Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling

Background: Molecular database search tools need statistical models to assess the significance for the resulting hits. In the classical approach one asks the question how probable a certain score is observed by pure chance. Asymptotic theories for such questions are available for two random i.i.d. sequences. Some effort had been made to include effects of finite sequence lengths and to account for specific compositions of the sequences. In many applications, such as a large-scale database homology search for transmembrane proteins, these models are not the most appropriate ones. Search sensitivity and specificity benefit from position-dependent scoring schemes or use of Hidden Markov Models. Additional, one may wish to go beyond the assumption that the sequences are i.i.d. Despite their practical importance, the statistical properties of these settings have not been well investigated yet. Results: In this paper, we discuss an efficient and general method to compute the score distribut...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Stefan Wolfsheimer, Inke Herms, Sven Rahmann, Alexander K. Hartmann
• Keywords: Artificial Intelligence, BMCBI 2011, Pure Chance, Sequences, Transmembrane Proteins