DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology
Por um escritor misterioso
Last updated 12 abril 2025

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.

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Frontiers Benchmarking Low-Frequency Variant Calling With Long

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

Detecting and Quantitating Low Fraction DNA Variants with Low

Whole genome deep sequencing analysis of cell-free DNA in samples

Genome-wide cell-free DNA mutational integration enables ultra

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

The changing face of circulating tumor DNA (ctDNA) profiling

Applications and analysis of targeted genomic sequencing in cancer

Cancers, Free Full-Text

Discovering the drivers of clonal hematopoiesis

Whole genome error-corrected sequencing for sensitive circulating
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