Evaluating deep learning for predicting epigenomic profiles | Nature Machine Intelligence
Deep learning has been successful at predicting epigenomic profiles from DNA sequences. Here researchers introduce a unified evaluation framework and use it to compare various binary and quantitative models trained to predict chromatin accessibility data. They highlight various modelling choices that affect generalization performance, including a downstream application of predicting variant effects.
Microbiome Toolbox: Methodological approaches to derive and visualize microbiome trajectories | Bioinformatics
The gut microbiome changes rapidly under the influence of different factors such as age, dietary changes, or medications to name just a few. To analyze and understand such changes researchers present a microbiome analysis toolbox. They implemented several methods for analysis and exploration to provide interactive visualizations for easy comprehension and reporting of longitudinal microbiome data.
A framework for clinical cancer subtyping from nucleosome profiling of cell-free DNA | Nature Communications
Cell-free DNA (cfDNA) has the potential to inform tumor subtype classification and help guide clinical precision oncology. Here researchers develop Griffin, a framework for profiling nucleosome protection and accessibility from cfDNA to study the phenotype of tumors using as low as 0.1x coverage whole genome sequencing data. Griffin employs a GC correction procedure tailored to variable cfDNA fragment sizes, which generates a better representation of chromatin accessibility and improves the accuracy of cancer detection and tumor subtype classification.