Abstract:
:Noise in genomic sequencing data is known to have effects on various stages of genomic data analysis pipelines. Variant identification is an important step of many of these pipelines, and is increasingly being used in clinical settings to aid medical practices. We propose a denoising method, dubbed SAMDUDE, which operates on aligned genomic data in order to improve variant calling performance. Denoising human data with SAMDUDE resulted in improved variant identification in both individual chromosome as well as whole genome sequencing (WGS) data sets. In the WGS data set, denoising led to identification of almost 2,000 additional true variants, and elimination of over 1,500 erroneously identified variants. In contrast, we found that denoising with other state-of-the-art denoisers significantly worsens variant calling performance. SAMDUDE is written in Python and is freely available at https://github.com/ihwang/SAMDUDE .
journal_name
Sci Repjournal_title
Scientific reportsauthors
Fischer-Hwang I,Ochoa I,Weissman T,Hernaez Mdoi
10.1038/s41598-019-51418-zsubject
Has Abstractpub_date
2019-10-21 00:00:00pages
15067issue
1issn
2045-2322pii
10.1038/s41598-019-51418-zjournal_volume
9pub_type
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