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.

Phasing analysis of lung cancer genomes using a long read

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

Applications and analysis of targeted genomic sequencing in cancer

Systematic comparative analysis of single-nucleotide variant

LFMD: detecting low-frequency mutations in high-depth genome

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

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

The changing face of circulating tumor DNA (ctDNA) profiling

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

Whole genome error-corrected sequencing for sensitive circulating

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

Applications and analysis of targeted genomic sequencing in cancer

Potential error sources in next-generation sequencing workflow. a