Quantifying optimal accuracy of local primary sequence bioinformatics methods

• Autor: Aalberts, Daniel P. ; Daub, Eric G. ; Dill, Jesse W.
• Assuntos: Algorithms ; Base Sequence ; Benchmarking - methods ; Bioinformatics ; Biological and medical sciences ; Computational Biology - methods ; Computer Simulation ; Fundamental and applied biological sciences. Psychology ; General aspects ; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) ; Models, Genetic ; Models, Statistical ; Molecular Sequence Data ; Reproducibility of Results ; RNA, Spliced Leader - genetics ; Sensitivity and Specificity ; Sequence Alignment - methods ; Sequence Analysis, RNA - methods ; Software
• É parte de: Bioinformatics, 2005-08, Vol.21 (16), p.3347-3351
• Notas: istex:5FD4B8A79C548DD40B763400E9197EE43F2B2C53
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• Descrição: 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@williams.edu, +1-413-597-3520 Supplementary information: Supplementary data for this paper are available on Bioinformatics online.
  
• Editor: Oxford: Oxford University Press
  
• Idioma: Inglês