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.
Machine learning-based genome-wide interrogation of somatic copy
Evaluating the performance of low-frequency variant calling tools
Systematic evaluation of error rates and causes in short samples
General illustration of our approach. (a) Distribution of observed
Evaluating the performance of low-frequency variant calling tools
PDF) The changing face of circulating tumor DNA (ctDNA) profiling
Applications and analysis of targeted genomic sequencing in cancer
Discovering the drivers of clonal hematopoiesis
Bioinformatic strategies for the analysis of genomic aberrations
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
Computational analysis of cancer genome sequencing data
The changing face of circulating tumor DNA (ctDNA) profiling
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