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A Practical Guide to Phylogenetics for Nonexperts

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SIBIS: a Bayesian model for inconsistent protein sequence estimation.

Walyd Khenoussi¹, Renaud Vanhoutrève¹, Olivier Poch¹

¹Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle, Strasbourg, F-67085, France.

Bioinformatics (Oxford, England)

|May 15, 2014

Summary

We developed SIBIS, a new method to detect errors in protein databases using evolutionary information. SIBIS significantly improves accuracy in identifying inconsistent protein sequences, crucial for reliable biological data analysis.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Protein coding gene prediction is challenging due to genome sequencing quality, gene structure elucidation models, and complex splicing.
Protein databases contain inconsistencies from natural variants and sequence prediction errors.

Purpose of the Study:

To develop a novel method, SIBIS, for detecting inconsistencies in protein sequences.
To improve the accuracy of protein sequence quality control.

Main Methods:

Utilized evolutionary information from multiple sequence alignments.
Employed a Bayesian framework with Dirichlet mixture models to estimate amino acid probabilities.
Developed SIBIS to identify inconsistent or erroneous sequence segments.

Main Results:

SIBIS demonstrated significantly higher sensitivity compared to previous methods for detecting validated errors.
A small loss in specificity was observed.
Analysis of UniProt human sequences revealed inconsistencies in 48% of uncharacterized sequences.

Conclusions:

SIBIS effectively detects protein sequence inconsistencies.
Integrating quality control methods like SIBIS is critical for robust inference of protein structural, functional, and phylogenetic information.
The method aids in improving the reliability of protein databases.