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

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.

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

Deep learning–based integration of genetics with registry data for

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

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

VeChat: correcting errors in long reads using variation graphs

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

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

DREAM flow chart. Models and experimental data are collected from

Dose imbalance of DYRK1A kinase causes systemic progeroid status

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

DeSP: a systematic DNA storage error simulation pipeline