Nucleic Acids Research Advance Access originally published online on October 28, 2008
Nucleic Acids Research 2009 37(Database issue):D738-D743; doi:10.1093/nar/gkn758
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Nucleic Acids Research, 2009, Vol. 37, Database issue D738-D743
Published by Oxford University Press 2008
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article appears in the following Nucleic Acids Research issue: Database issue [View the issue table of contents]
Articles |
Pseudofam: the pseudogene families database
1Program in Computational Biology and Bioinformatics, 2Department of Molecular Biophysics and Biochemistry, 3Department of Computer Science, 4Center for Medical Informatics and 5Department of Genetics, Yale University, New Haven, CT 06520, USA
*To whom correspondence should be addressed. Tel: +1 203 432 6105; Fax: +1 360 838 7861; Email: Mark.Gerstein{at}yale.edu
Received August 15, 2008. Revised October 4, 2008. Accepted October 6, 2008.
Pseudofam (http://pseudofam.pseudogene.org) is a database of pseudogene families based on the protein families from the Pfam database. It provides resources for analyzing the family structure of pseudogenes including query tools, statistical summaries and sequence alignments. The current version of Pseudofam contains more than 125 000 pseudogenes identified from 10 eukaryotic genomes and aligned within nearly 3000 families (approximately one-third of the total families in PfamA). Pseudofam uses a large-scale parallelized homology search algorithm (implemented as an extension of the PseudoPipe pipeline) to identify pseudogenes. Each identified pseudogene is assigned to its parent protein family and subsequently aligned to each other by transferring the parent domain alignments from the Pfam family. Pseudogenes are also given additional annotation based on an ontology, reflecting their mode of creation and subsequent history. In particular, our annotation highlights the association of pseudogene families with genomic features, such as segmental duplications. In addition, pseudogene families are associated with key statistics, which identify outlier families with an unusual degree of pseudogenization. The statistics also show how the number of genes and pseudogenes in families correlates across different species. Overall, they highlight the fact that housekeeping families tend to be enriched with a large number of pseudogenes.