Systematic comparison of variant calling pipelines using gold standard personal exome variants

Hwang, Sohyun; Kim, Eiru; Lee, Insuk; Marcotte, Edward M.

doi:10.1038/srep17875

Cited by 296 publications

(261 citation statements)

References 25 publications

Supporting

Mentioning

244

Contrasting

Order By: Relevance

“…Previous reports also showed the reduced sensitivity of Bowtie2 [35,36] while others revealed only minor [38] or inconclusive [40] differences between Bowtie2 and BWA. Interestingly, Li attributes the differences between variants called from BWA and Bowtie2 alignments to lower mapping scores that Bowtie2 assigns to reads with additional suboptimal alignments [36].…”

Section: Discussionmentioning

confidence: 95%

Positional bias in variant calls against draft reference assemblies

Briskine¹,

Shimizu²

2017

BMC Genomics

View full text Add to dashboard Cite

Background: Whole genome resequencing projects may implement variant calling using draft reference genomes assembled de novo from short-read libraries. Despite lower quality of such assemblies, they allowed researchers to extend a wide range of population genetic and genome-wide association analyses to non-model species. As the variant calling pipelines are complex and involve many software packages, it is important to understand inherent biases and limitations at each step of the analysis. Results: In this article, we report a positional bias present in variant calling performed against draft reference assemblies constructed from de Bruijn or string overlap graphs. We assessed how frequently variants appeared at each position counted from ends of a contig or scaffold sequence, and discovered unexpectedly high number of variants at the positions related to the length of either k-mers or reads used for the assembly. We detected the bias in both publicly available draft assemblies from Assemblathon 2 competition as well as in the assemblies we generated from our simulated short-read data. Simulations confirmed that the bias causing variants are predominantly false positives induced by reads from spatially distant repeated sequences. The bias is particularly strong in contig assemblies. Scaffolding does not eliminate the bias but tends to mitigate it because of the changes in variants' relative positions and alterations in read alignments. The bias can be effectively reduced by filtering out the variants that reside in repetitive elements. Conclusions: Draft genome sequences generated by several popular assemblers appear to be susceptible to the positional bias potentially affecting many resequencing projects in non-model species. The bias is inherent to the assembly algorithms and arises from their particular handling of repeated sequences. It is recommended to reduce the bias by filtering especially if higher-quality genome assembly cannot be achieved. Our findings can help other researchers to improve the quality of their variant data sets and reduce artefactual findings in downstream analyses.

show abstract

Section: Discussionmentioning

confidence: 95%

Positional bias in variant calls against draft reference assemblies

Briskine¹,

Shimizu²

2017

BMC Genomics

View full text Add to dashboard Cite

show abstract

“…For example, a recent study showed that one pipeline produces more than 4-fold more single-nucleotide variants (SNVs) from the same dataset than other pipelines (1). Published studies compared different pipelines that employ open source and commercial software, and most of them analyze NGS data generated by using Illumina's platform (1,15,39). There is no publicly available datasets applicable to WES of clinical samples of cancer tissues collected at single institute, which are sequenced at sufficient depths using the Ion Proton System.…”

Section: Identification Of Somatic Mutations From Wes Datamentioning

confidence: 99%

<b>Optimizing an ion semiconductor sequencing data analysis method to identify somatic mutations in the genomes of cancer cells in clinical tissue </b><b>samples </b>

et al. 2016

View full text Add to dashboard Cite

Identification of causal genomic alterations is an indispensable step in the implementation of personalized cancer medicine. Analytical methods play a central role in identifying such changes because of the vast amount of data produced by next generation sequencer. Most analytical techniques are designed for the Illumina platform and are therefore suboptimal for analyzing datasets generated by whole exome sequencing (WES) using the Ion Proton System. Accurate identification of somatic mutations requires the characterization of platform-dependent error profiles and genomic properties that affect the accuracy of sequence data as well as platform-oriented optimization of the pipeline. Therefore, we used the Ion Proton System to perform WES of DNAs isolated from tumor and matched control tissues of 1,058 patients with cancer who were treated at the Shizuoka Cancer Center Hospital. Among the initially identified candidate somatic single-nucleotide variants (SNVs), 10,279 were validated by manual inspection of the WES data followed by Sanger sequencing. These validated SNVs were used as an objective standard to determine an optimum cutoff value to improve the pipeline. Using this optimized pipeline analysis, 189,381 SNVs were identified in 1,101 samples. The analytical technique presented here is a useful resource for conducting clinical WES, particularly using semiconductor-based sequencing technology.

show abstract

“…We obtained 0. 27,28 , GATK remains the most adopted variant caller for Illumina data, while the Torrent Variant Caller (TVC) is almost the only one adopted for ION data. Both approaches produce a discrete percentage of false positive calls, as we observed in our datasets as well (Supplementary Table S2).…”

Section: Features Importancementioning

confidence: 99%

GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS

Ravasio

Ritelli

Legati

et al. 2017

Preprint

View full text Add to dashboard Cite

Exome sequencing approach is extensively used in research and diagnostic laboratories to discover pathological variants and study genetic architecture of human diseases. Even if present platforms produce high quality sequencing data, false positives variants remain an issue and can confound subsequent analysis and result interpretation. Our tool outperformed previous hard-filters, and calculates for each variant a score from 0.0 to 1.0, allowing application of different thresholds based on desired level of sensitivity and specificity.

show abstract

Systematic comparison of variant calling pipelines using gold standard personal exome variants

Cited by 296 publications

References 25 publications

Positional bias in variant calls against draft reference assemblies

Positional bias in variant calls against draft reference assemblies

<b>Optimizing an ion semiconductor sequencing data analysis method to identify somatic mutations in the genomes of cancer cells in clinical tissue </b><b>samples </b>

GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS

Contact Info

Product

Resources

About