Computing posterior probabilities for score-based alignments using ppALIGN.

Score-based pairwise alignments are widely used in bioinformatics in particular with molecular database search tools, such as the BLAST family. Due to sophisticated heuristics, such algorithms are usually fast but the underlying scoring model unfortunately lacks a statistical description of the reliability of the reported alignments. In particular, close to gaps, in low-score or low-complexity regions, a huge number of alternative alignments arise which results in a decrease of the certainty of the alignment.

Conotoxin protein classification using free scores of words and support vector machines.

Background: Conotoxin has been proven to be effective in drug design and could be used to treat various disorders such as schizophrenia, neuromuscular disorders and chronic pain. With the rapidly growing interest in conotoxin, accurate conotoxin superfamily classification tools are desirable to systematize the increasing number of newly discovered sequences and structures. However, despite the significance and extensive experimental investigations on conotoxin, those tools have not been intensively explored.

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.

Local sequence alignments statistics: deviations from Gumbel statistics in the rare-event tail.

Background: The optimal score for ungapped local alignments of infinitely long random sequences is known to follow a Gumbel extreme value distribution. Less is known about the important case, where gaps are allowed. For this case, the distribution is only known empirically in the high-probability region, which is biologically less relevant.