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.

Computational analysis of cancer genome sequencing data

LFMD: detecting low-frequency mutations in high-depth genome

PDF) The changing face of circulating tumor DNA (ctDNA) profiling

Whole genome error-corrected sequencing for sensitive circulating

Frontiers Standardization of Sequencing Coverage Depth in NGS

Machine learning guided signal enrichment for ultrasensitive

Bioinformatic strategies for the analysis of genomic aberrations

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

Machine learning guided signal enrichment for ultrasensitive

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Integration of intra-sample contextual error modeling for improved

Systematic evaluation of error rates and causes in short samples

Cancers, Free Full-Text

Cancers, Free Full-Text