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Bioinformatics Advance Access originally published online on May 27, 2005
Bioinformatics 2005 21(16):3347-3351; doi:10.1093/bioinformatics/bti521
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Quantifying optimal accuracy of local primary sequence bioinformatics methods

Daniel P. Aalberts *, Eric G. Daub and Jesse W. Dill

Department of Physics, Williams College Williamstown, MA 01267, USA

*To whom correspondence should be addressed.

Motivation: Traditional bioinformatics methods scan primary sequences for local patterns. It is important to assess how accurate local primary sequence methods can be.

Results: We study the problem of donor pre-mRNA splice site recognition, where the sequence overlaps between real and decoy datasets can be quantified, exposing the intrinsic limitations of the performance of local primary sequence methods. We assess the accuracy of primary sequence methods generally by studying how they scale with dataset size and demonstrate that our new primary sequence ranking methods have superior performance.

Availability: Our primary sequence ranking analysis tools are available at http://rna.williams.edu/

Contact: aalberts{at}williams.edu, +1-413-597-3520

Supplementary information: Supplementary data for this paper are available on Bioinformatics online.

Received on October 27, 2004; revised on May 25, 2005; accepted on May 25, 2005

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