Welcome to iRefIndex
Initiative Common Query
InterfaCe for iRefIndex
New iRefIndex release!
iRefIndex iRefIndex is a meta resource based on matching protein sequence data, providing access to a large collection of protein-protein interaction data aggregated from major primary public databases (including BIND, BAR, BioGRID, CORUM, DIP, InnateDB, IntAct, HPRD, HuRi, MATRIXDB, MPACT, MPPI and MPIDB, REACTOME, VIRUSHOST, UNIPROTPP, HPIDB, BHF-UCL, and QuickGO).
This index allows the user to search for a protein
and retrieve a non-redundant list of interactors for that protein. Hence,
iRefIndex represents a unifying index that facilitates searching for these
data whilst grouping together redundant interaction data and recording the
methods used to perform this grouping. This method allows users to
integrate their own data with the iRefIndex in a way that ensures proteins
with the exact same sequence will be represented only once. This tool
originally developed by Ian Donaldson is maintained by VIB Technologies.
Furthermore, the PSICQUIC web service of this
new version has been setup and is hosted on a virtual server at University
of Ghent. We anticipate to release more regular updates in the future.
More information on the current
version 20 at the iRefIndex
Scope of use of iRefIndex
iRefIndex is a meta-resource that provides information about physical protein-protein
interactions: proteins that interact physically with one another, detected by various experimental methods.
It provides information on the proteins involved in the interaction, the methods used
to detect the interaction, and the publications in which the interactions
This information can be used by scientists to better understand the molecular mechanisms of cellular processes and to aid in the discovery of new therapeutic targets. Indeed, physical protein interactions contribute to nearly all cellular processes , and their disruption often causes disease [2,3]
iRefIndex can be used to:
1. Understand the molecular mechanisms of
2. Identify new drug targets and understand
how drugs interact with their targets.
3. Help generate hypotheses for further
4. Study protein-protein interactions and
networks and help to understand the complexity of biological systems.
5. Provide a resource for functional
annotation and data integration.
6. Identify potential new partners for a
protein of interest.
7. Aid in the prediction of protein
8. Help to identify and understand the role
of disease-associated proteins.
Overall, iRefIndex is a valuable resource
for life scientists to help them better understand the complex
interactions and networks in living systems. Resources which use iRefIndex
for their application:
HumanNet v3: an improved database of
human gene networks for disease research (https://doi.org/10.1093/nar/gkab1048)
m6Acancer-Net: identification of
m6A-mediated cancer driver genes from gene-site heterogeneous network
BETA: a comprehensive benchmark for
computational drug–target prediction (https://doi.org/10.1093/bib/bbac199)
XDeathDB: a visualization platform
for cell death molecular interactions
FunCoup 5: functional Association
Networks in All Domains of Life, Supporting Directed Links and
Genoppi: an open-source software for
robust and standardized integration of proteomic and genetic data
TargetMine: a web-based platform that
provides integrated access to various data resources related to drug
target discovery and validation. Including information on proteins,
pathways, drugs and small molecules, as well as their interactions.
iRefIndex has first been developed by Ian Donaldson and his Team in Norway in 2008 
In 2010 it has been incorporated into the IrefWeb resource developed by the group of
Shoshana Wodak (Hospital of Sick Children, and University of Toronoto) .
Since 2017 iRefIndex has moved to the VIB (Vlaams Institute of Biotechnology), where it is being further developed by and actively maintained in collaboration with Shoshana Wodak currently Visiting Group Leader at the VIB-VUB Center for structural Biology (https://wodaklab.sites.vib.be/en) 
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.
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 https://irefindex.vib.be/wiki
This index may form the basis of alternative redundant groupings based on
gene identifiers or near sequence identity groupings."
Alberts B. The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell 1998;92(3):291-294.
Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet 2011;12(1):56-68.
 Ideker T, Sharan R. Protein networks in disease. Genome Res 2008;18(4):644-652.
S., Magklaras, G., & Donaldson, I. M. (2008). iRefIndex: A
consolidated protein interaction database with provenance. BMC
Bioinformatics, 9(1), 405. https://doi.org/10.1186/1471-2105-9-405
 iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence.
Turner B, Razick S, Turinsky AL, Vlasblom J, Crowdy EK, Cho E, Morrison K, Donaldson IM, Wodak SJ. Database (Oxford). 2010 Oct 12;2010:baq023. doi: 10.1093/database/baq023. PMID: 20940177]
Navigating the Global Protein-Protein Interaction Landscape Using iRefWeb.
Turinsky AL, Dupont S, Botzki A, Razick S, Turner B, Donaldson IM, Wodak SJ. Methods Mol Biol. 2021;2199:191-207. doi: 10.1007/978-1-0716-0892-0_12. PMID: 33125652]
"The Proteomics Standard Initiative Common
QUery InterfaCe (PSICQUIC) specification was created by the Human Proteome
Organization Proteomics Standards Initiative (HUPO-PSI) to enable
computational access to molecular-interaction data resources by means of a
standard Web Service and query language. Currently providing >150
million binary interaction evidences from 28 servers globally, the
PSICQUIC interface allows the concurrent search of multiple
molecular-interaction information resources using a single query. Here, we
present an extension of the PSICQUIC specification (version 1.3), which
has been released to be compliant with the enhanced standards in molecular
interactions. The new release also includes a new reference implementation
of the PSICQUIC server available to the data providers. It offers
augmented web service capabilities and improves the user experience.
PSICQUIC has been running for almost 5 years, with a user base growing
from only 4 data providers to 28 (April 2013) allowing access to 151 310
109 binary interactions. The power of this web service is shown in
PSICQUIC View web application, an example of how to simultaneously query,
browse and download results from the different PSICQUIC servers. This
application is free and open to all users with no login requirement."
N., Dumousseau, M., Orchard, S., Jimenez, R. C., Galeota, E., Launay,
G., Hermjakob, H. (2013). A new reference implementation of the
PSICQUIC web service. Nucleic Acids Research, 41(W1), W601-606.
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