International Molecular Exchange Consortium Research Articles

The IntAct database: efficient access to fine-grained molecular interaction data.

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.

A Network Biology Approach to Understanding the Tissue-Specific Roles of Non-Coding RNAs in Arthritis.

Discovery of non-coding RNAs continues to provide new insights into some of the key molecular drivers of musculoskeletal diseases. Among these, microRNAs have received widespread attention for their roles in osteoarthritis and rheumatoid arthritis. With evidence to suggest that long non-coding RNAs and circular RNAs function as competing endogenous RNAs to sponge microRNAs, the net effect on gene expression in specific disease contexts can be elusive. Studies to date have focused on elucidating individual long non-coding-microRNA-gene target axes and circular RNA-microRNA-gene target axes, with a paucity of data integrating experimentally validated effects of non-coding RNAs. To address this gap, we curated recent studies reporting non-coding RNA axes in chondrocytes from human osteoarthritis and in fibroblast-like synoviocytes from human rheumatoid arthritis. Using an integrative computational biology approach, we then combined the findings into cell- and disease-specific networks for in-depth interpretation. We highlight some challenges to data integration, including non-existent naming conventions and out-of-date databases for non-coding RNAs, and some successes exemplified by the International Molecular Exchange Consortium for protein interactions. In this perspective article, we suggest that data integration is a useful in silico approach for creating non-coding RNA networks in arthritis and prioritizing interactions for further in vitro and in vivo experimentation in translational research.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer's Disease Through Expert Curation of Key Protein Targets.

Background:The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality.Objective:To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer’s disease research.Methods:We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community.Results:Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset.Conclusion:This represents a significant enhancement in the expert curated data pertinent to Alzheimer’s disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

MINT, the molecular interaction database: 2012 update

The Molecular INTeraction Database (MINT, http://mint.bio.uniroma2.it/mint/) is a public repository for protein–protein interactions (PPI) reported in peer-reviewed journals. The database grows steadily over the years and at September 2011 contains approximately 235 000 binary interactions captured from over 4750 publications. The web interface allows the users to search, visualize and download interactions data. MINT is one of the members of the International Molecular Exchange consortium (IMEx) and adopts the Molecular Interaction Ontology of the Proteomics Standard Initiative (PSI-MI) standards for curation and data exchange. MINT data are freely accessible and downloadable at http://mint.bio.uniroma2.it/mint/download.do. We report here the growth of the database, the major changes in curation policy and a new algorithm to assign a confidence to each interaction.

MatrixDB, the extracellular matrix interaction database

MatrixDB (http://matrixdb.ibcp.fr) is a freely available database focused on interactions established by extracellular proteins and polysaccharides. Only few databases report protein–polysaccharide interactions and, to the best of our knowledge, there is no other database of extracellular interactions. MatrixDB takes into account the multimeric nature of several extracellular protein families for the curation of interactions, and reports interactions with individual polypeptide chains or with multimers, considered as permanent complexes, when appropriate. MatrixDB is a member of the International Molecular Exchange consortium (IMEx) and has adopted the PSI-MI standards for the curation and the exchange of interaction data. MatrixDB stores experimental data from our laboratory, data from literature curation, data imported from IMEx databases, and data from the Human Protein Reference Database. MatrixDB is focused on mammalian interactions, but aims to integrate interaction datasets of model organisms when available. MatrixDB provides direct links to databases recapitulating mutations in genes encoding extracellular proteins, to UniGene and to the Human Protein Atlas that shows expression and localization of proteins in a large variety of normal human tissues and cells. MatrixDB allows researchers to perform customized queries and to build tissue- and disease-specific interaction networks that can be visualized and analyzed with Cytoscape or Medusa.

Classification Systems for Bacterial Protein-Protein Interaction Document Retrieval

Protein-protein interaction (PPI) networks are essential to understand the fundamental processes governing cell biology. Recently, studying PPI networks becomes possible due to advances in experimental high-throughput genomics and proteomics technologies. Many interactions from such high-throughput studies and most interactions from small-scale studies are reported only in the scientific literature and thus are not accessible in a readily analyzable format. This has led to the birth of manual curation initiatives such as the International Molecular Exchange Consortium (IMEx). The manual curation of PPI knowledge can be accelerated by text mining systems to retrieve PPI-relevant articles (article retrieval) and extract PPI-relevant knowledge (information extraction). In this article, the authors focus on article retrieval and define the task as binary classification where PPI-relevant articles are positives and the others are negatives. In order to build such classifier, an annotated corpus is needed. It is very expensive to obtain an annotated corpus manually but a noisy and imbalanced annotated corpus can be obtained automatically, where a collection of positive documents can be retrieved from existing PPI knowledge bases and a large number of unlabeled documents (most of them are negatives) can be retrieved from PubMed. They compared the performance of several machine learning algorithms by varying the ratio of the number of positives to the number of unlabeled documents and the number of features used.