DREAMS: deep read-level error model for sequencing data applied to
Por um escritor misterioso
Last updated 27 setembro 2024
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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