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
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
Targeted error-suppressed quantification of circulating tumor DNA
Systematic comparative analysis of single-nucleotide variant
PDF) DREAMS: deep read-level error model for sequencing data
Evaluating the performance of low-frequency variant calling tools
DREAMS: deep read-level error model for sequencing data applied to
Applications and analysis of targeted genomic sequencing in cancer
PDF) The changing face of circulating tumor DNA (ctDNA) profiling
Potential error sources in next-generation sequencing workflow. a
Deep whole-genome ctDNA chronology of treatment-resistant prostate
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