Recent technologies use large-scale genetic and evolutionary datasets to model the structures of proteins and their complexes
Coevolution-based methods model protein structures by identifying pairs of amino acid residues that are likely to be close in space because they evolve together
Genetic interaction-based methods use point mutations to identify pairs of residues that share a common function and are likely to be close in space
Deep learning is playing an increasingly important role in protein structure modeling and helps extract the most informative content from evolutionary protein sequence data and experimental structures
In contrast to traditional structural biology approaches, the discussed technologies rely on data derived from biological function, rather than from physical properties
These datasets are primed to complement traditional structural biology methods to provide a more accurate and complete description of the structures of proteins in vivo
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Computational tools to analyse RNA-seq data often discard reads derived from transposable elements (TE) but measuring TE expression helps to understand when and where TE mobilization occurs and how it alters gene expression, chromatin accessibility or cellular signalling pathways
Measuring TE expression is not straightforward; not only are TEs highly repeated in their host genome but insertional polymorphisms (presence/absence) and internal sequence polymorphisms between individuals complicate their identification
This Review discusses the evolving definitions of transcriptional enhancers and the modern experimental tools to identify, characterize and validate them.
The authors discuss how diverse perspectives and methods provide differing but complementary insights into enhancers, each with notable strengths and caveats, and how they might be combined in a comprehensive catalogue of functional enhancers.