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
Last updated 03 junho 2024
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 errors in HiSeq data. A screenshot from the IGV browser
Systematic evaluation of error rates and causes in short samples
Integration of intra-sample contextual error modeling for improved
NGS-Based Tumor-Informed Analysis of Circulating Tumor DNA
DREAMS: Deep Read-level Error Model for Sequencing data applied to
LFMD: detecting low-frequency mutations in high-depth genome
Frontiers Standardization of Sequencing Coverage Depth in NGS
Phasing analysis of lung cancer genomes using a long read
Detecting and Quantitating Low Fraction DNA Variants with Low
Targeted error-suppressed quantification of circulating tumor DNA
AIVariant: a deep learning-based somatic variant detector for
Accurate detection of circulating tumor DNA using nanopore
Analysis of error profiles in deep next-generation sequencing data
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