Navigating the Global Protein-Protein Interaction Landscape Using iRefWeb.

iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .

Navigating the global protein-protein interaction landscape using iRefWeb.

iRefWeb is a bioinformatics resource that offers access to a large collection of data on protein-protein interactions in over a thousand organisms. This collection is consolidated from 14 major public databases that curate the scientific literature. The collection is enhanced with a range of versatile data filters and search options that categorize various types of protein-protein interactions and protein complexes. Users of iRefWeb are able to retrieve all curated interactions for a given organism or those involving a given protein (or a list of proteins), narrow down their search results based on different supporting evidence, and assess the reliability of these interactions using various criteria. They may also examine all data and annotations related to any publication that described the interaction-detection experiments. iRefWeb is freely available to the research community worldwide at http://wodaklab.org/iRefWeb .

A survey of protein interaction data and multigenic inherited disorders.

BACKGROUND: Multigenic diseases are often associated with protein complexes or interactions involved in the same pathway. We wanted to estimate to what extent this is true given a consolidated protein interaction data set. The study stresses data integration and data representation issues. RESULTS: We constructed 497 multigenic disease groups from OMIM and tested for overlaps with interaction and pathway data. A total of 159 disease groups had significant overlaps with protein interaction data consolidated by iRefIndex. A further 68 disease overlaps were found only in the KEGG pathway database. No single database contained all significant overlaps thus stressing the importance of data integration. We also found that disease groups overlapped with all three interaction data types: n-ary, spoke-represented complexes and binary data - thus stressing the importance of considering each of these data types separately. CONCLUSIONS: Almost half of our multigenic disease groups could potentially be explained by protein complexes and pathways. However, the fact that no database or data type was able to cover all disease groups suggests that no single database has systematically covered all disease groups for potential related complex and pathway data. This survey provides a basis for further curation efforts to confirm and search for overlaps between diseases and interaction data. The accompanying R script can be used to reproduce the work and track progress in this area as databases change. Disease group overlaps can be further explored using the iRefscape plugin for Cytoscape.

Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction.

BACKGROUND: Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction. RESULTS: Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins - this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein's membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn. CONCLUSIONS: These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.

iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database.

BACKGROUND: The iRefIndex addresses the need to consolidate protein interaction data into a single uniform data resource. iRefR provides the user with access to this data source from an R environment. RESULTS: The iRefR package includes tools for selecting specific subsets of interest from the iRefIndex by criteria such as organism, source database, experimental method, protein accessions and publication identifier. Data may be converted between three representations (MITAB, edgeList and graph) for use with other R packages such as igraph, graph and RBGL.The user may choose between different methods for resolving redundancies in interaction data and how n-ary data is represented. In addition, we describe a function to identify binary interaction records that possibly represent protein complexes. We show that the user choice of data selection, redundancy resolution and n-ary data representation all have an impact on graphical analysis. CONCLUSIONS: The package allows the user to control how these issues are dealt with and communicate them via an R-script written using the iRefR package - this will facilitate communication of methods, reproducibility of network analyses and further modification and comparison of methods by researchers.

iRefScape. A Cytoscape plug-in for visualization and data mining of protein interaction data from iRefIndex.

BACKGROUND: The iRefIndex consolidates protein interaction data from ten databases in a rigorous manner using sequence-based hash keys. Working with consolidated interaction data comes with distinct challenges: data are redundant, overlapping, highly interconnected and may be collected and represented using different curation practices. These phenomena were quantified in our previous studies. RESULTS: The iRefScape plug-in for the Cytoscape graphical viewer addresses these challenges. We show how these factors impact on data-mining tasks and how our solutions resolve them in a simple and efficient manner. A uniform accession space is used to limit redundancy and support search expansion and searching on multiple accession types. Multiple node and edge features support data filtering and mining. Node colours and features supply information about search result provenance. Overlapping evidence is presented using a multi-graph and a bi-partite representation is used to distinguish binary and n-ary source data. Searching for interactions between sets of proteins is supported and specifically includes searches on disease-related genes found in OMIM. Finally, a synchronized adjacency-matrix view facilitates visualization of relationships between sets of user defined groups. CONCLUSIONS: The iRefScape plug-in will be of interest to advanced users of interaction data. The plug-in provides access to a consolidated data set in a uniform accession space while remaining faithful to the underlying source data. Tools are provided to facilitate a range of tasks from a simple search to knowledge discovery. The plug-in uses a number of strategies that will be of interest to other plug-in developers.

Inflammation driven by tumour-specific Th1 cells protects against B-cell cancer.

The immune system can both promote and suppress cancer. Chronic inflammation and proinflammatory cytokines such as interleukin (IL)-1 and IL-6 are considered to be tumour promoting. In contrast, the exact nature of protective antitumour immunity remains obscure. Here, we quantify locally secreted cytokines during primary immune responses against myeloma and B-cell lymphoma in mice. Strikingly, successful cancer immunosurveillance mediated by tumour-specific CD4(+) T cells is consistently associated with elevated local levels of both proinflammatory (IL-1α, IL-1ß and IL-6) and T helper 1 (Th1)-associated cytokines (interferon-γ (IFN-γ), IL-2 and IL-12). Cancer eradication is achieved by a collaboration between tumour-specific Th1 cells and tumour-infiltrating, antigen-presenting macrophages. Th1 cells induce secretion of IL-1ß and IL-6 by macrophages. Th1-derived IFN-γ is shown to render macrophages directly cytotoxic to cancer cells, and to induce macrophages to secrete the angiostatic chemokines CXCL9/MIG and CXCL10/IP-10. Thus, inflammation, when driven by tumour-specific Th1 cells, may prevent rather than promote cancer.

