Biology subjects

Fostier, J.

Publications and source records attributed to Fostier, J..

7 recordsLinked to original sources

bioRxiv2026

Multiparental RNA-seq driven eQTL screening identifies loci underlying host plant fitness in a generalist herbivore

The two-spotted spider mite (Tetranychus urticae) is an extremely polyphagous pest, yet the genetic basis of this adaptive potential remains to be fully elucidated. Since expression quantitative trait loci (eQTLs) provide the genetic basis of numerous phenotypes, we aimed to identify trans-eQTL hotspots underlying T. urticae fitness upon transfer from a common (bean) to a challenging (tomato) host plant. Nonetheless, the identification of trans-eQTLs is complex and often constrained by methodological challenges and high costs. Therefore, we employed a multiparental mapping strategy driven by RNA-seq, enabling us to leverage extensive genetic variation in a cost-efficient manner. A randomly mating population was generated on bean from a small number of genetically diverse, often heterozygous parents, and subsequently transferred to tomato prior to RNA-seq. Upon whole genome sequencing of the parents, RNA-seq of the mapping population individuals was sufficient to reconstruct their genomes as a combination of parental haploblocks. Subsequent eQTL mapping identified 23 distinct trans-eQTL hotspot regions associated with the expression of numerous target genes. The most prominent hotspot on chromosome 3 was associated with approximately 900 genes and showed enrichment for functions related to detoxification and digestion. Furthermore, 4 of these trans-eQTL hotspot genotypes explained significant variation in mite fitness on the challenging host plant. This contrasted a traditional QTL mapping approach, where these genotypes could not be detected due to multiple testing correction. Our study hence offers a powerful strategy for trans-eQTL hotspot screening and the discovery of trait-associated loci in complex, multiparental genetic backgrounds.

bioinformatics↗

bioRxiv2026

A Conditional Random Field approach for de novo reconstruction of bacterial haplotypes from a de Bruijn graph representation

BackgroundDetecting distinct bacterial strains in a mixed sample is an important, yet less well-developed aspect of metagenomic research. Several methods exist that successfully retrieve a de novo reconstruction of viral strains. However, the reconstruction of bacterial haplotypes poses its own distinct challenges, and methods that successfully reconstruct full genome-length bacterial strains de novo are scarce. Here, we develop HaploDetox, a method for de novo bacterial haplotype reconstruction from short reads. We use a de Bruijn graph representation of the reads in which nodes correspond with k-mers from the read set and arcs represent overlap between two nodes sequences. Our aim is to accurately assign labels to each node and arc in the graph to reveal the presence or absence of their corresponding sequence in individual strains. ResultsUsing a negative binomial mixture model, we model the relationship between the read coverage of nodes and arcs in the graph and their presence in a strain. We achieve improved labelling accuracy by including contextual information from neighbouring nodes and arcs with a Conditional Random Field. These labels are used to extract strain-specific de Bruijn graphs from the original graph. Additionally, we allow users to assess the number of strains present in the dataset based on model selection criteria. We evaluate our node/arc labelling accuracy on simulated datasets and in silico mixes of real datasets containing different numbers of strains, as well as on in vitro mixed real datasets. Existing de novo haplotype reconstruction methods present their reconstruction as strain-specific sets of SNPs. We demonstrate that HaploDetox assigns strain-specific SNPs with a higher recall and similar precision than existing methods, by aligning the unitigs from strain-specific graphs to a reference genome. ConclusionsWe achieve improved strain-specific SNP phasing accuracy as compared to existing methods for de novo bacterial haplotype reconstruction. Additionally, HaploDetox is not limited to the determination of strain-specific SNPs, and other types of variant calls can be obtained through reference alignment. Finally, strain-specific de Bruijn graphs are an important first step towards full genome-length bacterial haplotype-aware assembly.

bioinformatics↗

bioRxiv2025

Columba: Fast Approximate Pattern Matching withOptimized Search Schemes

Aligning sequencing reads to reference genomes is a fundamental task in bioinformatics. Aligners can be classified as lossy or lossless: lossy aligners prioritize speed by reporting only one or a few high-scoring alignments, whereas lossless aligners output all optimal alignments, ensuring completeness and sensitivity. This paper introduces Columba, a high-performance lossless aligner tailored for Illumina sequencing data. Columba processes single or paired-end reads in FASTQ format and outputs alignments in SAM format. By utilizing advanced search schemes and bit-parallel alignment techniques, Columba achieves exceptional speed. Columba is available in two variants. The first is based on the bidirectional FM-index. The second, Columba RLC, employs run-length compression using a bidirectional move structure, significantly reducing memory usage for large, repetitive datasets like pan-genomes. Through extensive benchmarking, Columba outperforms existing lossless aligners in speed, particularly at higher error rates. Tests on the human genome and bacterial and human pan-genome datasets demonstrate Columbas robustness and efficiency. We integrated Columba into the OptiType HLA genotyping pipeline, where it substantially reduced computational time while maintaining accuracy. These results position Columba as a versatile, state-of-the-art tool for high-sensitivity genomic analyses.

