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.
NGS-Based Tumor-Informed Analysis of Circulating Tumor DNA
DREAMS: deep read-level error model for sequencing data applied to
Calibration-free NGS quantitation of mutations below 0.01% VAF
A machine learning approach for somatic mutation discovery
Systematic evaluation of error rates and causes in short samples
DREAMS: Deep Read-level Error Model for Sequencing data applied to
Potential error sources in next-generation sequencing workflow. a
Machine learning guided signal enrichment for ultrasensitive
LFMD: detecting low-frequency mutations in high-depth genome
DREAMS: deep read-level error model for sequencing data applied to
Phasing analysis of lung cancer genomes using a long read
Cell-free DNA approaches for cancer early detection and
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