RDscan: A New Method for Improving Germline and Somatic Variant Calling Based on Read Depth Distribution.

Sunho Lee^1,2, Seokchol Hong¹, Jonathan Woo¹, Jae-Hak Lee¹, Kyunghee Kim¹, Lucia Kim³, Kunsoo Park², Jongsun Jung¹

¹Genome Data Integration Centre, Syntekabio, Inc., Daejeon, Republic of Korea.

²Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.

³Department of Pathology, Inha University Hospital, Incheon, Republic of Korea.

PubMed DOI

Abstract

Several tools have been developed for calling variants from next-generation sequencing (NGS) data. Although they are generally accurate and reliable, most of them have room for improvement, especially regarding calling variants in datasets with low read depth. In addition, the somatic variants predicted by several somatic variant callers tend to have very low concordance rates. In this study, we d…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.