Biology subjects

Pedersen, B. S.

Publications and source records attributed to Pedersen, B. S..

27 records · Page 2Linked to original sources

bioRxiv2019

Large, three-generation CEPH families reveal post-zygotic mosaicism and variability in germline mutation accumulation

The number of de novo mutations (DNMs) found in an offsprings genome is known to increase with both paternal and maternal age. But does the rate of mutation accumulation in parental gametes differ across families? To answer this question, we analyzed DNMs in 33 large, three-generation families collected in Utah by the Centre dEtude du Polymorphisme Humain (CEPH) consortium. We observed significant variability in parental age effects on DNM counts across families, ranging from 0.24 to 3.33 additional DNMs per year. Using up to 14 grandchildren in these families, we find that 3% of DNMs originated following primordial germ cell specification (PGCS) in a parent, and differ from non-mosaic germline DNMs in their mutational spectra. We also identify a median of 3 gonosomal mutations per sample in the F1 generation, which, along with post-PGCS DNMs, occur at equivalent frequencies on the paternal and maternal haplotypes. These results demonstrate that the rate of germline mutation accumulation varies among families with similar ancestry, and confirm that parental mosaicism is a substantial source of de novo mutations in children. Data and code availabilityCode used for statistical analysis and figure generation has been deposited on GitHub as a collection of annotated Jupyter Notebooks: https://github.com/quinlan-lab/ceph-dnm-manuscript. Data files containing germline de novo mutations, as well as the gonosomal and post-primordial germ cell specification (PGCS) mosaic mutations, are included with these Notebooks. To mitigate compatibility/version issues, we have also made all notebooks available in a Binder environment, accessible at the above GitHub repository.

genetics↗

bioRxiv2018

duphold: scalalable, depth-based annotation and curation of high-confidence structural variant calls.

Most structural variant detection tools use clusters of discordant read-pair and split-read alignments to identify variants, yet do not integrate depth of sequence coverage as an additional means to support or refute putative events. Here, we present duphold, as a new method to efficiently annotate structural variant calls with sequence depth information that can add (or remove) confidence to SV predicted to affect copy number. It indicates not only the change in depth across the event, but also the presence of a rapid change in depth relative to the regions surrounding the breakpoints. It uses a unique algorithm that allows the run time to be nearly independent of the number of variants. This performance is important for large, jointly-called projects with many samples, each of which must be evaluated at thousands of sites. We show that filtering on duphold annotations can greatly improve the specificity of deletion calls and that its annotations match visual inspection. Duphold can annotate structural variant predictions made from both short-read and long-read data. It is available under the MIT license at: https://github.com/brentp/duphold.

bioinformatics↗

bioRxiv2018

SV-plaudit: A cloud-based framework for manually curating thousands of structural variants

SV-plaudit is a framework for rapidly curating structural variant (SVs) predictions. For each SV, we generate an image that visualizes the coverage and alignment signals from a set of samples. Images are uploaded to our cloud framework where users assess the quality of each image using a client-side web application. Reports can then be generated as a tab-delimited file or annotated VCF. As a proof of principle, nine researchers collaborated for one hour to evaluate 1,350 SVs each. We anticipate that SV-plaudit will become a standard step in variant calling pipelines and the crowd-sourced curation of other biological results.\n\nCode available at https://github.com/jbelyeu/SV-plaudit\n\nDemonstration video available at https://www.youtube.com/watch?v=ono8kHMKxDs

bioinformatics↗

bioRxiv2018

hts-nim: scripting high-performance genomic analyses

MotivationExtracting biological insight from genomic data inevitably requires custom software. In many cases, this is accomplished with scripting languages, owing to their accessibility and brevity. Unfortunately, the ease of scripting languages typically comes at a substantial performance cost that is especially acute with the scale of modern genomics datasets.\n\nResultsWe present hts-nim, a high-performance library written in the Nim programming language that provides a simple, scripting-like syntax without sacrificing performance.\n\nAvailabilityhts-nim is available at https://github.com/brentp/hts-nim and the example tools are at https://github.com/brentp/hts-nim-tools both under the MIT license.\n\nContactbpederse@gmail.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

A map of constrained coding regions in the human genome.

