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.

Applications and analysis of targeted genomic sequencing in cancer

PDF) Error Characterization and Statistical Modeling Improves

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

Discovery of clonal hematopoiesis driver genes a Summary of the

Cancers, Free Full-Text

Genome-wide cell-free DNA mutational integration enables ultra

Deep whole-genome ctDNA chronology of treatment-resistant prostate

Evaluating the performance of low-frequency variant calling tools

Bioinformatic strategies for the analysis of genomic aberrations

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