Comparative analysis of whole-genome sequencing pipelines to minimize false negative findings

Hwang, Kyu-Baek; Lee, In-Hee; Li, Honglan; Won, Dhong-Geon; Hernández-Ferrer, Carles; Negron, Jose A.; Kong, Sek Won

doi:10.1038/s41598-019-39108-2

Cited by 74 publications

(69 citation statements)

References 46 publications

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“…Many underlying algorithms of variant calling pipelines were developed for the analysis of variants in the human genome, e.g., to investigate genetic disorders or to study tumor samples [20,[41][42][43][44]. Although the applications in biomedical research and plant sciences differ substantially, plant scientists have largely followed benchmarking studies derived from research on human samples assuming similar performances.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Read Mapping and Variant Calling Tools for the Analysis of Plant NGS Data

Schilbert

Rempel

Pucker

2020

Plants

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High-throughput sequencing technologies have rapidly developed during the past years and have become an essential tool in plant sciences. However, the analysis of genomic data remains challenging and relies mostly on the performance of automatic pipelines. Frequently applied pipelines involve the alignment of sequence reads against a reference sequence and the identification of sequence variants. Since most benchmarking studies of bioinformatics tools for this purpose have been conducted on human datasets, there is a lack of benchmarking studies in plant sciences. In this study, we evaluated the performance of 50 different variant calling pipelines, including five read mappers and ten variant callers, on six real plant datasets of the model organism Arabidopsis thaliana. Sets of variants were evaluated based on various parameters including sensitivity and specificity. We found that all investigated tools are suitable for analysis of NGS data in plant research. When looking at different performance metrics, BWA-MEM and Novoalign were the best mappers and GATK returned the best results in the variant calling step.

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Section: Introductionmentioning

confidence: 99%

Comparison of Read Mapping and Variant Calling Tools for the Analysis of Plant NGS Data

Schilbert

Rempel

Pucker

2020

Plants

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“…Overall, despite the enormous potential of WES and, mostly, WGS, data management, data analysis and biological interpretation are critical to achieve optimal results [37,38]. NGS sequencing produces millions/billions of massive sequencing reads; thus, a huge amount of raw data is generated, especially when high sequencing depth is required.…”

Section: Ngs Technologiesmentioning

confidence: 99%

Applications of Next Generation Sequencing to the Analysis of Familial Breast/Ovarian Cancer

et al. 2020

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Next generation sequencing (NGS) provides a powerful tool in the field of medical genetics, allowing one to perform multi-gene analysis and to sequence entire exomes (WES), transcriptomes or genomes (WGS). The generated high-throughput data are particularly suitable for enhancing the understanding of the genetic bases of complex, multi-gene diseases, such as cancer. Among the various types of tumors, those with a familial predisposition are of great interest for the isolation of novel genes or gene variants, detectable at the germline level and involved in cancer pathogenesis. The identification of novel genetic factors would have great translational value, helping clinicians in defining risk and prevention strategies. In this regard, it is known that the majority of breast/ovarian cases with familial predisposition, lacking variants in the highly penetrant BRCA1 and BRCA2 genes (non-BRCA), remains unexplained, although several less penetrant genes (e.g., ATM, PALB2) have been identified. In this scenario, NGS technologies offer a powerful tool for the discovery of novel factors involved in familial breast/ovarian cancer. In this review, we summarize and discuss the state of the art applications of NGS gene panels, WES and WGS in the context of familial breast/ovarian cancer.

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“…Hwang et al, compared both the European NA12878 and the African NA19240 samples from the 1000 Genomes Project. They found a pipeline consisting of BWA-MEM and subsequently the Genome Analysis ToolKit with the Haplotype Caller to be su cient to reliably detect variants in most regions, except for rare variants and di cult regions, as for example simple repeats [9].…”

Section: Introductionmentioning

confidence: 99%

Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines

Gerber

Weißbach

Sys³

et al. 2020

Preprint

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Background Next Generation Sequencing (NGS) is the fundament of various studies providing insights into questions from biology and medicine. Nevertheless, integrating data from different experimental backgrounds can introduce strong biases. In order to methodically investigate the magnitude of systematic errors, we performed a cross-sectional observational study on a genomic cohort of 99 subjects each sequenced via (i) Illumina HiSeq X, (ii) Illumina HiSeq and (iii) Complete Genomics. Consequently, we systematically analyzed the heterogeneity between the sequencing cohorts with respect to genomic annotation and common filter criteria like minimum allele frequency (MAF). Results The number of detected variants/variant classes per individual was highly dependent on the sequencing technology. We observed a statistically significant overrepresentation of variants uniquely called by a single platform which indicates potential systematic biases. These variants were enriched in low complexity genomic regions and simple repeats. Furthermore, estimates of allele frequency were highly discrepant for a subset of variants in pairwise comparisons between different sequencing platforms. Applying common filters – such as MAF 5% and HWE- greatly reduced the heterogeneity between cohorts but still left discrepancies of several thousand variants after filtering.Conclusion We provide empirical evidence of systematic heterogeneity in variant calls between alternative experimental and data analysis setups. Our results highlight the potential benefit of reprocessing genomic data with harmonized pipelines when integrating data from different studies.

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Comparative analysis of whole-genome sequencing pipelines to minimize false negative findings

Cited by 74 publications

References 46 publications

Comparison of Read Mapping and Variant Calling Tools for the Analysis of Plant NGS Data

Comparison of Read Mapping and Variant Calling Tools for the Analysis of Plant NGS Data

Applications of Next Generation Sequencing to the Analysis of Familial Breast/Ovarian Cancer

Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines

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