Systematic comparison of the protein-protein interaction databases from a user's perspective

Bajpai, Akhilesh Kumar; Davuluri, Sravanthi; Tiwary, Kriti; Narayanan, Sithalechumi; Oguru, Sailaja; Basavaraju, Kavyashree; Dayalan, Deena; Thirumurugan, Kavitha; Acharya, Kshitish K.

doi:10.1016/j.jbi.2020.103380

Cited by 75 publications

(55 citation statements)

References 37 publications

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“…The STRING combined score indicates approximate confidence of data support reporting by seven channels, including three genomic context prediction channels, namely gene neighborhoods, cooccurrence, and fusions, further to coexpression, experiments, databases, and text‐mining channels. Thereby, the STRING is considered as a useful and valuable tool for static PPI analysis owing to its comprehensive and up‐to‐date data, user‐friendly web‐server, and consistent scoring system 32,34,38 . All seven channels of the STRING were selected for analysis.…”

Section: Methodsmentioning

confidence: 99%

RAB5A is associated with genes involved in exosome secretion: Integration of bioinformatics analysis and experimental validation

Gorji-Bahri

Moghimi

Hashemi

2020

J of Cellular Biochemistry

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Exosomes, as cell–cell communicators with an endosomal origin, are involved in the progression of various diseases. RAB5A, a member of the small Rab GTPases family, which is well known as a key regulator of cellular endocytosis, is expected to be involved in exosome secretion. Here, we found the impact of RAB5A on exosome secretion from human hepatocellular carcinoma cell line using a rapid yet reliable bioinformatics approach followed by experimental analysis. Initially, RAB5A and exosome secretion‐related genes were gathered from bioinformatics tools, namely, CTD, COREMINE, and GeneMANIA; and published papers. Protein–protein interaction (PPI) was then constructed by the Search Tool for Retrieval of Interacting Genes (STRING) database. Among them, several genes with different combined scores were validated by the real‐time quantitative polymerase chain reaction (RT‐qPCR) in stable RAB5A knockdown cells. Thereafter, to validate the bioinformatics results functionally, the impact of RAB5A knockdown on exosome secretion was evaluated. Bioinformatics analysis showed that RAB5A interacts with 37 genes involved in exosome secretion regulatory pathways. Validation by RT‐qPCR confirmed the association of RAB5A with candidate interacted genes and interestingly showed that even medium to low combined scores of the STRING database could be experimentally valid. Moreover, the functional analysis demonstrated that the stable silencing of RAB5A could experimentally decrease exosome secretion. In conclusion, we suggest RAB5A as a regulator of exosome secretion based on our bioinformatics approach and experimental analysis. Also, we propose the usage of PPI‐derived from the STRING database regardless of their combined scores in advanced bioinformatics analysis.

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Section: Methodsmentioning

confidence: 99%

RAB5A is associated with genes involved in exosome secretion: Integration of bioinformatics analysis and experimental validation

Gorji-Bahri

Moghimi

Hashemi

2020

J of Cellular Biochemistry

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“…A recent study comparing PPI databases found 375 resources in their literature search and observed that PubMed timeline data indicated a steady increase in research articles on PPIs across the years ( Bajpai et al, 2020 ). Resources such as the Human Protein Reference Database (HPRD) ( Peri et al, 2004 ; Keshava Prasad et al, 2009 ), the Biological General Repository for Interaction Datasets (BioGRID) ( Stark et al, 2006 ), and the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) ( Szklarczyk et al, 2019 ) are incredibly useful resources that enable the analysis of interaction data in various contexts.…”

Section: Introductionmentioning

confidence: 99%

Measuring pathway database coverage of the phosphoproteome

Huckstep

Fearnley

Davis

2021

PeerJ

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Protein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. However, data saturation is occurring and the bottleneck of assigning biologically relevant functionality to phosphosites needs to be addressed. There has been finite success in using data-driven approaches to reveal phosphosite functionality due to a range of limitations. The alternate, more suitable approach is making use of prior knowledge from literature-derived databases. Here, we analysed seven widely used databases to shed light on their suitability to provide functional insights into phosphoproteomics data. We first determined the global coverage of each database at both the protein and phosphosite level. We also determined how consistent each database was in its phosphorylation annotations compared to a global standard. Finally, we looked in detail at the coverage of each database over six experimental datasets. Our analysis highlights the relative strengths and weaknesses of each database, providing a guide in how each can be best used to identify biological mechanisms in phosphoproteomic data.

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“…4C,D) (Szklarczyk et al 2019). We found no overlap with four other PPI datasets, BioGRID, HIPPIE, HINT and a yeast two-hybrid dataset, reflecting the modest size of the RH-Seq network (Alanis-Lobato, Schaefer 2020; Bajpai et al 2020;Das, Yu 2012;Luck et al 2020;Oughtred et al 2021).…”

Section: Network Overlapsmentioning

confidence: 84%

“…Protein-protein interactions (PPIs) have been identified using a variety of technologies, including yeast two hybrid assays and immunoprecipitation-mass spectrometry (Luck et al 2020;Yugandhar et al 2019). Although these approaches are resource intensive, most human PPIs have been evaluated using large experimental efforts and cataloged in publicly available databases (Bajpai et al 2020;Luck et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Cost-effective mapping of genetic interactions in mammalian cells

Smith

2021

Preprint

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Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≤ $50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

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Systematic comparison of the protein-protein interaction databases from a user's perspective

Cited by 75 publications

References 37 publications

RAB5A is associated with genes involved in exosome secretion: Integration of bioinformatics analysis and experimental validation

RAB5A is associated with genes involved in exosome secretion: Integration of bioinformatics analysis and experimental validation

Measuring pathway database coverage of the phosphoproteome

Cost-effective mapping of genetic interactions in mammalian cells

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