gProt: Annotating Protein Interactions Using Google and Gene Ontology

Proteins are one of most significant components in living organism, and their main role in cells is to undertake various physiological functions by interacting with each other. Thus, the prediction of protein-protein interactions (PPIs) is crucial for understanding the molecular basis of biological processes, such as chronic infections. Given the fact that laboratory-based experiments are normally time-consuming and labor-intensive, computational prediction algorithms have become popular at present. However, few of them could simultaneously consider both the structural information of PPI networks and the biological information of proteins for an improved accuracy. To do so, we assume that the prior information of functional modules is known in advance and then simulate the generative process of a PPI network associated with the biological information of proteins, i.e., Gene Ontology, by using an established Bayesian model. In order to indicate to what extent two proteins are likely to interact with each other, we propose a novel scoring function by combining the membership distributions of proteins with network paths. Experimental results show that our algorithm has a promising performance in terms of several independent metrics when compared with state-of-the-art prediction algorithms, and also reveal that the consideration of modularity in PPI networks provides us an alternative, yet much more flexible, way to accurately predict PPIs.

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Tissue-specific mouse mRNA isoform networks

10.1101/558361 ◽

2019 ◽

Author(s):

Gaurav Kandoi ◽

Julie A. Dickerson

Keyword(s):

Gene Ontology ◽

Protein Interactions ◽

Biological Process ◽

Regulation Of Gene Expression ◽

Functional Networks ◽

Sequence Information ◽

Mrna Isoforms ◽

Tissue Specific ◽

Class Labels ◽

Mrna Isoform

AbstractAlternative Splicing produces multiple mRNA isoforms of genes which have important diverse roles such as regulation of gene expression, human heritable diseases, and response to environmental stresses. However, little has been done to assign functions at the mRNA isoform level. Functional networks, where the interactions are quantified by their probability of being involved in the same biological process are typically generated at the gene level. We use a diverse array of tissue-specific RNA-seq datasets and sequence information to train random forest models that predict the functional networks. Since there is no mRNA isoform-level gold standard, we use single isoform genes co-annotated to Gene Ontology biological process annotations, Kyoto Encyclopedia of Genes and Genomes pathways, BioCyc pathways and protein-protein interactions as functionally related (positive pair). To generate the non-functional pairs (negative pair), we use the Gene Ontology annotations tagged with “NOT” qualifier. We describe 17 Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION) following a leave-one-tissue-out strategy in addition to an organism level reference functional network for mouse. We validate our predictions by comparing its performance with previous methods, randomized positive and negative class labels, updated Gene Ontology annotations, and by literature evidence. We demonstrate the ability of our networks to reveal tissue-specific functional differences of the isoforms of the same genes.

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Mitochondrial dysfunction in rheumatoid arthritis: A comprehensive analysis by integrating gene expression, protein-protein interactions and gene ontology data

PLoS ONE ◽

10.1371/journal.pone.0224632 ◽

2019 ◽

Vol 14 (11) ◽

pp. e0224632

Author(s):

Venugopal Panga ◽

Ashwin Adrian Kallor ◽

Arunima Nair ◽

Shilpa Harshan ◽

Srivatsan Raghunathan

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Gene Ontology ◽

Mitochondrial Dysfunction ◽

Protein Interactions ◽

Comprehensive Analysis ◽

Protein Protein Interactions

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Using indirect protein interactions for the prediction of Gene Ontology functions

BMC Bioinformatics ◽

10.1186/1471-2105-8-s4-s8 ◽

2007 ◽

Vol 8 (Suppl 4) ◽

pp. S8 ◽

Cited By ~ 36

Author(s):

Hon Chua ◽

Wing-Kin Sung ◽

Limsoon Wong

Keyword(s):

Gene Ontology ◽

Protein Interactions

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Publisher Correction: Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry

Scientific Reports ◽

10.1038/s41598-018-26708-7 ◽

2018 ◽

Vol 8 (1) ◽

Author(s):

Ravindra Kumar ◽

Sabindra K. Samal ◽

Samapika Routray ◽

Rupesh Dash ◽

Anshuman Dixit

Keyword(s):

Gene Ontology ◽

Oral Cancer ◽

Candidate Genes ◽

Pathway Analysis ◽

Protein Interactions ◽

Protein Protein Interactions

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Identification of potential core genes in Kawasaki disease using bioinformatics analysis

Journal of International Medical Research ◽

10.1177/0300060519862057 ◽

2019 ◽

Vol 47 (9) ◽

pp. 4051-4058

Author(s):

Wenbin Wu ◽

Keyou You ◽

Jinchan Zhong ◽

Zhanwei Ruan ◽

Bubu Wang

Keyword(s):

Gene Expression ◽

Gene Ontology ◽

Kawasaki Disease ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Humoral Response ◽

Hub Genes ◽

Potential Core

Objective The present study aimed to elucidate the underlying pathogenesis of Kawasaki disease (KD) and to identify potential biomarkers for KD. Methods Gene expression profiles for the GSE68004 dataset were downloaded from the Gene Expression Omnibus database. The pathways and functional annotations of differentially expressed genes (DEGs) in KD were examined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Protein–protein interactions of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING). Results Gene Ontology analysis revealed that DEGs in KD were significantly enriched in biological processes, including the inflammatory response, innate immune response, defense response to Gram-positive bacteria, and antibacterial humoral response. In addition, 10 hub genes with high connectivity were selected from among these DEGs ( ITGAM, MPO, MAPK14, SLC11A1, HIST2H2BE, ELANE, CAMP, MMP9, NTS, and HIST2H2AC). Conclusion The identification of several novel hub genes in KD enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with KD. ITGAM inhibitors in particular may be potential targets for KD therapy.

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