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SeqSQC: A Bioconductor Package for Evaluating the Sample Quality of Next-generation Sequencing Data.


ABSTRACT: As next-generation sequencing (NGS) technology has become widely used to identify genetic causal variants for various diseases and traits, a number of packages for checking NGS data quality have sprung up in public domains. In addition to the quality of sequencing data, sample quality issues, such as gender mismatch, abnormal inbreeding coefficient, cryptic relatedness, and population outliers, can also have fundamental impact on downstream analysis. However, there is a lack of tools specialized in identifying problematic samples from NGS data, often due to the limitation of sample size and variant counts. We developed SeqSQC, a Bioconductor package, to automate and accelerate sample cleaning in NGS data of any scale. SeqSQC is designed for efficient data storage and access, and equipped with interactive plots for intuitive data visualization to expedite the identification of problematic samples. SeqSQC is available at http://bioconductor.org/packages/SeqSQC.

SUBMITTER: Liu Q 

PROVIDER: S-EPMC6620264 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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SeqSQC: A Bioconductor Package for Evaluating the Sample Quality of Next-generation Sequencing Data.

Liu Qian Q   Hu Qiang Q   Yao Song S   Kwan Marilyn L ML   Roh Janise M JM   Zhao Hua H   Ambrosone Christine B CB   Kushi Lawrence H LH   Liu Song S   Zhu Qianqian Q  

Genomics, proteomics & bioinformatics 20190405 2


As next-generation sequencing (NGS) technology has become widely used to identify genetic causal variants for various diseases and traits, a number of packages for checking NGS data quality have sprung up in public domains. In addition to the quality of sequencing data, sample quality issues, such as gender mismatch, abnormal inbreeding coefficient, cryptic relatedness, and population outliers, can also have fundamental impact on downstream analysis. However, there is a lack of tools specialized  ...[more]

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