DREAMS: deep read-level error model for sequencing data applied to

Por um escritor misterioso
Last updated 17 março 2025
DREAMS: deep read-level error model for sequencing data applied to
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
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DREAMS: deep read-level error model for sequencing data applied to
DREAMS: deep read-level error model for sequencing data applied to
DREAMS: deep read-level error model for sequencing data applied to
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DREAMS: deep read-level error model for sequencing data applied to
DREAMS: Deep Read-level Error Model for Sequencing data applied to
DREAMS: deep read-level error model for sequencing data applied to
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DREAMS: deep read-level error model for sequencing data applied to
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DREAMS: deep read-level error model for sequencing data applied to
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DREAMS: deep read-level error model for sequencing data applied to
PDF) DREAMS: deep read-level error model for sequencing data
DREAMS: deep read-level error model for sequencing data applied to
Jesper Berg Nors - Research - Aarhus University
DREAMS: deep read-level error model for sequencing data applied to
DREAMS: deep read-level error model for sequencing data applied to
DREAMS: deep read-level error model for sequencing data applied to
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