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SVM-dependent pairwise HMM: an application to protein pairwise alignments.

Gabriele Orlando^1,2,3,4, Daniele Raimondi^1,2,3,4, Taushif Khan^1,2

¹Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.

Bioinformatics (Oxford, England)

|July 2, 2017

Summary

Rigapollo is a flexible pairwise alignment method that uses a hidden Markov model-support vector machine (HMM-SVM) to align proteins. This novel approach outperforms existing methods for highly divergent protein sequences.

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Area of Science:

Bioinformatics
Computational Biology
Protein Science

Background:

Reliable protein alignments are essential for bioinformatics.
Current methods struggle with highly divergent protein sequences.
Feature importance varies across protein classes, necessitating customizable approaches.

Purpose of the Study:

To develop a flexible pairwise protein alignment method.
To enable customizable alignment strategies based on user-defined features.
To improve alignment accuracy for divergent protein sequences.

Main Methods:

Development of Rigapollo, a pairwise alignment method.
Utilizing a pairwise hidden Markov model-support vector machine (HMM-SVM).
Incorporating user-defined features for alignment construction.

Main Results:

Rigapollo demonstrates high flexibility in incorporating diverse information types.
The method outperforms state-of-the-art techniques on benchmark datasets.
Achieved superior performance specifically for highly divergent protein sequences.

Conclusions:

Rigapollo offers a customizable and effective solution for protein alignment.
The HMM-SVM approach enhances alignment accuracy for challenging datasets.
This method advances bioinformatics applications requiring precise protein sequence comparison.