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 evaluation of error rates and causes in short samples
Machine learning guided signal enrichment for ultrasensitive
Machine learning guided signal enrichment for ultrasensitive
The challenge of somatic variant detection accuracy in liquid
PDF) DREAMS: deep read-level error model for sequencing data
Integrated digital error suppression for improved detection of
LFMD: detecting low-frequency mutations in high-depth genome
General illustration of our approach. (a) Distribution of observed
Integration of intra-sample contextual error modeling for improved
Applications and analysis of targeted genomic sequencing in cancer
PDF) DREAMS: deep read-level error model for sequencing data
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