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.

Multimodal Annotation Tools Top Tools

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

Estimation of sequencing error rates in short reads

Comparing Current Noise in Biological and Solid-State Nanopores

Systematic evaluation of error rates and causes in short samples

Why We Sleep: Unlocking the Power of by Walker, Matthew

Deep learning–based integration of genetics with registry data for

Inter-rater Reliability: Definition & Applications

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

Remote Sensing, Free Full-Text

An Automated Model Test System for Systematic Development and

Grammatical Error Correction Using Neural Networks

Training, Validation, Test Datasets for ML Algorithms

Reimagining our futures together: a new social contract for education

Integration of intra-sample contextual error modeling for improved