A statistical framework for detecting mislabeled and contaminated samples using shallow-depth sequence data.

ABSTRACT:

Background

Researchers typically sequence a given individual multiple times, either re-sequencing the same DNA sample (technical replication) or sequencing different DNA samples collected on the same individual (biological replication) or both. Before merging the data from these replicate sequence runs, it is important to verify that no errors, such as DNA contamination or mix-ups, occurred during the data collection pipeline. Methods to detect such errors exist but are often ad hoc, cannot handle missing data and several require phased data. Because they require some combination of genotype calling, imputation, and haplotype phasing, these methods are unsuitable for error detection in low- to moderate-depth sequence data where such tasks are difficult to perform accurately. Additi

SUBMITTER: Chan AW 

PROVIDER: S-EPMC6292093 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature