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Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine
Flavio Pazos Obregón1,2, Diego Silvera3, Pablo Soto3
1Departamento de Biología del Neurodesarrollo, Instituto de Investigaciones Biológicas Clemente Estable, Av. Italia 3318, 11600, Montevideo, Uruguay. fpazos@iibce.edu.uy.
Gene location can predict function in eukaryotes. Machine learning models using only gene location features outperformed sequence-based methods like BLAST for predicting gene functions in model organisms.
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Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Most gene functions remain unknown, hindering biological research.
- Current gene function prediction methods often rely on sequence data, but gene location is also a strong indicator of function.
- Existing location-based methods have limitations due to their reliance on sequence identity.
Purpose of the Study:
- To investigate the predictive power of gene location for function prediction in eukaryotes.
- To develop and evaluate machine learning models trained exclusively on genomic location features.
- To compare the performance of location-based models against sequence-based methods.
Main Methods:
- Utilized machine learning models trained solely on features derived from gene locations within genomes.
- Applied models to predict gene functions across five model eukaryotic species: yeast, C. elegans, D. melanogaster, M. musculus, and H. sapiens.
- Compared model performance against the BLAST algorithm for predicting gene functions.
Main Results:
- Gene location features alone were sufficient to predict thousands of gene functions across model eukaryotes.
- The developed location-based models outperformed BLAST in predicting terms within the Biological Process and Cellular Component Gene Ontology categories.
- Demonstrated that gene location can be a more effective predictor of function than sequence in certain contexts.
Conclusions:
- Genomic gene location is a significant and underutilized feature for automated gene function prediction.
- Location-based machine learning approaches offer a powerful alternative or complement to sequence-based methods.
- This study highlights the potential of leveraging genomic context for advancing functional genomics.