Abstract:
BACKGROUND:Inadvertent sample swaps are a real threat to data quality in any medium to large scale omics studies. While matches between samples from the same individual can in principle be identified from a few well characterized single nucleotide polymorphisms (SNPs), omics data types often only provide low to moderate coverage, thus requiring integration of evidence from a large number of SNPs to determine if two samples derive from the same individual or not. METHODS:We select about six thousand SNPs in the human genome and develop a Bayesian framework that is able to robustly identify sample matches between next generation sequencing data sets. RESULTS:We validate our approach on a variety of data sets. Most importantly, we show that our approach can establish identity between different omics data types such as Exome, RNA-Seq, and MethylCap-Seq. We demonstrate how identity detection degrades with sample quality and read coverage, but show that twenty million reads of a fairly low quality RNA-Seq sample are still sufficient for reliable sample identification. CONCLUSION:Our tool, SMASH, is able to identify sample mismatches in next generation sequencing data sets between different sequencing modalities and for low quality sequencing data.
journal_name
BMC Genomicsjournal_title
BMC genomicsauthors
Westphal M,Frankhouser D,Sonzone C,Shields PG,Yan P,Bundschuh Rdoi
10.1186/s12864-019-6332-7subject
Has Abstractpub_date
2019-12-30 00:00:00pages
1001issue
Suppl 12issn
1471-2164pii
10.1186/s12864-019-6332-7journal_volume
20pub_type
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