performance of the tools decreased.

bioinformatics↗

bioRxiv2024

Constrained enumeration of k-mers from a collection of references with metadata

While recent developments in k-mers indexing methods have opened up many new possibilities, they still have limitations in handling certain types of queries, such as identifying k-mers present in one dataset but absent in another. In this article, we present a framework for efficiently enumerating all k-mers within a collection of references that satisfy constraints related to their metadata tags. Our method involves simplifying the query beforehand to reduce computation delays; the construction of the solution itself is carried out using CBL, a recent data structure specifically dedicated to the optimised computation of set operations on k-mer sets. We provide an implementation to our solution and we demonstrate its capabilities using real genomic data (microbial and RNA-seq), and show examples of use cases to identify k-mers of biological interest. FundingThis work is funded by a grant from the French ANR: Full-RNA ANR-22-CE45-0007. Igor Martayan is supported by a doctoral grant from ENS Rennes.

bioinformatics↗

bioRxiv2024

Exploring a large cancer cell line RNA-sequencing dataset with k-mers

Analyzing the immense diversity of RNA isoforms in large RNA-seq repositories requires laborious data processing using specialized tools. Indexing techniques based on k-mers have previously been effective at searching for RNA sequences across thousands of RNA-seq libraries but falling short of enabling direct RNA quantification. We show here that RNAs queried in the form of k-mer sets can be quantified in seconds, with a precision akin to that of conventional RNA quantification methods. We showcase several applications by exploring an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples. Non-reference RNA sequences such as RNAs harboring driver mutations and fusions, splicing isoforms or RNAs derived from repetitive elements, can be retrieved with high accuracy. Moreover, we show that k-mer indexing offers a powerful means to reveal variant RNAs induced by specific gene alterations, for instance in splicing factors. A web server allows public queries in CCLE and other indexes: https://transipedia.fr. Code is provided to allow users to set up their own server from any RNA-seq dataset.

bioinformatics↗

bioRxiv2023

Glutaredoxin regulation of primary root growth confers early drought stress tolerance in pearl millet

Seedling root traits impact plant establishment under challenging environments. Pearl millet is one of the most heat and drought tolerant cereal crops that provides a vital food source across the sub-Saharan Sahel region. Pearl millets early root system features a single fast-growing primary root which we hypothesize is an adaptation to the Sahelian climate. Using crop modelling, we demonstrate that early drought stress is an important constraint in agrosystems in the Sahel where pearl millet was domesticated. Furthermore, we show that increased pearl millet primary root growth is correlated with increased early water stress tolerance in field conditions. Genetics including GWAS and QTL approaches identify genomic regions controlling this key root trait. Combining gene expression data, re-sequencing and re-annotation of one of these genomic regions identified a glutaredoxin-encoding gene PgGRXC9 as the candidate stress resilience root growth regulator. Functional characterization of its closest Arabidopsis homolog AtROXY19 revealed a novel role for this glutaredoxin (GRX) gene clade in regulating cell elongation. In summary, our study suggests a conserved function for GRX genes in conferring root cell elongation and enhancing resilience of pearl millet to its Sahelian environment.

plant biology↗

bioRxiv2023

A strongly improved assembly of the pearl millet reference genome using OxfordNanopore long reads and optical mapping

Pearl millet (Pennisetum glaucum (L.)) R. Br. syn. Cenchrus americanus (L.) Morrone) is an important crop in South Asia and sub-Saharan Africa which contributes to ensure food security. Its genome has an estimated size of 1.76 Gb and displays a high level of repetitiveness above 80%. A first assembly was previously obtained for the Tift 23D2B1-P1-P5 cultivar genotype using short-read sequencing technologies. This assembly is however incomplete and fragmented with around 200 Mb unplaced on chromosomes. We report here an improved quality assembly of the pearl millet Tift 23D2B1-P1-P5 cultivar genotype obtained with an approach combining Oxford Nanopore long reads and Bionano Genomics optical maps. This strategy allowed us to add around 200 Mb at the chromosome-level assembly. Moreover we strongly improved continuity in the order of the contigs and scaffolds wihtin the chromosomes, particularly in the centromeric regions. Notably, we added more than 100 Mb around the centromeric region on chromosome 7. This new assembly also displayed a higher gene completeness with a complete BUSO score of 98.4% using the Poales database. This more complete and higher quality assembly of the Tift 23D2B1-P1-P5 genotype now available to the community will help in the development of research on the role of structural variants, and more broadly in genomics studies and the breeding of pearl millet.

