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.
Accurate detection of circulating tumor DNA using nanopore
Systematic comparative analysis of single-nucleotide variant
Circulating tumor DNA and liquid biopsy in oncology
Whole genome error-corrected sequencing for sensitive circulating
The changing face of circulating tumor DNA (ctDNA) profiling
Multimodal analysis of cell-free DNA whole-genome sequencing for
Systematic evaluation of error rates and causes in short samples
Types of errors. A screenshot from the IGV browser [21] showing
PDF) DREAMS: Deep Read-level Error Model for Sequencing data
Genome-wide cell-free DNA mutational integration enables ultra
de
por adulto (o preço varia de acordo com o tamanho do grupo)
