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.

Multimodal analysis of cell-free DNA whole-genome sequencing for

The changing face of circulating tumor DNA (ctDNA) profiling

Systematic evaluation of error rates and causes in short samples

Calibration-free NGS quantitation of mutations below 0.01% VAF

Machine learning guided signal enrichment for ultrasensitive

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

PDF) DREAMS: Deep Read-level Error Model for Sequencing data

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

A review of deep learning applications in human genomics using

Applications and analysis of targeted genomic sequencing in cancer

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