DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology

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 generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.

DREAMS: deep read-level error model for sequencing data applied to

Circulating tumor DNA and liquid biopsy in oncology

Systematic evaluation of error rates and causes in short samples

Novel hybridization- and tag-based error-corrected method for

Analysis of error profiles in deep next-generation sequencing data

Machine learning guided signal enrichment for ultrasensitive

DREAMS: Deep Read-level Error Model for Sequencing data applied to

DREAMS: Deep Read-level Error Model for Sequencing data applied to

DREAMS: Deep Read-level Error Model for Sequencing data applied to