title: Quantifying optimal accuracy of local primary sequence bioinformatics methods.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Physics, Williams College, Williamstown, MA 01267, USA. aalberts@williams.edu
authors.forename: Daniel P
authors.initials: DP
authors.lastname: Aalberts
authors.forename: Eric G
authors.initials: EG
authors.lastname: Daub
authors.forename: Jesse W
authors.initials: JW
authors.lastname: Dill
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D019985:Benchmarking / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D020038:RNA, Spliced Leader / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
chemical_list: D020038:RNA, Spliced Leader