DREAMS: deep read-level error model for sequencing data applied to
Por um escritor misterioso
Last updated 01 abril 2025

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.

Frontiers Learning the Regulatory Code of Gene Expression
GitHub - JakobSkouPedersenLab/dreams: Deep Read-level Error Model
Machine Learning in Predictive Toxicology: Recent Applications and

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

Characterizing genetic intra-tumor heterogeneity across 2,658

Recent advances and applications of deep learning methods in

Systematic evaluation of error rates and causes in short samples

DREAM flow chart. Models and experimental data are collected from

PDF) DREAMS: deep read-level error model for sequencing data

A review on machine learning approaches and trends in drug

A benchmark study on error-correction by read-pairing and tag

Applied Sciences, Free Full-Text
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