Biology subjects

Holley, G.

Publications and source records attributed to Holley, G..

9 recordsLinked to original sources

bioRxiv2023

kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq

The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.

bioinformatics↗

bioRxiv2022

Accurate quantification of single-nucleus and single-cell RNA-seq transcripts

In single-cell and single-nucleus RNA sequencing, the coexistence of nascent (unprocessed) and mature (processed) mRNA poses challenges in accurate read mapping and the interpretation of count matrices. The traditional transcriptome reference, defining the region of interest in bulk RNA-seq, restricts its focus to mature mRNA transcripts. This restriction leads to two problems: reads originating outside of the region of interest are prone to mismapping within this region, and additionally, such external reads cannot be matched to specific transcript targets. Expanding the region of interest to encompass both nascent and mature mRNA transcript targets provides a more comprehensive framework for RNA-seq analysis. Here, we introduce the concept of distinguishing flanking k-mers (DFKs) to improve mapping of sequencing reads. We have developed an algorithm to identify DFKs, which serve as a sophisticated background filter, enhancing the accuracy of mRNA quantification. This dual strategy of an expanded region of interest coupled with the use of DFKs enhances the precision in quantifying both mature and nascent mRNA molecules, as well as in delineating reads of ambiguous status.

bioinformatics↗

bioRxiv2021

The sequences of 150,119 genomes in the UK biobank

We describe the analysis of whole genome sequences (WGS) of 150,119 individuals from the UK biobank (UKB). This constitutes a set of high quality variants, including 585,040,410 SNPs, representing 7.0% of all possible human SNPs, and 58,707,036 indels. The large set of variants allows us to characterize selection based on sequence variation within a population through a Depletion Rank (DR) score for windows along the genome. DR analysis shows that coding exons represent a small fraction of regions in the genome subject to strong sequence conservation. We define three cohorts within the UKB, a large British Irish cohort (XBI) and smaller African (XAF) and South Asian (XSA) cohorts. A haplotype reference panel is provided that allows reliable imputation of most variants carried by three or more sequenced individuals. We identified 895,055 structural variants and 2,536,688 microsatellites, groups of variants typically excluded from large scale WGS studies. Using this formidable new resource, we provide several examples of trait associations for rare variants with large effects not found previously through studies based on exome sequencing and/or imputation.

genetics↗

bioRxiv2021

Population-scale detection of non-reference sequence variants using colored de Bruijn Graphs

MotivationWith the increasing throughput of sequencing technologies, structural variant (SV) detection has become possible across tens of thousands of genomes. Non-reference sequence (NRS) variants have drawn less attention compared to other types of SVs due to the computational complexity of detecting them. When using short-read data, the detection of NRS variants inevitably involves a de novo assembly which requires high-quality sequence data at high coverage. Previous studies have demonstrated how sequence data of multiple genomes can be combined for the reliable detection of NRS variants. However, the algorithms proposed in these studies have limited scalability to larger sets of genomes. ResultsWe introduce PopIns2, a tool to discover and characterize NRS variants in many genomes, which scales to considerably larger numbers of genomes than its predecessor PopIns. In this article, we briefly outline the PopIns2 workflow and highlight our novel algorithmic contributions. We developed an entirely new approach for merging contig assemblies of unaligned reads from many genomes into a single set of NRS using a colored de Bruijn graph. Our tests on simulated data indicate that the new merging algorithm ranks among the best approaches in terms of quality and reliability and that PopIns2 shows the best precision for a growing number of genomes processed. Results on the Polaris Diversity Cohort and a set of 1000 Icelandic human genomes demonstrate unmatched scalability for the application on population-scale datasets. AvailabilityThe source code of PopIns2 is available from https://github.com/kehrlab/PopIns2. Contactthomas.krannich@bihealth.de or birte.kehr@klinik.uni-regensburg.de

bioinformatics↗

bioRxiv2020

Benchmarking small variant detection with ONT reveals high performance in challenging regions

BackgroundThe development of long read sequencing (LRS) has led to greater access to the human genome. LRS produces long read lengths at the cost of high error rates and has shown to be more useful in calling structural variants than short read sequencing (SRS) data. In this paper we evaluate how to use LRS data from Oxford Nanopore Technologies (ONT) to call small variants in regions in- and outside the reach of SRS. ResultsCalling single nucleotide polymorphisms (SNPs) with ONT data has comparable accuracy to Illumina when evaluating against the Genome in a Bottle truth set v4.2. In the major histocompatibility complex (MHC) and regions where mapping short reads is difficult, the F-measure of ONT calls exceeds those of short reads by 2-4% when sequence coverage is 20X or greater. We develop recommendations for how to perform small variant calling with LRS data and improve current approaches to the difficult regions by re-genotyping variants to increase the F-measure from 97.24% to 98.78%. Furthermore, we show how LRS can call variants in genomic regions inaccessible to SRS, including medically relevant genes such as STRC and CFC1B. ConclusionsAlthough small variant calling in LRS data is still immature, current methods are clearly useful in difficult and inaccessible regions of the genome, enabling variant calling in medically relevant genes not accessible to SRS.

bioinformatics↗

bioRxiv2020

Ratatosk - Hybrid error correction of long reads enables accurate variant calling and assembly

