Progress toward a universal biomedical data translator.

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science.

MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations.

Motivation: Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns.

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts.

Gene Ontology is used extensively in scientific knowledgebases and repositories to organize a wealth of biological information. However, interpreting annotations derived from differential gene lists is often difficult without manually sorting into higher-order categories. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics.

SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology.

Motivation: The Gene Ontology (GO) is the unifying biological vocabulary for codifying, managing and sharing biological knowledge. Quality issues in GO, if not addressed, can cause misleading results or missed biological discoveries. Manual identification of potential quality issues in GO is a challenging and arduous task, given its growing size.