bioinformatics↗

bioRxiv2025

Run-length compressed metagenomic read classification with SMEM-finding and tagging

Metagenomic read classification is a fundamental task in computational biology, yet it remains challenging due to the scale, diversity, and complexity of sequencing datasets. We propose a novel, run-length compressed index based on the move structure that enables efficient multi-class metagenomic classification in O(r) space, where r is the number of character runs in the BWT of the reference text. Our method identifies all super-maximal exact matches (SMEMs) of length at least L between a read and the reference dataset and associates each SMEM with one class identifier using a sampled tag array. A consensus algorithm then compacts these SMEMs with their class identifier into a single classification per read. We are the first to perform run-length compressed read classification based on full SMEMs instead of semi-SMEMs. We evaluate our approach on both long and short reads in two conceptually distinct datasets: a large bacterial pan-genome with few metagenomic classes and a smaller 16S rRNA gene database spanning thousands of genera or classes. Our method consistently outperforms SPUMONI 2 in accuracy and runtime while maintaining the same asymptotic memory complexity of O(r). Compared to Cliffy, we demonstrate better memory efficiency while achieving superior accuracy on the simpler dataset and comparable performance on the more complex one. Overall, our implementation carefully balances accuracy, runtime, and memory usage, offering a versatile solution for metagenomic classification across diverse datasets. The open-source C++11 implementation is available at https://github.com/biointec/tagger under the AGPL-3.0 license.

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bioRxiv2024

b-move: faster bidirectional character extensions in a run-length compressed index

Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.s r-index and Nishimoto and Tabeis move structure, alleviate memory constraints but focus primarily on backward search for MEM-finding. Arakawa et al.s br-index initiates complete approximate pattern matching using bidirectional search in run-length compressed space, but with significant computational overhead due to complex memory access patterns. We introduce b-move, a novel bidirectional extension of the move structure, enabling fast, cache-efficient bidirectional character extensions in run-length compressed space. It achieves bidirectional character extensions up to 8 times faster than the br-index, closing the performance gap with FM-index-based alternatives, while maintaining the br-indexs favorable memory characteristics. For example, all available complete E. coli genomes on NCBIs RefSeq collection can be compiled into a b-move index that fits into the RAM of a typical laptop. Thus, b-move proves practical and scalable for pan-genome indexing and querying. We provide a C++ implementation of b-move, supporting efficient lossless approximate pattern matching including locate functionality, available at https://github.com/biointec/b-move under the AGPL-3.0 license. FundingLore Depuydt: PhD Fellowship FR (1117322N), Research Foundation - Flanders (FWO) Luca Renders: PhD Fellowship SB (1SE7822N), Research Foundation - Flanders (FWO) Travis Gagie: NSERC Discovery Grant RGPIN-07185-2020 to Travis Gagie and NIH grant R01HG011392 to Ben Langmead

bioinformatics↗

bioRxiv2020

Multithreaded variant calling in elPrep 5

We present elPrep 5, which updates the elPrep framework for processing sequencing alignment/map files with variant calling. elPrep 5 can now execute the full pipeline described by the GATK Best Practices for variant calling, which consists of PCR and optical duplicate marking, sorting by coordinate order, base quality score recalibration, and variant calling using the haplotype caller algorithm. elPrep 5 produces identical BAM and VCF output as GATK4 while significantly reducing the runtime by parallelizing and merging the execution of the pipeline steps. Our benchmarks show that elPrep 5 speeds up the runtime of the variant calling pipeline by a factor 8-16x on both whole-exome and whole-genome data while using the same hardware resources as GATK 4. This makes elPrep 5 a suitable drop-in replacement for GATK 4 when faster execution times are needed.

bioinformatics↗

bioRxiv2018

elPrep: A multithreaded framework for sequence analysis

We present elPrep 4, a reimplementation from scratch of the elPrep framework for processing sequence alignment map files in the Go programming language. elPrep 4 includes multiple new features allowing us to process all of the preparation steps defined by the GATK Best Practice pipelines for variant calling. This includes new and improved functionality for sorting, (optical) duplicate marking, base quality score recalibration, BED and VCF parsing, and various filtering options. The implementations of these options in elPrep 4 faithfully reproduce the outcomes of their counterparts in GATK 4, SAMtools, and Picard, even though the underlying algorithms are redesigned to take advantage of elPreps parallel execution framework to vastly improve the runtime and resource use compared to these tools. Our benchmarks show that elPrep executes the preparation steps of the GATK Best Practices up to 13x faster on WES data, and up to 7.4x faster for WGS data compared to running the same pipeline with GATK 4, while utilizing fewer compute resources.

bioinformatics↗