Samplot: A Platform for Structural Variant Visual Validation and Automated Filtering

Belyeu, Jonathan R; Chowdhury, Murad; Brown, Joseph; Pedersen, Brent S.; Cormier, Michael; Quinlan, Aaron R.; Layer, Ryan M.

doi:10.1101/2020.09.23.310110

Cited by 10 publications

(13 citation statements)

References 29 publications

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“…We then visually scrutinized the evidence in the parents and grandparents of each thirdgeneration CEPH sample with a dnSV call. We examined IGV and Samplot (Belyeu et al 2020) images that included the offspring sample, both parents, and both sets of grandparents and carefully examined each for any missed split read, discordant pair, or coverage depth signals indicating that the putative dnSV was actually a missed transmission event. This provided an extra opportunity to detect elusive inherited variants.…”

Section: Resultsmentioning

confidence: 99%

“…Given the lack of validated training set for CEPH and the much smaller sample size, only a simple filter of less than 30 for depth-based GQ and PE/SR GQ in parents was applied. All variants that passed these filters were manually reviewed using duphold (Pedersen and Quinlan 2019), IGV (Robinson et al 2011), SV-Plaudit (Belyeu et al 2018) with Samplot (Belyeu et al 2020), and an internal R based visualization script found in GATK-SV. In order to reduce the chance of missing a variant of interest, all private variants with a passing parental GQ are included in the manual investigation.…”

Section: Methodsmentioning

confidence: 99%

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De novostructural mutation rates and gamete-of-origin biases revealed through genome sequencing of 2,396 families

Belyeu

Brand

Wang

et al. 2020

Preprint

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Each human genome includes de novo mutations that arose during gametogenesis. While these germline mutations represent a fundamental source of new genetic diversity, they can also create deleterious alleles that impact fitness. The germline mutation rate for single nucleotide variants and factors that significantly influence this rate, such as parental age, are now well established. However, far less is known about the frequency, distribution, and features that impact de novo structural mutations. We report a large, family-based study of germline mutations, excluding aneuploidy, that affect genome structure among 572 genomes from 33 families in a multigenerational CEPH-Utah cohort and 2,363 cases of non-familial autism spectrum disorder (ASD), 1,938 unaffected siblings, and both parents (9,599 genomes in total). We find that de novo structural mutations detected by alignment-based, short-read WGS occurred at an overall rate of at least 0.160 events per genome in unaffected individuals and was significantly higher (0.206 per genome) in ASD cases. In both probands and unaffected samples, nearly 73% of de novo structural mutations arose in paternal gametes, and predict most de novo structural mutations to be caused by mutational mechanisms that do not require sequence homology. After multiple testing correction we did not observe a statistically significant correlation between parental age and the rate of de novo structural variation in offspring. These results highlight that a spectrum of mutational mechanisms contribute to germline structural mutations, and that these mechanisms likely have markedly different rates and selective pressures than those leading to point mutations.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

De novostructural mutation rates and gamete-of-origin biases revealed through genome sequencing of 2,396 families

Belyeu

Brand

Wang

et al. 2020

Preprint

Self Cite

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show abstract

“…Next, we exclude CNVs in telomeric regions, CNVs found in only one timepoint, and CNVs that are less than four windows (2 kb) long. Finally, we manually inspect our structural variant and CNV calls together using a modified version of Samplot ( Belyeu et al, 2020 ) to create a list of confirmed copy number variants in our populations ( Supplementary file 3 ). During this analysis, we noticed two regions with high copy-number that experienced copy-number changes in many populations: one associated with the CUP1 tandem array and one associated with the ribosomal DNA tandem array.…”

Section: Methodsmentioning

confidence: 99%

Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast populations

Johnson

Gopalakrishnan

Goyal

et al. 2021

eLife

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Laboratory experimental evolution provides a window into the details of the evolutionary process. To investigate the consequences of long-term adaptation, we evolved 205 Saccharomyces cerevisiae populations (124 haploid and 81 diploid) for ~10,000 generations in three environments. We measured the dynamics of fitness changes over time, finding repeatable patterns of declining adaptability. Sequencing revealed that this phenotypic adaptation is coupled with a steady accumulation of mutations, widespread genetic parallelism, and historical contingency. In contrast to long-term evolution in E. coli, we do not observe long-term coexistence or populations with highly elevated mutation rates. We find that evolution in diploid populations involves both fixation of heterozygous mutations and frequent loss-of-heterozygosity events. Together, these results help distinguish aspects of evolutionary dynamics that are likely to be general features of adaptation across many systems from those that are specific to individual organisms and environmental conditions.

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“…We filtered calls that overlapped the centromere, assembly gaps, or sequence coverage (PacBio CLR lra alignments) greater than twice the average coverage. Each resulting callset was manually curated using dot-plots of the genome assemblies or HiFi reads against the reference, and using samplot (Belyeu et al , 2020). Calls were classified as true positives if at least one haplotype or an exemplary HiFi read clearly showed the inversion or it was indicated from the samplot alignments, a duplication if the dot-plot signature was a fixed inverted duplication or inverted transposition, false-positive if both haplotypes did not show variation or an inverted duplication structure, and NA if it was not clear, commonly in pericentric regions.…”

Section: Resultsmentioning

confidence: 99%

lra: the Long Read Aligner for Sequences and Contigs

Ren

Chaisson

2020

Preprint

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MotivationIt is computationally challenging to detect variation by aligning long reads from single-molecule sequencing (SMS) instruments, or megabase-scale contigs from SMS assemblies. One approach to efficiently align long sequences is sparse dynamic programming (SDP), where exact matches are found between the sequence and the genome, and optimal chains of matches are found representing a rough alignment. Sequence variation is more accurately modeled when alignments are scored with a gap penalty that is a convex function of the gap length. Because previous implementations of SDP used a linear-cost gap function that does not accurately model variation, and implementations of alignment that have a convex gap penalty are either inefficient or use heuristics, we developed a method, lra, that uses SDP with a convex-cost gap penalty. We use lra to align long-read sequences from PacBio and Oxford Nanopore (ONT) instruments as well as de novo assembly contigs.ResultsAcross all data types, the runtime of lra is between 52-168% of the state of the art aligner minimap2 when generating SAM alignment, and 9-15% of an alternative method, ngmlr. This alignment approach may be used to provide additional evidence of SV calls in PacBio datasets, and an increase in sensitivity and specificity on ONT data with current SV detection algorithms. The number of calls discovered using pbsv with lra alignments are within 98.3-98.6% of calls made from minimap2 alignments on the same data, and give a nominal 0.2-0.4% increase in F1 score by Truvari analysis. On ONT data with SV called using Sniffles, the number of calls made from lra alignments is 3% greater than minimap2-based calls, and 30% greater than ngmlr based calls, with a 4.6-5.5% increase in Truvari F1 score. When applied to calling variation from de novo assembly contigs, there is a 5.8% increase in SV calls compared to minimap2+paftools, with a 4.3% increase in Truvari F1 score.Availability and implementationAvailable in bioconda: https://anaconda.org/bioconda/lra and github: https://github.com/ChaissonLab/LRAContactmchaisso@usc.edu, jingwenr@usc.edu

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Samplot: A Platform for Structural Variant Visual Validation and Automated Filtering

Cited by 10 publications

References 29 publications

De novostructural mutation rates and gamete-of-origin biases revealed through genome sequencing of 2,396 families

De novostructural mutation rates and gamete-of-origin biases revealed through genome sequencing of 2,396 families

Phenotypic and molecular evolution across 10,000 generations in laboratory budding yeast populations

lra: the Long Read Aligner for Sequences and Contigs

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