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

General illustration of our approach. (a) Distribution of observed

Analytical validation of an error-corrected ultra-sensitive ctDNA

Identification of 12 cancer types through genome deep learning

Systematic evaluation of error rates and causes in short samples

White blood cell and cell-free DNA analyses for detection of

PDF) DREAMS: deep read-level error model for sequencing data

The changing face of circulating tumor DNA (ctDNA) profiling

PDF) Error Characterization and Statistical Modeling Improves

Machine learning guided signal enrichment for ultrasensitive

Systematic evaluation of error rates and causes in short samples

Optimizing Cancer Genome Sequencing and Analysis - ScienceDirect