Mark Gerstein

TL;DR: These analyses highlight the considerable added value of assembly-based lrWGS to create new catalogs of insertions and transposable elements, as well as disease-associated repeat expansions in genomic sequences that were previously recalcitrant to routine assessment.

TL;DR: Though fundamentally 3D-structural in nature, this analysis is computationally fast, thereby allowing us to run it across the PDB and to evaluate general properties of predicted allosteric residues, and it is found that these tend to be conserved over diverse evolutionary time scales.

TL;DR: These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types.

TL;DR: Several examples of machine learning techniques used in a genomic context are given, including clustering methods to organize microarray expression data, support vector machines to predict protein function, Bayesian networks to predict subcellular localization, and decision trees to optimize target selection for high-throughput proteomics.

TL;DR: A principal aim of post-genomic biology is elucidating the structures, functions and biochemical properties of all gene products in a genome, but to adequately comprehend such a large amount of information the authors need new descriptions of proteins that scale to the genomic level, and a unified ontology for proteomics is needed.