genomics↗

bioRxiv2022

Frugal alignment-free identification of FLT3-internal tandem duplications with FiLT3r

BackgroundInternal tandem duplications in the FLT3 gene, termed FLT3-ITDs, are useful molecular markers in acute myeloid leukemia (AML) for patient risk stratification and follow-up. FLT3-ITDs are increasingly screened through high-throughput sequencing (HTS) raising the need for robust and efficient algorithms. We developed a new algorithm, which performs no alignment and uses little resources, to identify and quantify FLT3-ITDs in HTS data. ResultsOur algorithm (FiLT3r) focuses on the k-mers from reads covering FLT3 exons 14 and 15. We show that those k-mers bring enough information to accurately detect, determine the length and quantify FLT3-ITD duplications. We compare the performances of FiLT3r to state-of-the-art alternatives and to fragment analysis, the gold standard method, on a cohort of 185 AML patients sequenced with capture-based HTS. On this dataset FiLT3r is more precise (no false positive nor false negative) than the other software evaluated. We also assess the software on public RNA-Seq data, which confirms the previous results and shows that FiLT3r requires little resources compared to other software. ConclusionFiLT3r is a free software available at https://gitlab.univ-lille.fr/filt3r/filt3r. The repository also contains a Snakefile to reproduce our experiments. We show that FiLT3r detects FLT3-ITDs better than other software while using less memory and time.

bioinformatics↗

bioRxiv2020

REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets

MotivationIn this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets. ResultsWe used REINDEER to index the abundances of sequences within 2,585 human RNA-seq experiments in 45 hours using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of 4 billion distinct k-mers across 2,585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph (DBG) of each dataset, then conceptually merges those DBGs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances. Availabilityhttps://github.com/kamimrcht/REINDEER Contactcamille.marchet@univ-lille.fr

bioinformatics↗

bioRxiv2019

Data structures based on k-mers for querying large collections of sequencing datasets

High-throughput sequencing datasets are usually deposited in public repositories, e.g. the European Nucleotide Archive, to ensure reproducibility. As the amount of data has reached petabyte scale, repositories do not allow to perform online sequence searches; yet such a feature would be highly useful to investigators. Towards this goal, in the last few years several computational approaches have been introduced to index and query large collections of datasets. Here we propose an accessible survey of these approaches, which are generally based on representing datasets as sets of k-mers. We review their properties, introduce a classification, and present their general intuition. We summarize their performance and highlight their current strengths and limitations.

bioinformatics↗

bioRxiv2017

Exhaustive capture of biological variation in RNA-seq data through k-mer decomposition

Each individual cell produces its own set of transcripts, which is the combined result of genetic variation, transcription regulation and post-transcriptional processing. Due to this combinatorial nature, obtaining the exhaustive set of full-length transcripts for a given species is a never-ending endeavor. Yet, each RNA deep sequencing experiment produces a variety of transcripts that depart from the reference transcriptome and should be properly identified. To address this challenge, we introduce a k-mer-based software protocol for capturing local RNA variation from a set of standard RNA-seq libraries, independently of a reference genome or transcriptome. Our software, called DE-kupl, analyzes k-mer contents and detects k-mers with differential abundance directly from the raw data files, prior to assembly or mapping. This enables to retrieve the virtually complete set of unannotated variation lying in an RNA-seq dataset. This variation is subsequently assigned to biological events such as differential lincRNAs, antisense RNAs, splice and polyadenylation variants, introns, expressed repeats, and SNV-harboring or exogenous RNA. We applied DE-kupl to public RNA-seq datasets, including an Epythelial-Mensenchymal Transition model and different human tissues. DE-kupl identified abundant novel events and showed excellent reproducibility when applied to independent deep sequencing experiments. DE-kupl is a new paradigm for analyzing differential RNA-seq data with no preconception on target events, which can also provide fresh insights into existing RNA-seq repositories.

bioinformatics↗