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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Identifying functionally informative evolutionary sequence profiles.

Nelson Gil¹, Andras Fiser¹

¹Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA.

Bioinformatics (Oxford, England)

|December 7, 2017

Summary

This summary is machine-generated.

We developed SAMMI, an automated method for selecting optimal multiple sequence alignments (MSAs). SAMMI objectively identifies the most informative MSAs for bioinformatics tasks like protein function prediction.

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Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Multiple sequence alignments (MSAs) are crucial for bioinformatics tasks like protein structure prediction and functional annotation.
Optimal selection of sequences for MSAs is challenging and often manual.
Existing methods lack objective criteria for selecting biologically informative MSAs.

Purpose of the Study:

To introduce SAMMI (Selection of Alignment by Maximal Mutual Information), an automated approach for selecting optimal MSAs.
To provide an objective, sequence-based method for MSA selection.
To improve the quality of MSAs for downstream bioinformatics applications.

Main Methods:

SAMMI utilizes maximal mutual information to select optimal MSAs from a large set of alternatives.
The approach assumes that MSAs with maximal mutual information contain diverse yet structurally and functionally homogeneous protein sequences.
Tested on 435 proteins from protein-ligand and protein-protein interaction databases for functional site residue prediction.

Main Results:

SAMMI objectively selects optimal MSAs based on sequence diversity and homogeneity.
The method demonstrated effectiveness in selecting MSAs for functional site residue prediction.
Analysis of conservation patterns confirmed the biological relevance of SAMMI-selected MSAs.

Conclusions:

SAMMI offers an automated and objective solution for selecting biologically informative MSAs.
This approach enhances the accuracy of bioinformatics applications reliant on MSAs.
The SAMMI program is freely available with source code for broader accessibility.