DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection, Genome Biology
Descrição
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.
Systematic comparative analysis of single-nucleotide variant
Somatic small-variant calling methods in Illumina DRAGEN
PDF) DREAMS: deep read-level error model for sequencing data
DREAMS: deep read-level error model for sequencing data applied to
DREAMS: Deep Read-level Error Model for Sequencing data applied to
AIVariant: a deep learning-based somatic variant detector for
Machine learning guided signal enrichment for ultrasensitive
Ultra-deep sequencing data from a liquid biopsy proficiency study
Systematic evaluation of error rates and causes in short samples
Genes, Free Full-Text
Frontiers Benchmarking Low-Frequency Variant Calling With Long
Types of errors. A screenshot from the IGV browser [21] showing
Integration of intra-sample contextual error modeling for improved
Analysis of error profiles in deep next-generation sequencing data
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