Deep catalogs of genetic variation collected from many thousands of humans enable the detection of intraspecies constraint by revealing coding regions with a scarcity of variation. While existing techniques summarize constraint for entire genes, single metrics cannot capture the fine-scale variability in constraint within each protein-coding gene. To provide greater resolution, we have created a detailed map of constrained coding regions (CCRs) in the human genome by leveraging coding variation observed among 123,136 humans from the Genome Aggregation Database (gnomAD). The most constrained coding regions in our map are enriched for both pathogenic variants in ClinVar and de novo mutations underlying developmental disorders. CCRs also reveal protein domain families under high constraint, suggest unannotated or incomplete protein domains, and facilitate the prioritization of previously unseen variation in studies of disease. Finally, a subset of CCRs with the highest constraint likely exist within genes that cause yet unobserved human phenotypes owing to strong purifying selection.

genomics↗

bioRxiv2017

mosdepth: quick coverage calculation for genomes and exomes

Mosdepth is a new command-line tool for rapidly calculating genome-wide sequencing coverage. It measures depth from BAM (Li et al. 2009) or CRAM files at either each nucleotide position in a genome or for sets of genomic regions. Genomic regions may be specified as either a BED file to evaluate coverage across capture regions, or as a fixed-size window as required for copy-number calling. Mosdepth uses a simple algorithm that is computationally efficient and enables it to quickly produce optional coverage summaries. We demonstrate that mosdepth is faster than existing tools and provides flexibility in the types of coverage profiles produced.\n\nAvailabilitymosdepth is available from https://github.com/brentp/mosdepth under the MIT license..\n\nContactbpederse@gmail.com, aaronquinlan@gmail.com\n\nSupplementary informationDetailed documentation is available at https://github.com/brentp/mosdepth

bioinformatics↗

bioRxiv2017

GIGGLE: a search engine for large-scale integrated genome analysis

GIGGLE is a genomics search engine that identifies and ranks the significance of shared genomic loci between query features and thousands of genome interval files. GIGGLE scales to billions of intervals, is faster (+1,000X) than existing methods, and its speed extends the accessibility and utility of resources such as ENCODE, Roadmap Epigenomics, and GTEX by facilitating data integration and hypothesis generation. GIGGLE is available at https://github.com/ryanlayer/giggle.

bioinformatics↗

bioRxiv2017

Indexcov: fast coverage quality control for whole-genome sequencing

The BAM1 and CRAM2 formats provide a supplementary linear index that facilitates rapid access to sequence alignments in arbitrary genomic regions. Comparing consecutive entries in a BAM or CRAM index allows one to infer the number of alignment records per genomic region for use as an effective proxy of sequence depth in each genomic region. Based on these properties, we have developed indexcov, an efficient estimator of whole-genome sequencing coverage to rapidly identify samples with aberrant coverage profiles, reveal large scale chromosomal anomalies, recognize potential batch effects, and infer the sex of a sample. Indexcov is available at: https://github.com/brentp/goleft under the MIT license.

genomics↗

bioRxiv2017

Nanopore sequencing and assembly of a human genome with ultra-long reads

Nanopore sequencing is a promising technique for genome sequencing due to its portability, ability to sequence long reads from single molecules, and to simultaneously assay DNA methylation. However until recently nanopore sequencing has been mainly applied to small genomes, due to the limited output attainable. We present nanopore sequencing and assembly of the GM12878 Utah/Ceph human reference genome generated using the Oxford Nanopore MinION and R9.4 version chemistry. We generated 91.2 Gb of sequence data ([~]30x theoretical coverage) from 39 flowcells. De novo assembly yielded a highly complete and contiguous assembly (NG50 [~]3Mb). We observed considerable variability in homopolymeric tract resolution between different basecallers. The data permitted sensitive detection of both large structural variants and epigenetic modifications. Further we developed a new approach exploiting the long-read capability of this system and found that adding an additional 5x-coverage of ultra-long reads (read N50 of 99.7kb) more than doubled the assembly contiguity. Modelling the repeat structure of the human genome predicts extraordinarily contiguous assemblies may be possible using nanopore reads alone. Portable de novo sequencing of human genomes may be important for rapid point-of-care diagnosis of rare genetic diseases and cancer, and monitoring of cancer progression. The complete dataset including raw signal is available as an Amazon Web Services Open Dataset at: https://github.com/nanopore-wgs-consortium/NA12878.

genomics↗