Identifying and mitigating batch effects in whole genome sequencing data.

ABSTRACT:

Background

Large sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.

Results

We describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identi

SUBMITTER: Tom JA 

PROVIDER: S-EPMC5525370 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature