Comprehensive Characterization of Cancer Driver Genes and Mutations.

TL;DR: Molecular and imaging markers identified can be further investigated to guide patient stratification for more precise treatment of EC and deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis.

TL;DR: An overview of various experimental systems linking carcinogen exposure‐driven cell transformation with the study of cancer driver events is provided and the advantages and disadvantages of the currently available cell‐based models are described while outlining future directions for in vitro modeling and functional testing of cancerDriver events.

TL;DR: Banyai et al. as discussed by the authors used a novel approach to address the question of whether negative selection occurs in cancer, exploiting the fact that driver genes show significantly higher mutation rates than passenger genes.

TL;DR: In this paper, the authors compared the predictive values of CIRCLE-seq, an in-vitro off-target prediction method, with in-silico prediction (ISP) based solely on genomic sequence comparisons.

TL;DR: The ANNOVAR tool to annotate single nucleotide variants and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP is developed.

TL;DR: Circos uses a circular ideogram layout to facilitate the display of relationships between pairs of positions by the use of ribbons, which encode the position, size, and orientation of related genomic elements.

TL;DR: The aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC) provides direct evidence for the presence of widespread mutational recurrence.

TL;DR: It is shown that hypermutation localized to small genomic regions, ‘kataegis’, is found in many cancer types, and this results reveal the diversity of mutational processes underlying the development of cancer.