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In six new studies published in Science, the Telomere-to-Telomere (T2T) Consortium reports the assembly and initial characterization of the final, previously unresolved 8% of the human genome.
In this Journal Club article, Yana Bromberg discusses an early application of machine learning for protein structure prediction — a paper that shaped her career. It illustrates the value of ensuring that machine learning approaches are rooted in known biological principles.
A paper in Nature Biotechnology describes epigenetic expression inference from cfDNA-sequencing (EPIC-seq), and demonstrates its use for non-invasive classification of cancers.
A recent study in Cell describes a developmentally important liquid-to-solid phase transition involving oskar ribonucleoprotein granules in Drosophila melanogaster oocytes.
A paper in Molecular Cell reports EpiDamID, a new tool for simultaneously profiling transcription and histone post-translation modifications in single cells.
In this Review, Fitz-James and Cavalli discuss the diverse and often multilayered mechanisms by which transgenerational epigenetic inheritance can occur in different species.
Large-scale genetic datasets and deep learning approaches are being used to model the structures of proteins or protein complexes. This Review describes approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical–genetic interaction mapping and their application and integration to inform structural modelling.
In this Review, Ding, Sharon and Bar-Joseph discuss how dynamic features can be incorporated into single-cell transcriptomics studies, using both experimental and computational strategies to provide biological insights.
DNA methylation-based predictors of health aim to predict outcomes such as exposure, phenotype or disease on the basis of genome-wide levels of DNA methylation. The authors review applications of existing DNA methylation-based predictors and highlight key statistical best practices to ensure their reliable performance.
