Quantifying optimal accuracy of local primary sequence bioinformatics methods.

Daniel P Aalberts; Eric G Daub; Jesse W Dill

doi:10.1093/bioinformatics/bti521

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Quantifying optimal accuracy of local primary sequence bioinformatics methods.

Daniel P Aalberts·Eric G Daub·Jesse W Dill

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Abstract

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.

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Algorithms Base Sequence Benchmarking Computational Biology Computer Simulation Models, Genetic Models, Statistical Molecular Sequence Data RNA, Spliced Leader Reproducibility of Results Sensitivity and Specificity Sequence Alignment Sequence Analysis, RNA Software Evaluation Study Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.

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Daniel P Aalberts, Eric G Daub, Jesse W Dill. 2005-05-27. Quantifying optimal accuracy of local primary sequence bioinformatics methods.. https://doi.org/10.1093/bioinformatics%2Fbti521

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