Rose Oughtred

The BioGRID Interaction Database: 2011 update

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions.

The BioGRID interaction database

The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.

The Gene Ontology project in 2008

The Gene Ontology (GO) project (http://www.geneontology.org/) provides a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://www.sequenceontology.org/). The ontologies have been extended and refined for several biological areas, and improvements to the structure of the ontologies have been implemented. To improve the quantity and quality of gene product annotations available from its public repository, the GO Consortium has launched a focused effort to provide comprehensive and detailed annotation of orthologous genes across a number of ‘reference’ genomes, including human and several key model organisms. Software developments include two releases of the ontology-editing tool OBO-Edit, and improvements to the AmiGO browser interface.

Gene Ontology annotations at SGD: new data sources and annotation methods

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current.

Saccharomyces Genome Database provides mutant phenotype data

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is a scientific database for the molecular biology and genetics of the yeast Saccharomyces cerevisiae, which is commonly known as baker’s or budding yeast. The information in SGD includes functional annotations, mapping and sequence information, protein domains and structure, expression data, mutant phenotypes, physical and genetic interactions and the primary literature from which these data are derived. Here we describe how published phenotypes and genetic interaction data are annotated and displayed in SGD.

Genome Snapshot: a new resource at the Saccharomyces Genome Database (SGD) presenting an overview of the Saccharomyces cerevisiae genome

Sequencing and annotation of the entire Saccharomyces cerevisiae genome has made it possible to gain a genome-wide perspective on yeast genes and gene products. To make this information available on an ongoing basis, the Saccharomyces Genome Database (SGD) () has created the Genome Snapshot (). The Genome Snapshot summarizes the current state of knowledge about the genes and chromosomal features of S.cerevisiae. The information is organized into two categories: (i) number of each type of chromosomal feature annotated in the genome and (ii) number and distribution of genes annotated to Gene Ontology terms. Detailed lists are accessible through SGDs Advanced Search tool (), and all the data presented on this page are available from the SGD ftp site ().

Expanded protein information at SGD: new pages and proteome browser

The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; ) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82 000 manually-curated interactions.

The Princeton Protein Orthology Database (P-POD): A Comparative Genomics Analysis Tool for Biologists

Many biological databases that provide comparative genomics information and tools are now available on the internet. While certainly quite useful, to our knowledge none of the existing databases combine results from multiple comparative genomics methods with manually curated information from the literature. Here we describe the Princeton Protein Orthology Database (P-POD, http://ortholog.princeton.edu), a user-friendly database system that allows users to find and visualize the phylogenetic relationships among predicted orthologs (based on the OrthoMCL method) to a query gene from any of eight eukaryotic organisms, and to see the orthologs in a wider evolutionary context (based on the Jaccard clustering method). In addition to the phylogenetic information, the database contains experimental results manually collected from the literature that can be compared to the computational analyses, as well as links to relevant human disease and gene information via the OMIM, model organism, and sequence databases. Our aim is for the P-POD resource to be extremely useful to typical experimental biologists wanting to learn more about the evolutionary context of their favorite genes. P-POD is based on the commonly used Generic Model Organism Database (GMOD) schema and can be downloaded in its entirety for installation on ones own system. Thus, bioinformaticians and software developers may also find P-POD useful because they can use the P-POD database infrastructure when developing their own comparative genomics resources and database tools.

Fungal BLAST and Model Organism BLASTP Best Hits: new comparison resources at the Saccharomyces Genome Database (SGD).

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) is a scientific database of gene, protein and genomic information for the yeast Saccharomyces cerevisiae. SGD has recently developed two new resources that facilitate nucleotide and protein sequence comparisons between S.cerevisiae and other organisms. The Fungal BLAST tool provides directed searches against all fungal nucleotide and protein sequences available from GenBank, divided into categories according to organism, status of completeness and annotation, and source. The Model Organism BLASTP Best Hits resource displays, for each S.cerevisiae protein, the single most similar protein from several model organisms and presents links to the database pages of those proteins, facilitating access to curated information about potential orthologs of yeast proteins.

Recurated protein interaction datasets

controlled vocabularies for annotation allows the database users to efficiently select subsets of data according to criteria relevant for their particular use. In contrast, Cusick et al.1 define a set of criteria for a specific use restricted only to direct pairwise protein-protein interactions, which they refer to as ‘binary’ interactions. They evaluate literature-curated datasets against these criteria and then assert that failure to meet their criteria represents “incorrect curation.” The criteria defined by Cusick et al.1 vary slightly from species to species but aim to select only direct interactions with multiple independent supporting reports. While this is one valid use, other users might, for example, look for all observed interactions of a given protein, whether direct or indirect, to subsequently assess the supporting evidence by reading the supporting publications. Whereas protein-protein interaction databases may also use the term ‘binary’ when referring to pairs of interacting proteins, our usage of the term refers to any interaction pair and makes no judgment regarding whether the interaction is direct or indirect. We strongly object to the notion that inclusion of an interaction with limited supporting evidence of a direct interaction represents a curation error. On the contrary, most interaction databases always fully curate a given publication and would consider it an egregious omission if only a subset of the protein interactions reported in a publication or its supplementary material would be contained in the database. When informationfor example, species informationin a publication is ambiguous, database curators attempt to contact the authors and only leave out data if clarification cannot be obtained. In response to the claims of Cusick et al.1, we reanalyzed interactions presented in their paper to identify actual curation errors, defined as inconsistencies between the original published data and their representation in our databases. Details of our analysis are available in the Supplementary Note, and we reannotated versions of the original tables supplied by Cusick et al.1 (Supplementary Tables 1–3). The actual curation error rate was, in fact, consistently under 10%. For the yeast dataset, we confirmed 4 actual curation errors among the 100 sample interactions from BioGRID chosen by Cusick et al.1; the curation error rate of 4% is precisely the value originally reported for the dataset7 and an order of magnitude lower than the claim by Cusick et al.1: “Of the interacting pairs in the sample, 35% were incorrectly curated.” For comparison, we analyzed a subset of the BioGRID data that is also present in the DIP database and identified 1 actual curation error out of 29 shared records, that is, a similarly low error rate of 3%. For the human dataset, of the 220 sampled interactions annotated in MINT, only 10 were curation errors, corresponding to a curation error rate of 4.5%. Similarly, only 4 out of 42 curation records reported in DIP contained errors, a 9% curation error rate, or one-fifth of the 45% curation error rate implied by Cusick et al.1. For the Arabidopsis thaliana, the IntAct dataset contained 3 actual curation errors in 183 curation records, resulting in an error rate of 2%, less than one-fifth of the 10.7% rate claimed by Cusick et al.1 in their Table 2. For TAIR, the actual error rate was only 3%, or less than one-third of the rate claimed by Cusick et al.1. Accurate and detailed curation is an arduous process both in terms of individual curator expertise and curation time. To optimize the use of public funding, the member databases of the International Molecular Exchange Consortium (IMEx)8 DIP, IntAct and MINT coordinate their curation efforts to avoid unnecessary redundancy, measured in PBS4, which may result in an overestimation of photostability compared to commonly used live-cell imaging conditions. The use of media depleted of vitamins for fluorescence imaging of live cultured cells appears to be a simple and efficient way to improve the performance of some widely used fluorescent proteins in various ensemble and single-molecule applications1,5,6.