Skip Navigation


Bioinformatics Advance Access originally published online on January 3, 2007
Bioinformatics 2007 23(4):515-516; doi:10.1093/bioinformatics/btl637
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
23/4/515    most recent
btl637v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (1)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Pandini, A.
Right arrow Articles by Kleinjung, J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Pandini, A.
Right arrow Articles by Kleinjung, J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

MinSet: a general approach to derive maximally representative database subsets by using fragment dictionaries and its application to the SCOP database

Alessandro Pandini 1, Laura Bonati 1, Franca Fraternali 2 and Jens Kleinjung 3,*

1 Dipartimento di Scienze dell'Ambiente e del Territorio, Università degli Studi di Milano-Bicocca Milano, Italy
2 Bioinformatics Unit, King's College London, UK
3 Division of Mathematical Biology, National Institute for Medical Research The Ridgeway, London NW7 1AA, UK

*To whom correspondence should be addressed.


  Abstract

Motivation: The size of current protein databases is a challenge for many Bioinformatics applications, both in terms of processing speed and information redundancy. It may be therefore desirable to efficiently reduce the database of interest to a maximally representative subset.

Results: The MinSet method employs a combination of a Suffix Tree and a Genetic Algorithm for the generation, selection and assessment of database subsets. The approach is generally applicable to any type of string-encoded data, allowing for a drastic reduction of the database size whilst retaining most of the information contained in the original set. We demonstrate the performance of the method on a database of protein domain structures encoded as strings. We used the SCOP40 domain database by translating protein structures into character strings by means of a structural alphabet and by extracting optimized subsets according to an entropy score that is based on a constant-length fragment dictionary. Therefore, optimized subsets are maximally representative for the distribution and range of local structures. Subsets containing only 10% of the SCOP structure classes show a coverage of >90% for fragments of length 1–4.

Availability: http://mathbio.nimr.mrc.ac.uk/~jkleinj/MinSet

Contact: jkleinj{at}nimr.mrc.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Charlie Hodgman

Received on September 12, 2006; revised on December 11, 2006; accepted on December 12, 2006

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.