PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation

Sherman, Maxwell A.; Barton, Alison R.; Lodato, Michael A.; Vitzthum, Carl; Coulter, Michael; Walsh, Christopher A.; Park, Peter J.

doi:10.1093/nar/gkx1195

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…Phasing was performed on the GATK HaplotypeCaller germline variants from replicate 6 bulk brain tissue using Eagle [ 49 ] and the 1000 Genomes Phase 3 reference panel [ 50 ]. Second, we determined the optimal kernel size to maximize accuracy using PaSD-qc [ 51 ], which was determined to be 10 kb. Third, we instituted a likelihood ratio test to distinguish a first-round amplification artifact from a true mosaic variant; let f ( X ; p ) be the probability of observing reads X where p is the expected proportion of alternate reads.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive identification of somatic nucleotide variants in human brain tissue

Wang¹,

Bae²,

Thorpe³

et al. 2021

Genome Biol

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Background Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. Results Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. Conclusions This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

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Section: Methodsmentioning

confidence: 99%

Comprehensive identification of somatic nucleotide variants in human brain tissue

Wang¹,

Bae²,

Thorpe³

et al. 2021

Genome Biol

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show abstract

“…Information on haplotype phase will also be useful for calling retrotranspon insertions ( Evrony et al , 2012 ) or single-nucleotide variants (SNVs). Recently, ( Bohrson et al , 2018 ) showed how they were able to reduce false positive rates in SNV calling in single-cell DNA sequencing by phasing SNVs to nearby SNPs. However, many SNVs cannot be phased to SNPs with short reads, especially in whole-exome data.…”

Section: Discussionmentioning

confidence: 99%

“…Since each read generally derives from a single chromosome, if a read spans multiple SNPs, then the observed alleles are presumed from a single haplotype. Haplotype and SNP phase information has applications in population genetics ( Tewhey et al , 2011 ) and clinical and medical genomics ( Glusman et al , 2014 ; Roach et al , 2010 ; van de Ven et al , 2012 ) as well as being used to improve other analyses such as SNP imputation and genotyping ( Browning and Yu, 2009 ; Marchini et al , 2007 ) and somatic variant calling ( Bohrson et al , 2018 ). Obtaining long continuous haplotype blocks is a challenge as the distance between adjacent SNPs is longer than reads and read fragments in most sequencing technologies.…”

Section: Introductionmentioning

confidence: 99%

Haplotype phasing in single-cell DNA-sequencing data

Satas

Raphael

2018

Bioinformatics

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MotivationCurrent technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants.ResultsWe show that amplification biases have a potential upside: long-range correlations in rates of allele dropout provide a signal for phasing haplotypes at the lengths of amplicons from WGA, lengths which are generally longer than than individual sequence reads. We describe a statistical test to measure concurrent allele dropout between single-nucleotide polymorphisms (SNPs) across multiple sequenced single cells. We use results of this test to perform haplotype assembly across a collection of single cells. We demonstrate that the algorithm predicts phasing between pairs of SNPs with higher accuracy than phasing from reads alone. Using whole-genome sequencing data from only seven neural cells, we obtain haplotype blocks that are orders of magnitude longer than with sequence reads alone (median length 10.2 kb versus 312 bp), with error rates <2%. We demonstrate similar advantages on whole-exome data from 16 cells, where we obtain haplotype blocks with median length 9.2 kb—comparable to typical gene lengths—compared with median lengths of 41 bp with sequence reads alone, with error rates <4%. Our algorithm will be useful for haplotyping of rare alleles and studies of allele-specific somatic aberrations.Availability and implementationSource code is available at https://www.github.com/raphael-group.Supplementary information Supplementary data are available at Bioinformatics online.

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“…Phasing was performed on the GATK HaplotypeCaller germline variants from Replicate 6 bulk brain tissue using Eagle 46 and the 1000 Genomes Phase 3 reference panel 47 . Second, we determined the optimal kernel size to maximize accuracy using PaSD-qc 48 , which was determined to be 10 kb. Third, we instituted a likelihood ratio test to distinguish a first-round amplification artefact from a true mosaic variant; let f(X;p ) be the probability of observing reads X where p is the expected proportion of alternate reads.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive identification of somatic nucleotide variants in human brain tissue

Wang

Bae

Thorpe

et al. 2020

Preprint

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Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. Here, the Brain Somatic Mosaicism Network conducted a coordinated, multi-institutional study to: (i) examine the ability of existing methods to detect simulated somatic single nucleotide variants (SNVs) in DNA mixing experiments; (ii) generate multiple replicates of whole genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual; (iii) devise strategies to discover somatic SNVs; and (iv) apply various approaches to validate somatic SNVs. These efforts led to the identification of 43 bona fide somatic SNVs that ranged in variant allele fractions from ~0.005 to ~0.28. Guided by these results, we devised best practices for calling mosaic SNVs from 250X whole genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrated that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. Thus, this study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

show abstract

PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation

Cited by 13 publications

References 29 publications

Comprehensive identification of somatic nucleotide variants in human brain tissue

Comprehensive identification of somatic nucleotide variants in human brain tissue

Haplotype phasing in single-cell DNA-sequencing data

Comprehensive identification of somatic nucleotide variants in human brain tissue

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