Local sequence alignments with monotonic gap penalties.

Journal article

MOTIVATION: Sequence alignments obtained using affine gap penalties are not always biologically correct, because the insertion of long gaps is over-penalised. There is a need for an efficient algorithm which can find local alignments using non-linear gap penalties. RESULTS: A dynamic programming algorithm is described which computes optimal local sequence alignments for arbitrary, monotonically increasing gap penalties, i.e. where the cost g(k) of inserting a gap of k symbols is such that g(k) >/= g(k-1). The running time of the algorithm is dependent on the scoring scheme; if the expected score of an alignment between random, unrelated sequences of lengths m, n is proportional to log mn, then with one exception, the algorithm has expected running time O(mn). Elsewhere, the running time is no greater than O(mn(m+n)). Optimisations are described which appear to reduce the worst-case run-time to O(mn) in many cases. We show how using a non-affine gap penalty can dramatically increase the probability of detecting a similarity containing a long gap. AVAILABILITY: The source code is available to academic collaborators under licence.

Authors: Mott, R

• Publication status: Published
• Journal: Bioinformatics (Oxford, England)
• Volume: 15
• Issue: 6
• Pages: 455-462
• Publication date: 1999-06-01
• DOI: 10.1093/bioinformatics/15.6.455
• EISSN: 1367-4811
• ISSN: 1367-4803
• Language: English
• Keywords: Algorithms; Software; Sensitivity and Specificity; Computational Biology; Evaluation Studies as Topic; Sequence Alignment