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 24 março 2025
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
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
Sequence motifs at systematic error sites. (a) The motif around
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
Computational analysis of cancer genome sequencing data
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Genes, Free Full-Text
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Identification of 12 cancer types through genome deep learning
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Genes, Free Full-Text
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Genome-wide cell-free DNA mutational integration enables ultra
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Potential error sources in next-generation sequencing workflow. a
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Systematic comparative analysis of single-nucleotide variant
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Machine learning-based genome-wide interrogation of somatic copy
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Phasing analysis of lung cancer genomes using a long read
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
PDF) DREAMS: deep read-level error model for sequencing data
DREAMS: deep read-level error model for sequencing data applied to  low-frequency variant calling and circulating tumor DNA detection, Genome  Biology
Discovering the drivers of clonal hematopoiesis
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

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