Abstract
Efficient family classification of newly discovered protein sequences is a central problem in bioinformatics. We present a new algorithm, using Probabilistic Suffix Trees, which identifies equivalences between the amino acids in different positions of a motif for each family. We also show that better classification can be achieved identifying representative fingerprints in the amino acid chains.
This work is partially supported by CAPES and is part of PRONEX/FAPESP’s Project Stochastic behavior, critical phenomena and rhythmic pattern identification in natural languages (grant number 03/09930-9).
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Leonardi, F., Galves, A. (2005). Sequence Motif Identification and Protein Family Classification Using Probabilistic Trees. In: Setubal, J.C., Verjovski-Almeida, S. (eds) Advances in Bioinformatics and Computational Biology. BSB 2005. Lecture Notes in Computer Science(), vol 3594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11532323_20
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DOI: https://doi.org/10.1007/11532323_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28008-8
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