Benchmarking challenging small variants with linked and long reads

Wagner, Justin; Olson, Nathan D.; Harris, Lindsay; Khan, Ziad; Farek, Jesse; Mahmoud, Medhat; Stanković, Ana K.; Kovačević, Vladimir; Yoo, Byunggil; Miller, Neil; Rosenfeld, Jeffrey; Ni, Bohan; Zarate, Samantha; Kirsche, Melanie; Aganezov, Sergey; Schatz, Michael C.; Narzisi, Giuseppe; Byrska-Bishop, Marta; Clarke, Wayne E.; Evani, Uday S.; Markello, Charles; Shafin, Kishwar; Zhou, Xin; Sidow, Arend; Bansal, Vikas; Ebert, Peter; Marschall, Tobias; Lansdorp, Peter M.; Hanlon, Vincent C.T.; Mattsson, Carl-Adam; Martinez-Barrio, Alvaro; Fiddes, Ian T.; Xiao, Chunlin; Fungtammasan, Arkarachai; Chin, Chen-Shan; Wenger, Aaron M.; Rowell, William J.; Sedlazeck, Fritz J.; Carroll, Andrew; Salit, Marc; Zook, Justin M.

doi:10.1016/j.xgen.2022.100128

Cited by 111 publications

(106 citation statements)

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“…Most existing reference imputation panels, such as the HRC (The Haplotype Reference Consortium, 2016) and TOPMed (Taliun et al, 2021), do not yet include SVs due to challenges that SV calling and phasing present. Also, evaluation of SV phasing accuracy has been difficult so far due to unavailability of well-established haplotype-resolved SV truth sets, similar to Platinum Genomes (Eberle et al, 2017) or the GIAB (Wagner et al, 2022;Zook et al, 2019), that exist for SNVs/INDELs. The recently published SV call set from the HGSVC (Ebert et al, 2021) allowed us to circumvent the latter issue and provided a much needed reference for evaluation of SV phasing accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…This is consistent with an independent estimation based on NA12878 from the jointly genotyped set of just the 2,504 unrelated samples where FDR is 0.98% (see STAR Methods; Figure S1F). Additionally, we evaluated singletons against the recently released GIAB truth set v4.2.1 (Wagner et al, 2022). Thanks to inclusion of additional technologies such as PacBio-HiFi and 10X Genomics, the GIAB v4.2.1 truth set excludes some of the calls believed to be mosaic variants that arose due to cell-line propagation that were present in the GIAB v3.3.2 truth set.…”

Section: Ll Open Accessmentioning

confidence: 99%

“…To ensure that this approach for FDR estimation is not biased due to inclusion of NA12878's parents in joint genotyping, we also computed FDR among singletons in NA12878 from an independent jointly genotyped high-coverage call set consisting of just the original 2,504 1kGP unrelated samples. Both of the FDR singleton analyses were restricted to the high confidence regions of the genome, as defined by either the GIAB v3.3.2 (Zook et al, 2019) or GIAB v4.2.1 (Wagner et al, 2022) truth sets.…”

Section: Snv/indel Discovery Using Gatkmentioning

confidence: 99%

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High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

Byrska-Bishop

Evani

Zhao

et al. 2022

Cell

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Section: Discussionmentioning

confidence: 99%

Section: Ll Open Accessmentioning

confidence: 99%

Section: Snv/indel Discovery Using Gatkmentioning

confidence: 99%

See 1 more Smart Citation

High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

Byrska-Bishop

Evani

Zhao

et al. 2022

Cell

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513

501

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“…Although VarSCAT can annotate variants that only partially overlap with an STR, we focused on highconfidence individual human small variant sets for analysis. The high-confidence small variant sets had high precision and covered much of the human genome, but they still excluded some difficult regions that were enriched with STRs and limited the sizes of variants due to difficulties in variant genotyping [47][48][49].…”

