DREAMS: deep read-level error model for sequencing data applied to
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.
Machine Learning in Predictive Toxicology: Recent Applications and
VeChat: correcting errors in long reads using variation graphs
20+ Deep Learning Projects for Beginners with Source Code
Frontiers Simultaneously collecting coding and non-coding
PDF) DREAMS: deep read-level error model for sequencing data
Using Nucleus and TensorFlow for DNA Sequencing Error Correction
Frontiers DEEPsc: A Deep Learning-Based Map Connecting Single
Cancers, Free Full-Text
Frontiers Learning the Regulatory Code of Gene Expression
