• allwayOrthologs_hsapiens-ptroglodytes-mmulatta-mmusculus-rnorvegicus-btaurus-sscrofa-ggallus_ens94.tsv: shared 1-to-1 orthologs in eight species (Ensembl v94)
• hg38_geneID2GO.EnsemblID2GO.EnsembleV94.Robj : hg38 Ensembl v94 geneID mapping to GO ID list R object.
• mm10_geneID2GO.EnsemblID2GO.EnsembleV94.Robj: mm10 Ensembl v94 geneID mapping to GO ID list R object.
• EnsemblID2GO.R: script to generate Ensembl gene ID mapping to GO ID list.
• hg38_length.EnsemblV94.RData: hg38 gene length Robject
• mm10_length_EnsemblV94.RData: mm10 gene length Robject

Bioinformatics, the application of computational tools to the management and analysis of biological data, has stimulated rapid research advances in genomics through the development of data archives such as GenBank, and similar progress is just beginning within ecology. One reason for the belated adoption of informatics approaches in ecology is the breadth of ecologically pertinent data (from genes to the biosphere) and its highly heterogeneous nature. The variety of formats, logical structures, and sampling methods in ecology create significant challenges. Cultural barriers further impede progress, especially for the creation and adoption of data standards. Here we describe informatics frameworks for ecology, from subject-specific data warehouses, to generic data collections that use detailed metadata descriptions and formal ontologies to catalog and cross-reference information. Combining these approaches with automated data integration techniques and scientific workflow systems will maximize the value of data and open new frontiers for research in ecology.

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

CAFA3 dataset (Pairs format)

Supplementary material for the paper "The use of Foundational Ontologies in Bioinformatics".

In toxicogenomics, functional annotation is an important step to gain additional insights into genes with aberrant expression that drive pathophysiological mechanisms. Nevertheless, there exists a gap on annotation of these genes which often hampers the interpretation of results and limits their applicability in translational medicine. In this study, we evaluated the coverage of functional annotations of differentially expressed genes (DEGs) induced by 10 selected compounds from the TG-GATEs database identified as high- or no-risk in causing drug-induced liver injury (most-DILI or no-DILI, respectively) using in vitro human data. Functional roles of DEGs not present in the most common biological annotation databases – termed “dark genes” – were unveiled via literature mining and via the identification of shared regulatory transcription factors or signaling pathways. Our results demonstrated that there were approximately 13% of dark genes induced by these compounds in vitro and we were able to obtain additional relevant information for up to 76% of those. Using interactome data from several sources, we have uncovered genes such as LRBA, and WDR26 as highly connected in the protein network that play roles in drug response. Genes such as MALAT1, H19, and MIR29C – whose links to hepatotoxicity have been confirmed – were identified as markers for the most-DILI group and appeared as top hits across all literature-based mining methods. Furthermore, we investigated the potential impact of dark genes on liver toxicity by identifying their rat orthologs in combination with their correlation to drug-induced liver pathologies observed in vivo following chemical exposure. We identified a set of important regulatory transcription factors of dark genes for all most-DILI compounds including E2F1 and JUND with supporting evidences in literature and we found Magee1 correlated with chemically induced bile duct hyperplasia and adverse responses at 29 days in rats in vivo. In conclusion, in this study we show the potential role of these poorly annotated genes in mechanisms underlying hepatotoxicity and offer a number of computational approaches that may help to minimize current gaps in gene annotation and highlight their values as potential biomarkers in toxicological studies.

EDAM is an ontology of general bioinformatics concepts, including topics, data types, formats, identifiers and operations. EDAM provides a controlled vocabulary for the description, in semantic terms, of things such as: web services (e.g. WSDL files), applications, tool collections and packages, work-benches and workflow software, databases and ontologies, XSD data schema and data objects, data syntax and file formats, web portals and pages, resource catalogues and documents (such as scientific publications).

