Biology subjects

MARCHET, C.

Publications and source records attributed to MARCHET, C..

4 recordsLinked to original sources

bioRxiv2025

Vizitig: context-rich exploration of sequencing datasets

Vizitig is the first platform for real-time exploration and querying of DNA and RNA sequence de Bruijn graphs across many samples, unifying visualization, metadata, and flexible search. It constructs compacted colored de Bruijn graphs from raw sequencing reads and reference sequences, then provides an interactive web interface for graph exploration. It integrates raw and reference-based data, handles complex variation, and provides a human-readable feature-based (also referred to as metadata) query language with scalable graph loading. Its domain-specific query language supports composable searches combining sequences of arbitrary size, genomic features such as gene or exon identifiers, and experimental factors such as sample identifier or abundance thresholds. On-demand subgraph loading retrieves only regions of interest, enabling interactive exploration of large datasets in the graphical user interface without loading the entire graph into memory. We demonstrate Vizitig's capabilities through case studies in pantranscriptomics and pangenomics. In pantranscriptomics, we recover fusion transcript breakpoints on long and short reads. In pangenomics, we explore sequence variations across yeast, rice, nematode, and human pangenomes. Vizitig scales from small virus genomes to human-scale pangenomes (our largest experiments comprises up to 20 assembled human haplotypes, on a laptop). Vizitig enables fast, reproducible analysis in both pangenomics and pantranscriptomics while providing a deployable and user-friendly working environment.

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↗

bioRxiv2023

Fractional Hitting Sets for Efficient and Lightweight Genomic Data Sketching

The exponential increase in publicly available sequencing data and genomic resources necessitates the development of highly efficient methods for data processing and analysis. Locality-sensitive hashing techniques have successfully transformed large datasets into smaller, more manageable sketches while maintaining comparability using metrics such as Jaccard and containment indices. However, fixed-size sketches encounter difficulties when applied to divergent datasets. Scalable sketching methods, such as Sourmash, provide valuable solutions but still lack resourceefficient, tailored indexing. Our objective is to create lighter sketches with comparable results while enhancing efficiency. We introduce the concept of Fractional Hitting Sets, a generalization of Universal Hitting Sets, which uniformly cover a specified fraction of the k-mer space. In theory and practice, we demonstrate the feasibility of achieving such coverage with simple but highly efficient schemes. By encoding the covered k-mers as super-k-mers, we provide a space-efficient exact representation that also enables optimized comparisons. Our novel tool, SuperSampler, implements this scheme, and experimental results with real bacterial collections closely match our theoretical findings. In comparison to Sourmash, SuperSampler achieves similar outcomes while utilizing an order of magnitude less space and memory and operating several times faster. This highlights the potential of our approach in addressing the challenges presented by the ever-expanding landscape of genomic data. SuperSampler is an open-source software and can be accessed at github.com/TimRouze/supersampler. The data required to reproduce the results presented in this manuscript is available at github.com/TimRouze/Expe_SPSP.

bioinformatics↗