Gene Ontology annotations: what they mean and where they come from

To address the challenges of information integration and retrieval, the computational genomics community increasingly has come to rely on the methodology of creating annotations of scientific literature using terms from controlled structured vocabularies such as the Gene Ontology (GO). Here we address the question of what such annotations signify and of how they are created by working biologists. Our goal is to promote a better understanding of how the results of experiments are captured in annotations, in the hope that this will lead both to better representations of biological reality through annotation and ontology development and to more informed use of GO resources by experimental scientists

Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease.

The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection

Cisplatin-resistant triple-negative breast cancer subtypes: multiple mechanisms of resistance.

BACKGROUND: Understanding mechanisms underlying specific chemotherapeutic responses in subtypes of cancer may improve identification of treatment strategies most likely to benefit particular patients. For example, triple-negative breast cancer (TNBC) patients have variable response to the chemotherapeutic agent cisplatin. Understanding the basis of treatment response in cancer subtypes will lead to more informed decisions about selection of treatment strategies. METHODS: In this study we used an integrative functional genomics approach to investigate the molecular mechanisms underlying known cisplatin-response differences among subtypes of TNBC. To identify changes in gene expression that could explain mechanisms of resistance, we examined 102 evolutionarily conserved cisplatin-associated genes, evaluating their differential expression in the cisplatin-sensitive, basal-like 1 (BL1) and basal-like 2 (BL2) subtypes, and the two cisplatin-resistant, luminal androgen receptor (LAR) and mesenchymal (M) subtypes of TNBC. RESULTS: We found 20 genes that were differentially expressed in at least one subtype. Fifteen of the 20 genes are associated with cell death and are distributed among all TNBC subtypes. The less cisplatin-responsive LAR and M TNBC subtypes show different regulation of 13 genes compared to the more sensitive BL1 and BL2 subtypes. These 13 genes identify a variety of cisplatin-resistance mechanisms including increased transport and detoxification of cisplatin, and mis-regulation of the epithelial to mesenchymal transition. CONCLUSIONS: We identified gene signatures in resistant TNBC subtypes indicative of mechanisms of cisplatin. Our results indicate that response to cisplatin in TNBC has a complex foundation based on impact of treatment on distinct cellular pathways. We find that examination of expression data in the context of heterogeneous data such as drug-gene interactions leads to a better understanding of mechanisms at work in cancer therapy response

Literature Triage and Indexing in the Mouse Genome Informatics (MGI) Group

The Mouse Genome Informatics (MGI; "http://www.informatics.jax.org":http://www.informatics.jax.org) group is comprised of several collaborating projects including the Mouse Genome Database (MGD) Project, the Gene Expression Database (GXD) Project, the Mouse Tumor Biology (MTB) Database Project, and the Gene Ontology (GO) Project. Literature identification and collection is performed cooperatively amongst the groups.

In recent years many institutional libraries have transitioned from a focus largely on print holdings to one of electronic access to journals. This change has necessitated adaptation on the part of the MGI curatorial group. Whereas the majority of journals covered by the group used to be surveyed in paper form, those journals are now surveyed electronically. Approximately 160 journals have been identified as those most relevant to the various database groups. Each curator in the group has the responsibility of scanning several journals for articles relevant to any of the database projects. Articles chosen via this process are marked as to their potential significance for various projects. Each article is catalogued in a Master Bibliography section of the MGI database system and annotated to the database sections for which it has been identified as relevant. A secondary triage process allows curators from each group to scan the chosen articles and mark ones desired for their project if such annotation has been missed on the initial scan.

Once articles have been identified for each database project a variety of processes are implemented to further categorize and index data from those articles. For example, the Alleles and Phenotype section of the MGD database indexes each article marked for MGD and in this indexing process they identify each mouse gene and allele examined in the article. The GXD database indexing process has a different focus. In this case articles are indexed with regard to the stage of development used in the study as well as the assay technique used. In each case the indexing gives an overview of the data held in the article and assists in the more extensive curation performed in the following step of the curation process. Indexing also provides each group with valuable information used to prioritize and streamline the overall curation process.

The MGI projects are supported by NHGRI grants HG000330, HG00273, and HG003622, NICHD grant HD033745, and NCI grant CA089713

Annotations are provided to the Gene Ontology Consortium as tab-delimited files with 15 fields

Four fields indicate the gene product being annotated, the ontology terms used in the association, the type of evidence supporting the annotation and the reference where the original evidence was presented. The three annotations described in this manuscript are shown.<p><b>Copyright information:</b></p><p>Taken from "Gene Ontology annotations: what they mean and where they come from"</p><p>http://www.biomedcentral.com/1471-2105/9/S5/S2</p><p>BMC Bioinformatics 2008;9(Suppl 5):S2-S2.</p><p>Published online 29 Apr 2008</p><p>PMCID:PMC2367625.</p><p></p

Adoption of PERILIPIN as a unifying nomenclature for the mammalian PAT-family of intracellular lipid storage droplet proteins

The PAT family of proteins has been identified in eukaryotic species as diverse as vertebrates, insects, and amebazoa. These proteins share a highly conserved sequence organization and avidity for the surfaces of intracellular, neutral lipid storage droplets. The current nomenclature of the various members lacks consistency and precision, deriving more from historic context than from recognition of evolutionary relationship and shared function. In consultation with the Mouse Genomic Nomenclature Committee, the Human Genome Organization Genomic Nomenclature Committee, and conferees at the 2007 FASEB Conference on Lipid Droplets: Metabolic Consequences of the Storage of Neutral Lipids, we have established a unifying nomenclature for the gene and protein family members. Each gene member will incorporate the root term PERILIPIN (PLIN), the founding gene of the PAT family, with the different genes/proteins numbered sequentially