Human protein reference database as a discovery resource for proteomics

By Suraj Peri, J. Navarro, Troels Kristiansen, Ramars Amanchy, Vineeth Surendranath, B. Muthusamy, T. K. Gandhi, K. N. Chandrika, Nandan P. Deshpande, Shubha Suresh and 23 more. Nucleic Acids Res., 2004

635 citations recorded by the source corpus.

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Citations in the corpus

Walking the interactome for prioritization of candidate disease genes. (2008, methodology): “These datasets comprise interactions extracted from HPRD, 20 BIND, 21 and BioGrid 22 .”
Comparing Statistical Methods for Constructing Large Scale Gene Networks (2012, methodology): “In the simulation studies, the network structures were simulated based on the real protein-protein interaction networks [38,39], Figure 1.”
Reactome: a knowledgebase of biological pathways (2004, unknown): “This will also enable data exchange with other pathway databases, such as the Cycs (7), KEGG (8) and amaze (9), and molecular interaction databases, such as BIND (10) and HPRD (11).”
Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database (2011, methodology): “The workflow utilizes 6 major steps: (1) Accessing the most recent release of the Swiss-Prot database and its controlled vocabulary of PTMs; (2) Preprocessing of the PTM IDs; (3) Populating the experimental and putative statistics using logical conditions; (4) Sorting and categorizing the results; (”
Disease candidate gene identification and prioritization using protein interaction networks (2009, methodology): “This compilation is based on three interaction databases, namely, BIND (2389 genes and 4054 interactions) [44], BioGRID (7683 genes, 23205 interactions) [45], and HPRD (6594 genes and 22802 interactions) [46] (See Additional File 1 for the overlap among these three resources).”
Human Protein Reference Database—2009 update (2008, background): “Human Protein Reference Database (HPRD; http:// www.hprd.org/) is a resource for experimentally derived information about the human proteome including protein–protein interactions, post-translational modiﬁca-tions (PTMs) and tissue expression (1–4).”
Uncovering Biological Network Function via Graphlet Degree Signatures (2008, methodology): “In a graphlet G i , i ∈ { 0 , 1 ,... 29 } , nodes belonging to the same orbit are of the same shade (Prˇzulj, 2006). orbit i (i.e., this orbit is of the highest importance), we invert this scaled value of orbit dependencies as to assign the highest weight of 1 to orbit i with o i = 1 .”
SMART 5: domains in the context of genomes and networks (2005, background): “The data are derived from a variety of sources, including knowledge bases, such as BIND (8), KEGG (9), HPRD (10) and Reactome (11), as well as in silico pre45diction approaches and automated text-mining.”
Biological network comparison using graphlet degree distribution (2007, unknown): “Note that these PPI networks come from of a wide array of experimental techniques; for example, YHC and Y11K are mainly coming from tandem affinity purifications (TAP) and high throughput MS/MS protein complex identification (HMS-PCI), while YIC, YU, YICU,FE, FH, WE, WH, HS, and HG are yeast two-hyb”
Associating Genes and Protein Complexes with Disease via Network Propagation (2010, methodology): “We constructed a human PPI network with 9998 proteins and 41072 interactions that were assembled from three large scale experiments [22–24] and the Human Protein Reference Database (HPRD) [25].”
MPact: the MIPS protein interaction resource on yeast (2005, background): “The second messenger pathway and phosphatidylinositol signaling system in KEGG are represented by 17 and 9 proteins, respectively; 14 and 6 of those are found in our dataset.”
Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. (2006, methodology): “, 2004) and the Human Proteome Reference Database (Peri et al., 2004) and have uploaded previous phosphoproteome data sets to Phosida.”