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Biosom: gene synonym analysis by self-organizing map.

K R Otemaier¹, M B R Steffens¹, R T Raittz²

¹Programa de Pós-Graduação em Bioinformática, Universidade Federal do Paraná, Curitiba, PR, Brasil.

Genetics and Molecular Research : GMR

|March 3, 2015

Summary

This study introduces a novel methodology to identify gene synonyms and reduce naming inconsistencies in biological databases. This approach enhances gene annotation and data mining by minimizing errors and standardizing gene names.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene nomenclature guidelines exist but are inconsistently applied to new gene discoveries.
Lack of standardization leads to database errors like duplicate gene entries with different names, distinct genes with identical names, and ambiguous abbreviations.
These inconsistencies hinder accurate gene annotation and effective data mining in public biological databases.

Purpose of the Study:

To present a methodology for predicting gene synonyms and detecting naming redundancy and inconsistency.
To facilitate the annotation of newly identified genes and improve data mining in public databases.
To address issues of gene ambiguity and standardize gene naming conventions.

Main Methods:

Utilizes a Kohonen self-organizing map artificial neural network to group genes based on their names.
Employs the Matrix-U technique to identify and analyze the generated gene groups.
Applies these computational techniques to infer synonyms and predict potential gene names.

Main Results:

Successfully detected numerous errors related to gene nomenclature in existing databases.
Demonstrated the effectiveness of the methodology in identifying gene ambiguity and synonyms.
The approach can infer synonyms, predict hypothetical gene names, and highlight database record errors.

Conclusions:

The developed methodology effectively predicts gene synonyms, thereby minimizing naming redundancy and inconsistency.
This approach is valuable for annotating hypothetical and putative genes and can suggest functions for uncharacterized genes.
Standardizing gene nomenclature through synonym prediction is crucial for maintaining accurate and reliable biological databases.