Reducing INDEL calling errors in whole-genome and exome sequencing data

Han, Fang; Wu, Yiyang; Narzisi, Giuseppe; O'Rawe, Jason; Barrón, Laura T Jimenez; Rosenbaum, Julie; Ronemus, Michael; Iossifov, Ivan; Schatz, Michael C.; Lyon, Gholson J.

doi:10.1101/006148

Cited by 26 publications

(30 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future studies targeting the analysis of insertion/deletion (indel) polymorphisms may also be useful for identifying additional targets of selection. Indel polymorphisms were omitted from our analyses because their accurate genotyping remains quite challenging, especially without high coverage (≥60× 58 ) and in non-model species with incomplete, or draft genome assemblies. With regards to domestication selection, a specific, universal set of domestication genes may not exist, but we do speculate that there may be a 'core' set of genes shared across multiple domestication processes.…”

Section: Discussionmentioning

confidence: 99%

Genomic signatures of domestication in Old World camels

et al. 2020

View full text Add to dashboard Cite

Domestication begins with the selection of animals showing less fear of humans. In most domesticates, selection signals for tameness have been superimposed by intensive breeding for economical or other desirable traits. Old World camels, conversely, have maintained high genetic variation and lack secondary bottlenecks associated with breed development. By resequencing multiple genomes from dromedaries, Bactrian camels, and their endangered wild relatives, here we show that positive selection for candidate genes underlying traits collectively referred to as 'domestication syndrome' is consistent with neural crest deficiencies and altered thyroid hormone-based signaling. Comparing our results with other domestic species, we postulate that the core set of domestication genes is considerably smaller than the pandomestication setand overlapping genes are likely a result of chance and redundancy. These results, along with the extensive genomic resources provided, are an important contribution to understanding the evolutionary history of camels and the genomic features of their domestication.

show abstract

Section: Discussionmentioning

confidence: 99%

Genomic signatures of domestication in Old World camels

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Whereas TPs in the reference call-set are likely to be TPs given that they are called by multiple orthogonal technologies and pipelines, using the inverse of this set to confidently identify areas of the genome that are truly non-variant may not be justified. Recent evidence has shown that alignment-based [ 42 ] and some assembly-based [ 43 ] variant-callers show high error rates for large InDels and heterozygous InDels even at WGS coverage depths up to 90×. Although higher coverage (190×) WGS datasets contribute calls to the GiBv2.18 reference, the majority of datasets are <80×.…”

Section: Discussionmentioning

confidence: 99%

Achieving high-sensitivity for clinical applications using augmented exome sequencing

Patwardhan¹,

Harris²,

Li³

et al. 2015

Genome Med

View full text Add to dashboard Cite

BackgroundWhole exome sequencing is increasingly used for the clinical evaluation of genetic disease, yet the variation of coverage and sensitivity over medically relevant parts of the genome remains poorly understood. Several sequencing-based assays continue to provide coverage that is inadequate for clinical assessment.MethodsUsing sequence data obtained from the NA12878 reference sample and pre-defined lists of medically-relevant protein-coding and noncoding sequences, we compared the breadth and depth of coverage obtained among four commercial exome capture platforms and whole genome sequencing. In addition, we evaluated the performance of an augmented exome strategy, ACE, that extends coverage in medically relevant regions and enhances coverage in areas that are challenging to sequence. Leveraging reference call-sets, we also examined the effects of improved coverage on variant detection sensitivity.ResultsWe observed coverage shortfalls with each of the conventional exome-capture and whole-genome platforms across several medically interpretable genes. These gaps included areas of the genome required for reporting recently established secondary findings (ACMG) and known disease-associated loci. The augmented exome strategy recovered many of these gaps, resulting in improved coverage in these areas. At clinically-relevant coverage levels (100 % bases covered at ≥20×), ACE improved coverage among genes in the medically interpretable genome (>90 % covered relative to 10-78 % with other platforms), the set of ACMG secondary finding genes (91 % covered relative to 4-75 % with other platforms) and a subset of variants known to be associated with human disease (99 % covered relative to 52-95 % with other platforms). Improved coverage translated into improvements in sensitivity, with ACE variant detection sensitivities (>97.5 % SNVs, >92.5 % InDels) exceeding that observed with conventional whole-exome and whole-genome platforms.ConclusionsClinicians should consider analytical performance when making clinical assessments, given that even a few missed variants can lead to reporting false negative results. An augmented exome strategy provides a level of coverage not achievable with other platforms, thus addressing concerns regarding the lack of sensitivity in clinically important regions. In clinical applications where comprehensive coverage of medically interpretable areas of the genome requires higher localized sequencing depth, an augmented exome approach offers both cost and performance advantages over other sequencing-based tests.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0197-4) contains supplementary material, which is available to authorized users.

show abstract

“…The read depth needed can depend on multiple factors including guidelines from the scientific community, the presence of repetitive genomic regions (these are more difficult to sequence), the error rate of the sequencing platform, the algorithm used for assembling reads into a genomic sequence, and gene expression level (for RNA-seq). Read depth recommendations from the scientific literature include 100x for heterozygous single nucleotide variant detection by WES [ 19 ], 35x for genotype detection by WGS [ 20 ], 60x for detecting insertions/deletions (INDELs) by WGS [ 21 ], 10–25 million reads for differential gene expression profiling by RNA-seq [ 22 ], and 50–100 million reads for allele-specific gene expression by RNA-seq [ 23 ].…”

Section: Genomic Data Generationmentioning

confidence: 99%

Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine

Carter

2016

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

show abstract

Reducing INDEL calling errors in whole-genome and exome sequencing data

Cited by 26 publications

References 51 publications

Genomic signatures of domestication in Old World camels

Genomic signatures of domestication in Old World camels

Achieving high-sensitivity for clinical applications using augmented exome sequencing

Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine

Contact Info

Product

Resources

About