DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection.
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ABSTRACT:
SUBMITTER: Christensen MH
PROVIDER: S-EPMC10150536 | biostudies-literature | 2023 Apr
REPOSITORIES: biostudies-literature
Publications
DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection.
Christensen Mikkel H MH Drue Simon O SO Rasmussen Mads H MH Frydendahl Amanda A Lyskjær Iben I Demuth Christina C Nors Jesper J Gotschalck Kåre A KA Iversen Lene H LH Andersen Claus L CL Pedersen Jakob Skou JS
Genome biology 20230430 1
Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we g ...[more]
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