Biology subjects

Bourgey, M.

Publications and source records attributed to Bourgey, M..

9 recordsLinked to original sources

bioRxiv2025

Finding an optimal sequencing strategy to detect short and long genetic variants in a human genome

Advances in DNA sequencing have transformed genomics, enabling comprehensive insights into human genetic variation. While short-read sequencing (SRS) remains dominant due to its high accuracy and affordability, its limitations in complex genomic regions have spurred the adoption of long-read sequencing (LRS) platforms, such as those from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). Despite these advances, there is still a lack of systematic, large-scale benchmarking of variant calling performance across diverse platforms, variant types, sequencing depths, and genomic contexts. Here, we present a comprehensive benchmark of sequencing technologies and variant calling algorithms, evaluating their performance in detecting single nucleotide polymorphisms (SNPs), insertions/deletions (indels), and structural variants (SVs). We show that while SRS combined with DeepVariant or DRAGEN offers excellent small variant detection in well-mapped regions, LRS technologies significantly outperform SRS in complex regions and SV detection. PacBio achieves high SNP and smaller SV accuracy even at moderate coverage, while ONT excels in detecting large SVs. The SV callers Dysgu and SVIM emerged as top performers across LRS datasets. Our results highlight that no single platform is optimal for all variant types or regions: SRS remains optimal for high-throughput small variant detection in accessible regions, whereas LRS is critical for capturing SVs and resolving difficult-to-map loci. These findings offer practical guidance for selecting sequencing technologies, coverage and variant calling strategies tailored to specific research or clinical goals, contributing to more accurate and cost-effective genomic analyses. Highlights[bullet] State-of-the-art short variant calling algorithms are highly comparable but focus on precision and sensitivity differently [bullet]Short-read technologies outperform long-read technologies for small SNP and InDels, with the exception of difficult-to-map variants. [bullet]To capture the majority of variants, a minimum coverage of 15x for PacBio, 20x for SRS, or 30x for ONT is required. However, optimal coverage depends on zygosity, variant type, and the region of interest. [bullet]Long-read technologies outperform short-read technologies for all validation sets tested for deletions and insertions in all size categories.

genomics↗

bioRxiv2023

Evolution of chromosome arm aberrations in breast cancer through genetic network rewiring

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize the evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. TCGA data analysis showed recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a unique panel of 23 primary tumor/patient-derived xenograft basal breast cancers revealed early evolution of chr4p deletion. Mechanistically we show that Chr4p loss is associated with enhanced proliferation. Gene function studies identified an unknown gene, C4orf19, within chr4p, which suppressed proliferation when overexpressed and is a novel member of a PDCD10-GCKIII kinase module, we name as PGCA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames, identified chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner implicating network interactions. Together this sheds light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

genomics↗

bioRxiv2023

WEGS: a cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome

0Whole genome sequencing (WGS) at high-depth (30X) allows the accurate discovery of variants in the coding and non-coding DNA regions and helps elucidate the genetic underpinnings of human health and diseases. Yet, due to the prohibitive cost of high-depth WGS, most large-scale genetic association studies use genotyping arrays or high-depth whole exome sequencing (WES). Here we propose a novel, cost-effective method, which we call "Whole Exome Genome Sequencing" (WEGS), that combines low-depth WGS and high-depth WES with up to 8 samples pooled and sequenced simultaneously (multiplexed). We experimentally assess the performance of WEGS with four different depth of coverage and sample multiplexing configurations. We show that the optimal WEGS configurations are 1.7-2.0 times cheaper than standard WES (no-plexing), 1.8-2.1 times cheaper than high-depth WGS, reach similar recall and precision rates in detecting coding variants as WES, and capture more population-specific variants in the rest of the genome that are difficult to recover when using genotype imputation methods. We apply WEGS to 862 patients with peripheral artery disease and show that it directly assesses more known disease-associated variants than a typical genotyping array and thousands of non-imputable variants per disease-associated locus.

genomics↗

bioRxiv2020

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

bioinformatics↗

bioRxiv2020

Human T-bet governs innate and innate-like adaptive IFN-γ immunity against mycobacteria

