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.
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Machine learning guided signal enrichment for ultrasensitive
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Deep whole-genome ctDNA chronology of treatment-resistant prostate
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Applications and analysis of targeted genomic sequencing in cancer
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
AIVariant: a deep learning-based somatic variant detector for
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Machine learning guided signal enrichment for ultrasensitive
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Whole genome error-corrected sequencing for sensitive circulating
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Analytical and Clinical Validation of an Amplicon-based Next
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Systematic errors in HiSeq data. A screenshot from the IGV browser
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
DREAMS: deep read-level error model for sequencing data applied to
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Systematic evaluation of error rates and causes in short samples
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
DREAMS: deep read-level error model for sequencing data applied to
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Calibration-free NGS quantitation of mutations below 0.01% VAF
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Variant calling in control data. (a) Power (true-positive rate) of
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Machine learning guided signal enrichment for ultrasensitive
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
A review of deep learning applications in human genomics using
de por adulto (o preço varia de acordo com o tamanho do grupo)