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.

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

Bioinformatic strategies for the analysis of genomic aberrations

Systematic evaluation of error rates and causes in short samples

Evaluating the performance of low-frequency variant calling tools

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

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

Machine learning-based genome-wide interrogation of somatic copy

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Analysis of error profiles in deep next-generation sequencing data