Inborn errors of human IFN-{gamma} immunity underlie mycobacterial disease. We report a patient with mycobacterial disease due to an inherited deficiency of the transcription factor T-bet. This deficiency abolishes the expression of T-bet target genes, including IFNG, by altering chromatin accessibility and DNA methylation in CD4+ T cells. The patient has profoundly diminished counts of mycobacterial-reactive circulating NK, invariant NKT (iNKT), mucosal-associated invariant T (MAIT), and V{delta}2+ {gamma}{delta} T lymphocytes, and of non-mycobacterial-reactive classic TH1 lymphocytes, the remainders of which also produce abnormally low amounts of IFN-{gamma}. Other IFN-{gamma}-producing lymphocyte subsets however develop normally, but with low levels of IFN-{gamma} production, with exception of V{delta}2- {gamma}{delta} T lymphocytes, which produce normal amounts of IFN-{gamma} in response to non-mycobacterial stimulation, and non-classic TH1 (TH1*) lymphocytes, which produce IFN-{gamma} normally in response to mycobacterial antigens. Human T-bet deficiency thus underlies mycobacterial disease by preventing the development of, and IFN-{gamma} production by, innate (NK) and innate-like adaptive lymphocytes (iNKT, MAIT, and V{delta}2+ {gamma}{delta} T cells), with mycobacterial-specific, IFN-{gamma}-producing, purely adaptive {beta} TH1* cells unable to compensate for this deficit.

immunology↗

bioRxiv2019

Legacy Data Confounds Genomics Studies

Recent reports have identified differences in the mutational spectra across human populations. While some of these reports have been replicated in other cohorts, most have been reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data is used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower-quality data from the early phases of the 1kGP thus continues to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data.

bioinformatics↗

bioRxiv2018

GenPipes: an open-source framework for distributed and scalable genomic analyses

With the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing. Here we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for High Performance Computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA-Seq, ChIP-Seq, DNA-Seq, Methyl-Seq, Hi-C, capture Hi-C, metagenomics and PacBio long read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has been already configured on several servers and a docker image is also available to facilitate additional installations. In summary, GenPipes offers genomic researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.

bioinformatics↗

bioRxiv2018

Single-cell RNA-seq reveals that glioblastoma recapitulates normal brain development

SummaryCancer stem cells are critical for cancer initiation, development, and resistance to treatments. Our understanding of these processes, and how they relate to glioblastoma heterogeneity, is limited. To overcome these limitations, we performed single-cell RNA-sequencing on 38 296 glioblastoma cells and 22 637 normal human fetal brain cells. Using an unbiased approach, we mapped the lineage hierarchy of the developing human brain and compared the transcriptome of each cancer cell to this roadmap. We discovered a conserved neural trilineage cancer hierarchy with glial progenitor-like cells at the apex. We also found that this progenitor population contains the majority of cancers cycling cells and is the origin of heterogeneity. Finally, we show that this hierarchal map can be used to identify therapeutic targets specific to progenitor cancer stem cells. Our analyses show that normal brain development reconciles glioblastoma development, unravels the origin of glioblastoma heterogeneity, and helps to identify cancer stem cell-specific targets.

cancer biology↗

bioRxiv2018

Deleterious genetic variants in NOTCH1 are a major contributor to the incidence of non-syndromic Tetralogy of Fallot

AimsFamilial recurrence studies provide strong evidence for a genetic component to the predisposition to sporadic, non-syndromic Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease (CHD) phenotype. Rare genetic variants have been identified as important contributors to the risk of CHD, but relatively small numbers of TOF cases have been studied to date. Here, we use whole exome sequencing to assess the prevalence of rare, potentially deleterious variants in candidate genes previously associated with both syndromic and non-syndromic TOF, in the largest cohort of non-syndromic TOF patients reported to date.\n\nMethods & Results829 non-syndromic TOF patients underwent whole exome sequencing. A systematic review of the literature was conducted which revealed 77 genes in which mutations had been reported in patients with TOF. The presence of rare, deleterious variants in the 77 candidate genes was determined, defined by a minor allele frequency of [≤] 0.001 and scaled combined annotation-dependent depletion (CADD) score of [≥] 20. We found a clustering of heterozygous rare, deleterious variants in NOTCH1 (P=1.89E-15), DOCK6 (P=2.93E-07), MYOM2 (P= 7.35E-05), TTC37 (P=0.016), MESP1 (P=0.024) and TBX1 (P=0.039), after correcting for multiple testing. NOTCH1 was most frequently found to harbour deleterious variants. Changes were observed in 49 patients (6%; 95% confidence interval [CI]: 4.5% - 7.8%) and included six truncating/frameshift variants and forty missense variants. Sanger sequencing of the unaffected parents of thirteen cases identified five de novo variants. Variants were not confined to a single functional domain of the NOTCH1 protein but significant clustering of variants was evident in the EGF-like repeats (P=0.018). Three NOTCH1 missense variants (p.G200R, p.C607Y and de novo p.N1875S) were subjected to functional evaluation and showed a reduction in Jagged1 ligand-induced NOTCH signalling. p.C607Y, which exhibited the most significant reduction in signalling, also perturbed S1 cleavage of the NOTCH1 receptor in the Golgi.\n\nConclusionThe NOTCH1 locus is a frequent site of genetic variants predisposing to non-syndromic TOF with 6% of patients exhibiting rare, deleterious variants. Our data supports the polygenic origin of TOF and suggests larger studies may identify additional loci.

genetics↗