publications

2024

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

   Téo Lemane, Nolan Lezzoche, Julien Lecubin, Eric Pelletier, Magali Lescot, Rayan Chikhi, and Pierre Peterlongo

   Nature Computational Science, Feb 2024

   Publisher: Nature Publishing Group

   Abstract Link

   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. With negligible false positive rates below 0.01%, kmindex outperforms the precision of existing approaches by four orders of magnitude. Here we demonstrate the scalability of kmindex by successfully indexing 1,393 marine seawater metagenome samples from the Tara Oceans project. Additionally, we introduce the publicly accessible web server Ocean Read Atlas, which enables real-time queries on the Tara Oceans dataset.

2. The genomics and evolution of inter-sexual mimicry and female-limited polymorphisms in damselflies

   Beatriz Willink, Kalle Tunström, Sofie Nilén, Rayan Chikhi, Téo Lemane, Michihiko Takahashi, Yuma Takahashi, Erik I. Svensson, and Christopher West Wheat

   Nature Ecology & Evolution, Jan 2024

   Publisher: Nature Publishing Group

   Abstract Link

   Sex-limited morphs can provide profound insights into the evolution and genomic architecture of complex phenotypes. Inter-sexual mimicry is one particular type of sex-limited polymorphism in which a novel morph resembles the opposite sex. While inter-sexual mimics are known in both sexes and a diverse range of animals, their evolutionary origin is poorly understood. Here, we investigated the genomic basis of female-limited morphs and male mimicry in the common bluetail damselfly. Differential gene expression between morphs has been documented in damselflies, but no causal locus has been previously identified. We found that male mimicry originated in an ancestrally sexually dimorphic lineage in association with multiple structural changes, probably driven by transposable element activity. These changes resulted in ~900 kb of novel genomic content that is partly shared by male mimics in a close relative, indicating that male mimicry is a trans-species polymorphism. More recently, a third morph originated following the translocation of part of the male-mimicry sequence into a genomic position ~3.5 mb apart. We provide evidence of balancing selection maintaining male mimicry, in line with previous field population studies. Our results underscore how structural variants affecting a handful of potentially regulatory genes and morph-specific genes can give rise to novel and complex phenotypic polymorphisms.

2023

1. decOM: similarity-based microbial source tracking of ancient oral samples using k-mer-based methods

   Camila Duitama González, Riccardo Vicedomini, Téo Lemane, Nicolas Rascovan, Hugues Richard, and Rayan Chikhi

   Microbiome, Nov 2023

   Abstract Link

   The analysis of ancient oral metagenomes from archaeological human and animal samples is largely confounded by contaminant DNA sequences from modern and environmental sources. Existing methods for Microbial Source Tracking (MST) estimate the proportions of environmental sources, but do not perform well on ancient metagenomes. We developed a novel method called decOM for Microbial Source Tracking and classification of ancient and modern metagenomic samples using k-mer matrices.

2022

1. PhD thesis: Indexing and analysis of large sequencing collections using k-mer matrices

   Téo Lemane

   Université de Rennes 1 & Inria, Dec 2022

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

   Téo Lemane, Paul Medvedev, Rayan Chikhi, and Pierre Peterlongo

   Bioinformatics Advances, Jan 2022

   Abstract Link PDF

   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 k-mers which approximates the desired set of all the non-erroneous k-mers present in the sample. However, this approximation is imperfect, especially in the case of metagenomics data. Erroneous but abundant k-mers are wrongly included, and non-erroneous but low-abundant ones are wrongly discarded. We propose kmtricks, a novel approach for generating Bloom filters from terabase-sized collections of sequencing data. Our main contributions are (i) an efficient method for jointly counting k-mers across multiple samples, including a streamlined Bloom filter construction by directly counting, partitioning and sorting hashes instead of k-mers, which is approximately four times faster than state-of-the-art tools; (ii) a novel technique that takes advantage of joint counting to preserve low-abundant k-mers present in several samples, improving the recovery of non-erroneous k-mers. Our experiments highlight that this technique preserves around 8× more k-mers than the usual yet crude filtering of low-abundance k-mers in a large metagenomics dataset.https://github.com/tlemane/kmtricks.Supplementary data are available at Bioinformatics Advances online.

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

   Téo Lemane, Rayan Chikhi, and Pierre Peterlongo

   Bioinformatics, Dec 2022

   Abstract Link PDF

   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.https://github.com/tlemane/kmdiffSupplementary data are available at Bioinformatics online.

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

   Yoann Dufresne, Teo Lemane, Pierre Marijon, Pierre Peterlongo, Amatur Rahman, Marek Kokot, Paul Medvedev, Sebastian Deorowicz, and Rayan Chikhi

   Bioinformatics, Sep 2022

   Abstract Link PDF

   Bioinformatics applications increasingly rely on ad hoc disk storage of k-mer sets, e.g. for de Bruijn graphs or alignment indexes. Here, we introduce the K-mer File Format as a general lossless framework for storing and manipulating k-mer sets, realizing space savings of 3–5× compared to other formats, and bringing interoperability across tools.Format specification, C++/Rust API, tools: https://github.com/Kmer-File-Format/.Supplementary data are available at Bioinformatics online.

2021

1. Identification of isolated or mixed strains from long reads: a challenge met on Streptococcus thermophilus using a MinION sequencer

   Grégoire Siekaniec, Emeline Roux, Téo Lemane, Eric Guédon, and Jacques Nicolas

   Microbial Genomics, Sep 2021

   Publisher: Microbiology Society,

   Abstract Link PDF

   This study aimed to provide efficient recognition of bacterial strains on personal computers from MinION (Nanopore) long read data. Thanks to the fall in sequencing costs, the identification of bacteria can now proceed by whole genome sequencing. MinION is a fast, but highly error-prone sequencing device and it is a challenge to successfully identify the strain content of unknown simple or complex microbial samples. It is heavily constrained by memory management and fast access to the read and genome fragments. Our strategy involves three steps: indexing of known genomic sequences for a given or several bacterial species; a request process to assign a read to a strain by matching it to the closest reference genomes; and a final step looking for a minimum set of strains that best explains the observed reads. We have applied our method, called ORI, on 77 strains of Streptococcus thermophilus . We worked on several genomic distances and obtained a detailed classification of the strains, together with a criterion that allows merging of what we termed ‘sibling’ strains, only separated by a few mutations. Overall, isolated strains can be safely recognized from MinION data. For mixtures of several non-sibling strains, results depend on strain abundance.