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.

Integration of intra-sample contextual error modeling for improved

Discovering the drivers of clonal hematopoiesis

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

Whole genome error-corrected sequencing for sensitive circulating

Ultra-deep multi-oncopanel sequencing of benchmarking samples with

Calibration-free NGS quantitation of mutations below 0.01% VAF

Evaluating the performance of low-frequency variant calling tools

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

Integration of intra-sample contextual error modeling for improved

Whole genome error-corrected sequencing for sensitive circulating

Frontiers Multimodal analysis of genome-wide methylation, copy