Open AccessPICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology
Author(s) -
Aris Gioutlakis,
Maria I. Klapa,
Nicholas Κ. Moschonas
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0186039
Subject(s) - uniprot , computer science , gene ontology , normalization (sociology) , data mining , human proteome project , ontology , computational biology , a priori and a posteriori , proteome , data integration , information retrieval , database , bioinformatics , proteomics , gene , biology , genetics , philosophy , gene expression , epistemology , sociology , anthropology
It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE ( P rotein I ntera C tion K now L edgebas E ) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface ( www.pickle.gr ) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.