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.
White blood cell and cell-free DNA analyses for detection of
Systematic evaluation of error rates and causes in short samples
Genes, Free Full-Text
DREAMS: Deep Read-level Error Model for Sequencing data applied to
Machine learning guided signal enrichment for ultrasensitive
Whole genome error-corrected sequencing for sensitive circulating
Integration of intra-sample contextual error modeling for improved
Frontiers Benchmarking Low-Frequency Variant Calling With Long
Evaluating the performance of low-frequency variant calling tools
Applications and analysis of targeted genomic sequencing in cancer
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