The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

CIViC is an expert-crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer describing the therapeutic, prognostic, diagnostic and predisposing relevance of inherited and somatic variants of all types. CIViC is committed to open-source code, open-access content, public application programming interfaces (APIs) and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

Human immunodeficiency virus (HIV) infection of the central nervous system (CNS)is a significant cause of morbidity. The requirements for HIV adaptation to the CNS for neuropathogenesis and the value of CSF virus as a surrogate for virus activity in brain parenchyma are not well established. We studied 18 HIV-infected subjects, most with advanced immunodeficiency and some neurocognitive impairment but none with evidence of opportunistic infection or malignancy of the CNS. Clonal sequences of C2-V3 env and population sequences of pol from HIV RNA in cerebrospinal fluid (CSF) and plasma were correlated with clinical and virologic variables. Most (14 of 18) subjects had partitioning of C2-V3 sequences according to compartment, and 9 of 13 subjects with drug resistance exhibited discordant resistance patterns between the two compartments. Regression analyses identified three to seven positions in C2-V3 that discriminated CSF from plasma HIV. The presence of compartmental differences at one or more of the identified positions in C2-V3 was highly associated with the presence of discordant resistance (P ‫؍‬ 0.007), reflecting the autonomous replication of HIV and the independent evolution of drug resistance in the CNS. Discordance of resistance was associated with severity of neurocognitive deficits (P ‫؍‬ 0.07), while low nadir CD4 counts were linked both to the severity of neurocognitive deficits and to discordant resistance patterns (P ‫؍‬ 0.05 and 0.09, respectively). These observations support the study of CSF HIV as an accessible surrogate for HIV virions in the brain, confirm the high frequency of discordant resistance in subjects with advanced disease in the absence of opportunistic infection or malignancy of the CNS, and begin to identify genetic patterns in HIV env associated with adaptation to the CNS.

The particular coreceptor used by a strain of HIV-1 to enter a host cell is highly indicative of its pathology. HIV-1 coreceptor usage is primarily determined by the amino add sequences of the V3 loop region of the viral envelope glycoprotein. The canonical approach to sequence-based prediction of coreceptor usage was derived via statistical analysis of a less reliable and significantly smaller data set than is presently available. We aimed to produce a superior phenotypic classifier by applying modern machine learning (ML) techniques to the current database of V3 loop sequences with known phenotype. The trained classifiers along with the sequence data are available for public use at the supplementary website: http://genomiac2.ucsd.edu:8080/wetcat/v3.html and http://www.cs.waikato.ac.nz/ml/weka[corrected].

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.