COMPARTMENTS: unification and visualization of protein subcellular localization evidence

By Janos X. Binder, Sune Frankild, Kalliopi Tsafou, C. Stolte, S. O’Donoghue, Reinhard Schneider, Lars Juhl Jensen. Database J. Biol. Databases Curation, 2014

612 citations recorded by the source corpus.

Information on protein subcellular localization is important to understand the cellular functions of proteins. Currently, such information is manually curated from the literature, obtained from high-throughput microscopy-based screens and predicted from primary sequence. To get a comprehensive view of the localization of a protein, it is thus necessary to consult multiple databases and prediction tools. To address this, we present the COMPARTMENTS resource, which integrates all sources listed above as well as the results of automatic text mining. The resource is automatically kept up to date with source databases, and all localization evidence is mapped onto common protein identifiers and Gene Ontology terms. We further assign confidence scores to the localization evidence to facilitate comparison of different types and sources of evidence. To further improve the comparability, we assign confidence scores based on the type and source of the localization evidence. Finally, we visualize the unified localization evidence for a protein on a schematic cell to provide a simple overview. Database URL: http://compartments.jensenlab.org

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/26696df6cce2a89a53f3af4335bb3b474842e35e

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Citations in the corpus

The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest (2022, methodology): “In parallel with the network prediction, STRING also attempts to assign the submitted proteins to their corresponding pathways and functional subsystems, as imported for STRING from the three Gene Ontology branches (Biological process, Molecular Function and Cellular Component) (43), KEGG pathways (”
The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible (2016, background): “If available for the organism in question, the App also fetches information on the subcellular localization and tissue expression of each protein from the COMPARTMENTS (47) and TISSUES (48) databases as well as drug target information from Pharos (http://pharos.nih.gov/).”
DISEASES: Text mining and data integration of disease–gene associations (2014, methodology): “In addition to using it for disease–gene associations as described here, we have since applied the same scoring scheme to extract information on protein–small molecule associations in the STITCH database [48], protein subcellular localization in the COMPARTMENTS database [40], and tissue distributio”
MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins (2015, background): “COMPARTMENTS (46) lacks a downloadable list of mitochondrial genes, but for any query gene it provides a confidence score of localization to multiple cellular compartments (e.”
CCRK is a novel signalling hub exploitable in cancer immunotherapy (2018, neutral): “142 tor 7; AKT, AKT serine/threonine kinase; AR, androgen receptor; ARE, androgen response element; ARG1, arginase 1; ATM, ataxia related protein; BMDC, mouse bonemarrow-derived dendritic cell; CAK, CDK-activating kinase; CaMKV, CaM kinase like vesicle asCREB binding protein; CCND1, cyclin D1; CCRK,”
MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations (2020, background): “0), while the COMPARTMENTS database version 2020-08-31 (32) reported 1296 genes with strong mitochondrial evidence by an integrated score exceeding 4 (909 of which are in MitoCarta3.”
UniProt: a worldwide hub of protein knowledge (2018, methodology): “org/) (28,30) combined with protein location data from UniProt (expert annotation, rulebased automatic annotation) and imported from GO annotation.”
The in silico human surfaceome (2018, background): “The positive training set was composed of human ɑ-helical TM domain-containing proteins appearing in at least two of the following three datasets: (i) the “high confidence” subset of the CSPA containing 735 proteins, (ii) the UniProtKB/Swiss-Prot (Version 2015_01) containing 2,043 proteins attribute”