A generalization of the PST algorithm: modeling the sparse nature of protein sequences

Bioinformatics
Florencia G Leonardi

Abstract

A central problem in genomics is to determine the function of a protein using the information contained in its amino acid sequence. Variable length Markov chains (VLMC) are a promising class of models that can effectively classify proteins into families and they can be estimated in linear time and space. We introduce a new algorithm, called Sparse Probabilistic Suffix Trees (SPST), that identifies equivalence between the contexts of a VLMC. We show that, in many cases, the identification of these equivalence can improve the classification rate of the classical Probabilistic Suffix Trees (PST) algorithm. We also show that better classification can be achieved by identifying representative fingerprints in the amino acid chains, and this variation in the SPST algorithm is called F-SPST.

References

Jun 1, 1995·Protein Science : a Publication of the Protein Society·W R Pearson
Dec 7, 2000·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·A Apostolico, G Bejerano
Jan 31, 2004·Bioinformatics·Gill Bejerano

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Citations

Nov 22, 2008·PLoS Computational Biology·Nicolas MaubourguetPhilippe Faure
Aug 31, 2011·Biochemical and Biophysical Research Communications·Hasan OğulMahinur S Akkaya
Apr 21, 2019·Bioinformatics·Fabio CunialDjamal Belazzougui
Feb 12, 2021·Scientific Reports·Noslen HernándezClaudia D Vargas

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