Biology subjects

Eveleigh, R.

Publications and source records attributed to Eveleigh, R..

5 recordsLinked to original sources

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↗

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

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↗

bioRxiv2017

Accuracy of programs for the determination of HLA alleles from NGS data

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing on the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as many HLA alleles are major genetic factors that confer susceptibility to autoimmune diseases and is important for the matching of HLA alleles in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next generation sequencing (NGS) data. As genome screens are becoming increasingly routine in research, we wanted to test how well HLA alleles can be deduced from genome screens not designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using the ImmunoSEQ platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from the 1000 Genomes collection. When used with the default settings none of the methods gave perfect results for all the genes and samples. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. Most of the algorithms did not perform very well for the exome-only data. The results indicate that the use of these algorithms for accurate HLA allele determination based on NGS data is not straightforward.

immunology↗