Name
Title | ||
|---|---|---|
3PFDB - A database of Best Representative PSSM Profiles (BRPs) of Protein Families generated using a novel data mining approach. |
Abstract | ||
|---|---|---|
BACKGROUND: Protein families could be related to each other at broad levels that group them as superfamilies. These relationships are harder to detect at the sequence level due to high evolutionary divergence. Sequence searches are strongly directed and influenced by the best representatives of families that are viewed as starting points. PSSMs are useful approximations and mathematical representations of protein alignments, with wide array of applications in bioinformatics approaches like remote homology detection, protein family analysis, detection of new members and evolutionary modelling. Computational intensive searches have been performed using the neural network based sensitive sequence search method called FASSM to identify the Best Representative PSSMs for families reported in Pfam database version 22. RESULTS: We designed a novel data mining approach for the assessment of individual sequences from a protein family to identify a single Best Representative PSSM profile (BRP) per protein family. Using the approach, a database of protein family-specific best representative PSSM profiles called 3PFDB has been developed. PSSM profiles in 3PFDB are curated using performance of individual sequence as a reference in a rigorous scoring and coverage analysis approach using FASSM. We have assessed the suitability of 10, 85,588 sequences derived from seed or full alignments reported in Pfam database (Version 22). Coverage analysis using FASSM method is used as the filtering step to identify the best representative sequence, starting from full length or domain sequences to generate the final profile for a given family. 3PFDB is a collection of best representative PSSM profiles of 8,524 protein families from Pfam database. CONCLUSION: Availability of an approach to identify BRPs and a curated database of best representative PSI-BLAST derived PSSMs for 91.4% of current Pfam family will be a useful resource for the community to perform detailed and specific analysis using family-specific, best-representative PSSM profiles. 3PFDB can be accessed using the URL: http://caps.ncbs.res.in/3pfdb |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1186/1756-0381-2-8 | BioData mining |
Keywords | Field | DocType |
data mining,bioinformatics,biomedical research,neural network,protein family | Data mining,Protein family,Computer science,Homology (biology),Bioinformatics,Artificial neural network,Database | Journal |
Volume | Issue | ISSN |
2 | 1 | 1756-0381 |
Citations | PageRank | References |
6 | 0.49 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khader Shameer | 1 | 80 | 5.14 |
| Paramasivam Nagarajan | 2 | 6 | 0.82 |
| Kumar Gaurav | 3 | 10 | 0.95 |
| Ramanathan Sowdhamini | 4 | 215 | 21.20 |