Prolinks: a database of protein functional linkages derived from coevolution

By P. Bowers, Matteo Pellegrini, Michael J. Thompson, Joe Fierro, Todd O. Yeates, David S. Eisenberg. Genome Biology, 2004

314 citations recorded by the source corpus.

The advent of whole-genome sequencing has led to methods that infer protein function and linkages. We have combined four such algorithms (phylogenetic profile, Rosetta Stone, gene neighbor and gene cluster) in a single database - Prolinks - that spans 83 organisms and includes 10 million high-confidence links. The Proteome Navigator tool allows users to browse predicted linkage networks interactively, providing accompanying annotation from public databases. The Prolinks database and the Proteome Navigator tool are available for use online at http://dip.doe-mbi.ucla.edu/pronav.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7458cd4baf0a68c6edf84b7fd0e22215a89dd8d2

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In Molecular Biology

Citations in the corpus

Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases (2007, methodology): “Another database related to DIP is Prolinks, which brings together four methods of linking proteins: phylogenetic profiles, Rosetta Stone, gene neighbors, and gene clusters[104].”
Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins (2009, result): “Although we could not meaningfully compare our results to an alternate set of putative functional links generated recently [22] because of a lack of publicly accessible dataset scores, we found that less than 5% (3,368) of our predicted interactions are listed in the PROLINKS comparative genomics da”
The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families (2007, methodology): “The probability PrðOg1g2jdistÞ that any two genes g1 from C1 and g2 from C2 are in an operon is dependent on the distance between them as calculated by [47], and is weighted according to the sequence weights w1 and w i 2 described below, for all example pairs i.”
Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners (2007, methodology): “with the genomic inference methods [2] (gene neighbor, gene”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, background): “Databases such as STRING (von Mering et al ., 2007) or Prolinks (Bowers et al ., 2004) compile large sets of functional links across a wide range of organisms.”
The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo (2006, background): “…following: coherence in expression data across subsets of experimental conditions; co-occurrence of putative cis -acting regulatory motifs in the regulatory regions of bicluster members; and the presence of highly connected subgraphs in metabolic [23] and functional association networks [24-26].”
Using phylogenetic profiles to predict functional relationships. (2012, background): “Another implementation of phylogenetic profiles is found in the Prolinks database (20).”
Online Predicted Human Interaction Database (2005, unknown): “Several databases have also been published that make predictions about the functional relationships between proteins based on a variety of in silico methods (Predictome, STRING, Prolinks, POINT) (Bowers et al., 2004; Huang et al., 2004; Mellor et al., 2002; von Mering et al., 2003).”