Section: Discussionmentioning

confidence: 99%

“…To assess our newly developed tool, we used variants based on human reference assembly GRCh38 from the ClinVar database [46], Platinum Genome [47], the National Institute of Standards and Technology's Genome in a Bottle (GIAB) [48,49], and the 1000 Genomes Project [50,51] to study the breakpoint ambiguities of indels and small variants (1-50 bp) in STR regions.…”

Section: An Overview Of Varscatmentioning

confidence: 99%

VarSCAT: A computational tool for sequence context annotations of genomic variants

Wang

Khan

Elo

2022

Preprint

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The sequence contexts of genomic variants play important roles in understanding biological significances of variants and potential sequencing related variant calling issues. However, methods for assessing the diverse sequence contexts of genomic variants such as tandem repeats and unambiguous annotations have been limited. Herein, we describe the Variant Sequence Context Annotation Tool (VarSCAT) for annotating the sequence contexts of genomic variants, including breakpoint ambiguities, flanking sequences, variant nomenclatures, adjacent variants, and tandem repeats with user customizable options. Our analysis demonstrate that VarSCAT is more versatile and customizable than current methods or strategies for annotating variants in short tandem repeat (STR) regions. Variant sequence context annotations of high-confidence human variant sets with VarSCAT revealed that more than 75% of all human individual germline and clinically relevant insertions and deletions (indels) have breakpoint ambiguities. Moreover, we illustrate that more than 80% of human individual germline small variants in STR regions are indels and that the sizes of these indels correlated with STR motif sizes. VarSCAT is available athttps://github.com/elolab/VarSCAT.Author SummaryThe sequence contexts have significant impacts on the biological and technical aspects of genomic variants. The sequence contexts, such as tandem repeats or nearby indels, may increase the mutation rate of a region compared to other genome regions. Besides, variants in specific sequence contexts like STRs may also have distinguished biological consequences, which can lead to certain human diseases and thus they may be used as biomarkers for disease diagnosis and treatments. Moreover, potential ambiguous variant representations such as equivalent or redundant indels are also related with their sequence contexts, which may complicate variant harmonization from different sources. Our previous study demonstrated that more than half of false positive indel calls detected through next generation sequencing data are related with STRs. Thus, the sequence contexts of genomic variants are important and cannot be ignored. However, the current methods or strategies for assessing the sequence contexts of genomic variants are limited and not feasible to use. Here, we developed a computational tool VarSCAT for sequence contexts annotation of genomic variants. Our tool provides diverse sequence contexts annotations providing users information to further explore the variants of their interests. By applying VarSCAT to high confidence human variant sets, we demonstrate the influence of sequence context of genomic variants and emphasize the importance of sequence context assessment.

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Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution

Spealman

Chuong

et al. 2023

J Mol Evol

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Copy number variants (CNVs), comprising gene amplifications and deletions, are a pervasive class of heritable variation. CNVs play a key role in rapid adaptation in both natural, and experimental, evolution. However, despite the advent of new DNA sequencing technologies, detection and quantification of CNVs in heterogeneous populations has remained challenging. Here, we summarize recent advances in the use of CNV reporters that provide a facile means of quantifying de novo CNVs at a specific locus in the genome, and nanopore sequencing, for resolving the often complex structures of CNVs. We provide guidance for the engineering and analysis of CNV reporters and practical guidelines for single-cell analysis of CNVs using flow cytometry. We summarize recent advances in nanopore sequencing, discuss the utility of this technology, and provide guidance for the bioinformatic analysis of these data to define the molecular structure of CNVs. The combination of reporter systems for tracking and isolating CNV lineages and long-read DNA sequencing for characterizing CNV structures enables unprecedented resolution of the mechanisms by which CNVs are generated and their evolutionary dynamics.

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Benchmarking challenging small variants with linked and long reads

Cited by 111 publications

References 50 publications

High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

VarSCAT: A computational tool for sequence context annotations of genomic variants

Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution

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