Predictome: a database of putative functional links between proteins.
The current deluge of genomic sequences has spawned the creation of tools capable of making sense of the data. Computational and high-throughput experimental methods for generating links between proteins have recently been emerging. These methods effectively act as hypothesis machines, allowing researchers to screen large sets of data to detect interesting patterns that can then be studied in greater detail. Although the potential use of these putative links in predicting gene function has been demonstrated, a central repository for all such links for many genomes would maximize their usefulness. Here we present Predictome, a database of predicted links between the proteins of 44 genomes based on the implementation of three computational methods--chromosomal proximity, phylogenetic profiling and domain fusion--and large-scale experimental screenings of protein-protein interaction data. The combination of data from various predictive methods in one database allows for their comparison with each other, as well as visualization of their correlation with known pathway information. As a repository for such data, Predictome is an ongoing resource for the community, providing functional relationships among proteins as new genomic data emerges. Predictome is available at http://predictome.bu.edu.
Mellor
Joseph C
JC
Bioinformatics Graduate Program and Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
Yanai
Itai
I
Clodfelter
Karl H
KH
Mintseris
Julian
J
DeLisi
Charles
C
D000818:Animals; D019677:Artificial Gene Fusion; D002874:Chromosome Mapping; D030562:Databases, Protein; D005544:Forecasting; D016678:Genome; D016247:Information Storage and Retrieval; D020407:Internet; D046911:Macromolecular Substances; D010802:Phylogeny; D011506:Proteins / Q000235:genetics* / Q000502:physiology*; D018511:Systems Integration
D046911:Macromolecular Substances; D011506:Proteins