MotivationLong Read Sequencing (LRS) technologies are becoming essential to complement Short Read Sequencing (SRS) technologies for routine whole genome sequencing. LRS platforms produce DNA fragment reads, from 103 to 106 bases, allowing the resolution of numerous uncertainties left by SRS reads for genome reconstruction and analysis. In particular, LRS characterizes long and complex structural variants undetected by SRS due to short read length. Furthermore, assemblies produced with LRS reads are considerably more contiguous than with SRS while spanning previously inaccessible telomeric and centromeric regions. However, a major challenge to LRS reads adoption is their much higher error rate than SRS of up to 15%, introducing obstacles in downstream analysis pipelines. ResultsWe present Ratatosk, a new error correction method for erroneous long reads based on a compacted and colored de Bruijn graph built from accurate short reads. Short and long reads color paths in the graph while vertices are annotated with candidate Single Nucleotide Polymorphisms. Long reads are subsequently anchored to the graph using exact and inexact fc-mer matches to find paths corresponding to corrected sequences. We demonstrate that Ratatosk can reduce the raw error rate of Oxford Nanopore reads 6-fold on average with a median error rate as low as 0.28%. Ratatosk corrected data maintain nearly 99% accurate SNP calls and increase indel call accuracy by up to about 40% compared to the raw data. An assembly of the Ashkenazi individual HG002 created from Ratatosk corrected Oxford Nanopore reads yields a contig N50 of 43.22 Mbp and less misassemblies than an assembly created from PacBio HiFi reads. Availabilityhttps://github.com/DecodeGenetics/Ratatosk Contactguillaume.holley@decode.is

bioinformatics↗

bioRxiv2020

BlastFrost: Fast querying of 100,000s of bacterial genomes in Bifrost graphs

BlastFrost is a highly efficient method for querying 100,000s of genome assemblies. It builds on Bifrost, a recently developed dynamic data structure for compacted and colored de Bruijn graphs from bacterial genomes. BlastFrost queries a Bifrost data structure for sequences of interest, and extracts local subgraphs, thereby enabling the efficient identification of the presence or absence of individual genes or single nucleotide sequence variants. Here we describe the algorithms and implementation of BlastFrost. We also present two exemplar practical applications. In the first, we determined the presence of the individual genes within the SPI-2 Salmonella pathogenicity island within a collection of 926 representative genomes in minutes. In the second application, we determined the existence of known single nucleotide polymorphisms associated with fluoroquinolone resistance in the genes gyrA, gyrB and parE among 190, 209 Salmonella genomes. BlastFrost is available for download at https://github.com/nluhmann/BlastFrost.

bioinformatics↗

bioRxiv2019

Long read sequencing of 1,817 Icelanders provides insight into the role of structural variants in human disease

Long-read sequencing (LRS) promises to improve characterization of structural variants (SVs), a major source of genetic diversity. We generated LRS data on 3,622 Icelanders using Oxford Nanopore Technologies, and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions), spanning a median of 10 Mb per haploid genome. We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association with a rare (AF = 0.037%) deletion of the first exon of PCSK9. Carriers of this deletion have 0.93 mmol/L (1.31 SD) lower LDL cholesterol levels than the population average (p-value = 7.0{middle dot}10-20). We also discovered an association with a multi-allelic SV inside a large repeat region, contained within single long reads, in an exon of ACAN. Within this repeat region we found 11 alleles that differ in the number of a 57 bp-motif repeat, and observed a linear relationship (0.016 SD per motif inserted, p = 6.2{middle dot}10-18) between the number of repeats carried and height. These results show that SVs can be accurately characterized at population scale using long read sequence data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.

genetics↗

bioRxiv2019

Bifrost - Highly parallel construction and indexing of colored and compacted de Bruijn graphs

MotivationDe Bruijn graphs are the core data structure for a wide range of assemblers and genome analysis software processing High Throughput Sequencing datasets. For population genomic analysis, the colored de Bruijn graph is often used in order to take advantage of the massive sets of sequenced genomes available for each species. However, memory consumption of tools based on the de Bruijn graph is often prohibitive, due to the high number of vertices, edges or colors in the graph. In order to process large and complex genomes, most short-read assemblers based on the de Bruijn graph paradigm reduce the assembly complexity and memory usage by compacting first all maximal non-branching paths of the graph into single vertices. Yet, de Bruijn graph compaction is challenging as it requires the uncompacted de Bruijn graph to be available in memory.\n\nResultsWe present a new parallel and memory efficient algorithm enabling the direct construction of the compacted de Bruijn graph without producing the intermediate uncompacted de Bruijn graph. Bifrost features a broad range of functions such as sequence querying, storage of user data alongside vertices and graph editing that automatically preserve the compaction property. Bifrost makes full use of the dynamic index efficiency and proposes a graph coloring method efficiently mapping each k-mer of the graph to the set of genomes in which it occurs. Experimental results show that our algorithm is competitive with state-of-the-art de Bruijn graph compaction and coloring tools. Bifrost was able to build the colored and compacted de Bruijn graph of about 118,000 Salmonella genomes on a mid-class server in about 4 days using 103 GB of main memory.\n\nAvailabilityhttps://github.com/pmelsted/bifrost available with a BSD-2 license\n\nContactguillaumeholley@gmail.com

bioinformatics↗