Proteome analysis of microtubule-associated proteins and their interacting partners from mammalian brain.

The microtubule (MT) cytoskeleton is essential for a variety of cellular processes. MTs are finely regulated by distinct classes of MT-associated proteins (MAPs), which themselves bind to and are regulated by a large number of additional proteins. We have carried out proteome analyses of tubulin-rich and tubulin-depleted MAPs and their interacting partners isolated from bovine brain. In total, 573 proteins were identified giving us unprecedented access to brain-specific MT-associated proteins from mammalian brain. Most of the standard MAPs were identified and at least 500 proteins have been reported as being associated with MTs. We identified protein complexes with a large number of subunits such as brain-specific motor/adaptor/cargo complexes for kinesins, dynein, and dynactin, and proteins of an RNA-transporting granule. About 25% of the identified proteins were also found in the synaptic vesicle proteome. Analysis of the MS/MS data revealed many posttranslational modifications, amino acid changes, and alternative splice variants, particularly in tau, a key protein implicated in Alzheimer's disease. Bioinformatic analysis of known protein-protein interactions of the identified proteins indicated that the number of MAPs and their associated proteins is larger than previously anticipated and that our database will be a useful resource to identify novel binding partners.

Literature curation of protein interactions: measuring agreement across major public databases.

Literature curation of protein interaction data faces a number of challenges. Although curators increasingly adhere to standard data representations, the data that various databases actually record from the same published information may differ significantly. Some of the reasons underlying these differences are well known, but their global impact on the interactions collectively curated by major public databases has not been evaluated. Here we quantify the agreement between curated interactions from 15 471 publications shared across nine major public databases. Results show that on average, two databases fully agree on 42% of the interactions and 62% of the proteins curated from the same publication. Furthermore, a sizable fraction of the measured differences can be attributed to divergent assignments of organism or splice isoforms, different organism focus and alternative representations of multi-protein complexes. Our findings highlight the impact of divergent curation policies across databases, and should be relevant to both curators and data consumers interested in analyzing protein-interaction data generated by the scientific community. Database URL: http://wodaklab.org/iRefWeb.

iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence.

We present iRefWeb, a web interface to protein interaction data consolidated from 10 public databases: BIND, BioGRID, CORUM, DIP, IntAct, HPRD, MINT, MPact, MPPI and OPHID. iRefWeb enables users to examine aggregated interactions for a protein of interest, and presents various statistical summaries of the data across databases, such as the number of organism-specific interactions, proteins and cited publications. Through links to source databases and supporting evidence, researchers may gauge the reliability of an interaction using simple criteria, such as the detection methods, the scale of the study (high- or low-throughput) or the number of cited publications. Furthermore, iRefWeb compares the information extracted from the same publication by different databases, and offers means to follow-up possible inconsistencies. We provide an overview of the consolidated protein-protein interaction landscape and show how it can be automatically cropped to aid the generation of meaningful organism-specific interactomes. iRefWeb can be accessed at: http://wodaklab.org/iRefWeb. Database URL: http://wodaklab.org/iRefWeb/

iRefIndex: a consolidated protein interaction database with provenance.

BACKGROUND: Interaction data for a given protein may be spread across multiple databases. We set out to create a unifying index that would facilitate searching for these data and that would group together redundant interaction data while recording the methods used to perform this grouping. RESULTS: We present a method to generate a key for a protein interaction record and a key for each participant protein. These keys may be generated by anyone using only the primary sequence of the proteins, their taxonomy identifiers and the Secure Hash Algorithm. Two interaction records will have identical keys if they refer to the same set of identical protein sequences and taxonomy identifiers. We define records with identical keys as a redundant group. Our method required that we map protein database references found in interaction records to current protein sequence records. Operations performed during this mapping are described by a mapping score that may provide valuable feedback to source interaction databases on problematic references that are malformed, deprecated, ambiguous or unfound. Keys for protein participants allow for retrieval of interaction information independent of the protein references used in the original records. CONCLUSION: We have applied our method to protein interaction records from BIND, BioGrid, DIP, HPRD, IntAct, MINT, MPact, MPPI and OPHID. The resulting interaction reference index is provided in PSI-MITAB 2.5 format at http://irefindex.uio.no. This index may form the basis of alternative redundant groupings based on gene identifiers or near sequence identity groupings.

The contribution of DNA base damage to human cancer is modulated by the base excision repair interaction network.

Base excision repair (BER) is a major mode of repair of DNA base damage. BER is required for maintenance of genetic stability, which is important in the prevention of cancer. However, direct genetic associations between BER deficiency and human cancer have been difficult to firmly establish, and the first-generation mouse models deficient in individual DNA-glycosylases, which are the enzymes that give lesion specificity to the BER pathway, generally do not develop spontaneous tumors. This review summarizes our current understanding of the contribution of DNA base damage to human cancer, with a particular focus on DNA-glycosylases and two of the main enzymes that prevent misincorporation of damaged deoxynucleotide triphosphates into DNA: the dUTPase and MTH1. The available evidence suggests that the most important factors determining individual susceptibility to cancer are not mutations in individual DNA repair enzymes but rather the regulation of expression and modulation of function by protein modification and interaction partners. With this in mind, we present a comprehensive list of protein-protein interactions involving DNA-glycosylases or either of the two enzymes that limit incorporation of damaged nucleotides into DNA. Interacting partners with a known role in human cancer are specifically highlighted.