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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Pareto optimal pairwise sequence alignment.

Kevin W DeRonne¹, George Karypis

¹Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|August 10, 2013

Summary

This summary is machine-generated.

This study introduces an efficient algorithm for generating Pareto optimal pairwise alignments using multiple profile scoring functions. The new method significantly speeds up the process while yielding high-quality protein alignments.

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Area of Science:

Bioinformatics
Computational Biology
Structural Bioinformatics

Background:

Protein sequence alignment is crucial for understanding protein function and evolution.
Evolutionary profiles are widely used in protein sequence alignment.
Existing profile-profile scoring functions lack comprehensive Pareto optimal analysis for combined use.

Purpose of the Study:

To develop an efficient algorithm for generating Pareto optimal pairwise alignments by combining multiple profile scoring functions.
To investigate the optimal substructure property in generating Pareto optimal pairwise alignments.
To evaluate the performance of combined scoring functions compared to single functions.

Main Methods:

Developed a dynamic-programming-based heuristic algorithm to generate approximated Pareto optimal frontiers.
Utilized all combinations of two, three, and four profile scoring functions from a pool of 11.
Tested the algorithm on the ce_ref dataset (588 protein pairs) and validated on the BAliBASE RV11 dataset (913 protein pairs).

Main Results:

The heuristic algorithm efficiently generates approximated Pareto optimal frontiers, achieving comparable results to exact frontiers.
The approach significantly reduces computation time, especially for four objectives (over 58 times faster on average).
Alignments within Pareto frontiers demonstrate superior quality compared to those from single objective functions.

Conclusions:

The developed algorithm provides an efficient method for exploring Pareto optimal pairwise alignments.
Combining multiple profile scoring functions enhances alignment quality.
Identifying the single best alignment from a Pareto frontier remains a challenge.