The rich knowledge of morphological variation among organisms reported in the systematic literature has remained in free-text format, impractical for use in large-scale synthetic phylogenetic work. This noncomputable format has also precluded linkage to the large knowledgebase of genomic, genetic, developmental, and phenotype data in model organism databases. We have undertaken an effort to prototype a curated, ontology-based evolutionary morphology database that maps to these genetic databases (http://kb.phenoscape.org) to facilitate investigation into the mechanistic basis and evolution of phenotypic diversity. Among the first requirements in establishing this database was the development of a multispecies anatomy ontology with the goal of capturing anatomical data in a systematic and computable manner. An ontology is a formal representation of a set of concepts with defined relationships between those concepts. Multispecies anatomy ontologies in particular are an efficient way to represent the diversity of morphological structures in a clade of organisms, but they present challenges in their development relative to single-species anatomy ontologies. Here, we describe the Teleost Anatomy Ontology (TAO), a multispecies anatomy ontology for teleost fishes derived from the Zebrafish Anatomical Ontology (ZFA) for the purpose of annotating varying morphological features across species. To facilitate interoperability with other anatomy ontologies, TAO uses the Common Anatomy Reference Ontology as a template for its upper level nodes, and TAO and ZFA are synchronized, with zebrafish terms specified as subtypes of teleost terms. We found that the details of ontology architecture have ramifications for querying, and we present general challenges in developing a multispecies anatomy ontology, including refinement of definitions, taxon-specific relationships among terms, and representation of taxonomically variable developmental pathways.

ABSTRACT:

This dataset displays the utilization of Gene Ontology (GO) and the web application AmiGO for gene annotation across multiple species, including Homo sapiens (humans). The study involves the analysis of diverse gene types such as complexes, RNAs, proteins, and isoforms which are all gene products found specifically in humans. The provided dataset encompasses comprehensive information regarding these genes, including data source companies, gene names, descriptions, and Gene Ontology IDs. By leveraging Gene Ontology and AmiGO, this research aims to provide valuable insights into the functional characteristics and annotations of different gene types, contributing to a deeper understanding of gene function in the context of human biology.

Instructions:

Data was collected as 4 separate sets with 4 different types of genes and the duplicated genes, organism type, references, and dates were removed. Links to the specific gene descriptions were added to the dataset as a new column. All four data sets are appended to one another because all of the columns are identical. Isoforms have a different link location going to UniProt.

Inspiration:

This dataset uploaded to U-BRITE for "DRG_DEPOT" summer 2023 team project.

Acknowledgements:

Seth Carbon ¹, Amelia Ireland, Christopher J Mungall, ShengQiang Shu, Brad Marshall, Suzanna Lewis; AmiGO Hub; Web Presence Working Group

Collaborators, Affiliations expand PMID: 19033274 PMCID: PMC2639003 DOI: 10.1093/bioinformatics/btn615

AmiGO online access to ontology and annotation data- https://amigo.geneontology.org/amigo/search/annotation

U-BRITE last update data: 06/27/2023

Data resource catalog that collates metadata on bioinformatics Web-based data resources including databases, ontologies, taxonomies and catalogues. An entry includes information such as resource identifier(s), name, description and URL. ''''Query'''' lines are defined for each resource that describe what type(s) of data are available, in what format, how (by what identifier) the data can be retrieved and from where (URL). DRCAT was developed to provide more extensive data integration for EMBOSS, but it has many applications beyond EMBOSS. DRCAT entries (including ''''Query'''' lines) are annotated with terms from the EDAM ontology of common bioinformatics concepts.

see https://github.com/wangshenguiuc/OnClass/blob/master/scripts/RunOnClassExample.py for instructions

Starting in the fall of 2007, a collaborative group of influenza researchers have established an influenza ontology. The influenza ontology is an application ontology. Consolidated influenza sequence and surveillance terms from resources such as the BioHealthBase (BHB), a Bioinformatics Resource Center (BRC) for Biodefense and Emerging and Re-emerging Infectious Diseases, the Centers for Excellence in Influenza Research and Surveillance (CEIRS)

A protein database which connects multiple disparate bioinformatics tools and systems text mining, data mining, analysis and visualization tools, and databases and ontologies.

Introduction: The normal development of all heart valves requires highly coordinated signaling pathways and downstream mediators. While genomic variants can be responsible for congenital valve disease, environmental factors can also play a role. Later in life valve calcification is a leading cause of aortic valve stenosis, a progressive disease that may lead to heart failure. Current research into the causes of both congenital valve diseases and valve calcification is using a variety of high-throughput methodologies, including transcriptomics, proteomics and genomics. High quality genetic data from biological knowledge bases are essential to facilitate analyses and interpretation of these high-throughput datasets. The Gene Ontology (GO, http://geneontology.org/) is a major bioinformatics resource used to interpret these datasets, as it provides structured, computable knowledge describing the role of gene products across all organisms. The UCL Functional Gene Annotation team focuses on GO annotation of human gene products. Having identified that the GO annotations included in transcriptomic, proteomic and genomic data did not provide sufficient descriptive information about heart valve development, we initiated a focused project to address this issue.Methods: This project prioritized 138 proteins for GO annotation, which led to the curation of 100 peer-reviewed articles and the creation of 400 heart valve development-relevant GO annotations.Results: While the focus of this project was heart valve development, around 600 of the 1000 annotations created described the broader cellular role of these proteins, including those describing aortic valve morphogenesis, BMP signaling and endocardial cushion development. Our functional enrichment analysis of the 28 proteins known to have a role in bicuspid aortic valve disease confirmed that this annotation project has led to an improved interpretation of a heart valve genetic dataset.Discussion: To address the needs of the heart valve research community this project has provided GO annotations to describe the specific roles of key proteins involved in heart valve development. The breadth of GO annotations created by this project will benefit many of those seeking to interpret a wide range of cardiovascular genomic, transcriptomic, proteomic and metabolomic datasets.

Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis.

MTBBASE is a database about the physiology of the tuberculosis bacterium, in widely-used formats, and publicly accessible. Available already is MTB-GOA, paper-centric Gene Ontology (GO) annotations. Pathway data on reactome is in progress. This is a private effort because I am not affiliated with a university nor a company. Nevertheless, I am putting a constant part of the day into the work. The annotations were checked by the UniProt-GOA group at the EBI (after passing my own checks) and pathways are checked by a reactome editor. This work would not have been possible without NCBI''s Entrez service, the german national licence, UniProt, and tuberculist at epfl.ch.

Transcriptomic information (spatiotemporal gene expression profile data) on the postnatal cerebellar development of mice (C57B/6J & ICR). It is a tool for mining cerebellar genes and gene expression, and provides a portal to relevant bioinformatics links. The mouse cerebellar circuit develops through a series of cellular and morphological events, including neuronal proliferation and migration, axonogenesis, dendritogenesis, and synaptogenesis, all within three weeks after birth, and each event is controlled by a specific gene group whose expression profile must be encoded in the genome. To elucidate the genetic basis of cerebellar circuit development, CDT-DB analyzes spatiotemporal gene expression by using in situ hybridization (ISH) for cellular resolution and by using fluorescence differential display and microarrays (GeneChip) for developmental time series resolution. The CDT-DB not only provides a cross-search function for large amounts of experimental data (ISH brain images, GeneChip graph, RT-PCR gel images), but also includes a portal function by which all registered genes have been provided with hyperlinks to websites of many relevant bioinformatics regarding gene ontology, genome, proteins, pathways, cell functions, and publications. Thus, the CDT-DB is a useful tool for mining potentially important genes based on characteristic expression profiles in particular cell types or during a particular time window in developing mouse brains.

The chromosome-centric human proteome project aims to systematically map all human proteins, chromosome by chromosome, in a gene-centric manner through dedicated efforts from national and international teams. This mapping will lead to a knowledge-based resource defining the full set of proteins encoded in each chromosome and laying the foundation for the development of a standardized approach to analyze the massive proteomic data sets currently being generated. The neXtProt database lists 946 proteins as the human proteome of chromosome 7. However, 170 (18%) proteins of human chromosome 7 have no evidence at the proteomic, antibody, or structural levels and are considered “missing” in this study as they lack experimental support. We have developed a protocol for the functional annotation of these “missing” proteins by integrating several bioinformatics analysis and annotation tools, sequential BLAST homology searches, protein domain/motif and gene ontology (GO) mapping, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Using the BLAST search strategy, homologues for reviewed non-human mammalian proteins with protein evidence were identified for 90 “missing” proteins while another 38 had reviewed non-human mammalian homologues. Putative functional annotations were assigned to 27 of the remaining 43 novel proteins. Proteotypic peptides have been computationally generated to facilitate rapid identification of these proteins. Four of the “missing” chromosome 7 proteins have been substantiated by the ENCODE proteogenomic peptide data.

International database for laboratory mouse. Data offered by The Jackson Laboratory includes information on integrated genetic, genomic, and biological data. MGI creates and maintains integrated representation of mouse genetic, genomic, expression, and phenotype data and develops reference data set and consensus data views, synthesizes comparative genomic data between mouse and other mammals, maintains set of links and collaborations with other bioinformatics resources, develops and supports analysis and data submission tools, and provides technical support for database users. Projects contributing to this resource are: Mouse Genome Database (MGD) Project, Gene Expression Database (GXD) Project, Mouse Tumor Biology (MTB) Database Project, Gene Ontology (GO) Project at MGI, and MouseCyc Project at MGI.

Bioinformatics Resource Center for invertebrate vectors. Provides web-based resources to scientific community conducting basic and applied research on organisms considered potential agents of biowarfare or bioterrorism or causing emerging or re-emerging diseases.