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CONTRAlign: discriminative training for protein sequence alignment. (English) Zbl 1302.92098
Apostolico, Alberto (ed.) et al., Research in computational molecular biology. 10th annual international conference, RECOMB 2006, Venice, Italy, April 2–5, 2006, Proceedings. Berlin: Springer (ISBN 978-3-540-33295-4/pbk). Lecture Notes in Computer Science 3909. Lecture Notes in Bioinformatics, 160-174 (2006).
Summary: In this paper, we present CONTRAlign, an extensible and fully automatic framework for parameter learning and protein pairwise sequence alignment using pair conditional random fields. When learning a substitution matrix and gap penalties from as few as 20 example alignments, CONTRAlign achieves alignment accuracies competitive with available modern tools. As confirmed by rigorous cross-validated testing, CONTRAlign effectively leverages weak biological signals in sequence alignment: using CONTRAlign, we find that hydropathy-based features result in improvements of 5–6% in aligner accuracy for sequences with less than 20% identity, a signal that state-of-the-art hand-tuned aligners are unable to exploit effectively. Furthermore, when known secondary structure and solvent accessibility are available, such external information is naturally incorporated as additional features within the CONTRAlign framework, yielding additional improvements of up to 15–16% in alignment accuracy for low-identity sequences.
For the entire collection see [Zbl 1116.92001].

92D20 Protein sequences, DNA sequences
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