Human Protein Reference Database—2009 update

By Goparani Mishra, M. Suresh, K. Kumaran, N. Kannabiran, Shubha Suresh, Dr.Pronay Bala, K. Shivakumar, N. Anuradha, Raghunath Reddy, T. M. Raghavan and 33 more. 2008

1,581 citations recorded by the source corpus.

Human Protein Reference Database (HPRD—http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system—Human Proteinpedia (http://www.humanproteinpedia.org/)—through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15 000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.

Subfields: Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

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Citations in the corpus

Predicting multicellular function through multi-layer tissue networks (2017, methodology): “We retrieved the mapping of tissues in the Human Protein Reference Database (HPRD) (Prasad et al. , 2009) to tissues in the BRENDA Tissue Ontology (Chang et al. , 2014) from Greene et al. (2015).”
Biomedical text mining and its applications in cancer research (2013, neutral): “HPRD [150–152] The Human Protein Reference Database (HPRD) is a platform for human protein interaction networks and disease association.”
Deep learning in cancer diagnosis, prognosis and treatment selection (2021, methodology): “The study used gene expression profiles, structured by a PPI from Human Protein Reference Database (HPRD) [121], to predict metastasis of breast cancer samples.”
Differential network biology (2012, neutral): “Large network databases such as BioGRID, HPRD, IntAct, DIPand GeneMania (Xenarios et al , 2002; Keshava Prasad et al , 2009; Aranda et al , 2010; Warde-Farley et al , 2010; Stark et al , 2011) record hundreds of thousands of physical and genetic interactions from a wide variety of organisms.”
AnimalTFDB: a comprehensive animal transcription factor database (2011, methodology): “The protein–protein interaction information was parsed from BioGRID (15), HPRD (16) and An atlas of human and mouse TF interactions (17) databases.”
The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored (2010, background): “Annotated pathway knowledge is mostly kept in a separate set of resources (22–24) and yet other interactions can be found in various organismspecific databases (25,26) or text-mining resources (27,28).”
Network-based in silico drug efficacy screening (2016, positive): “Wetookthehuman protein–protein interaction (PPI) network compiled by Menche et al. 12 that contained experimentally documented human physical interactions from TRANSFAC 38 , IntAct 39 , MINT 40 , BioGRID 41 , HPRD 42 , KEGG 43 , BIGG 44 , CORUM 45 , PhosphoSitePlus 46 and a large scale signalling ne”
PHOSIDA 2011: the posttranslational modification database (2010, neutral): “ELM (12), PhosphoSite (13), HPRD (14) and dbPTM (15) are further comprehensive databases that contain phosphorylation sites from different projects.”
Algorithms for Detecting Significantly Mutated Pathways in Cancer (2010, positive): “We use the protein interaction network from the Human Protein Reference Database (June 2008 version) [22] which consists of 18796 vertices and 37107 edges.”
Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes (2012, methodology): “In fact, for the ten other cancer outcome studies we investigated [5,13,33,52–58], a TRANSFAC-based network gave the best results in seven, and a HPRD-based network in three of the studies.”