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| Main Authors: | , , , , |
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| Format: | Artículo científico |
| Language: | en |
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GigaScience
2024
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| Online Access: | https://pubmed.ncbi.nlm.nih.gov/39460934/ |
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| _version_ | 1868266287734456320 |
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| author | Frazer, Seth A Baghbanzadeh, Mahdi Rahnavard, Ali Crandall, Keith A Oakley, Todd H |
| author_facet | Frazer, Seth A Baghbanzadeh, Mahdi Rahnavard, Ali Crandall, Keith A Oakley, Todd H Frazer, Seth A Baghbanzadeh, Mahdi Rahnavard, Ali Crandall, Keith A Oakley, Todd H |
| collection | PubMed - marine biology |
| contents | Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD). Frazer, Seth A Baghbanzadeh, Mahdi Rahnavard, Ali Crandall, Keith A Oakley, Todd H Machine Learning Opsins Animals Phenotype Databases, Genetic Genetic Association Studies Genotype Humans Mutation Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families, including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax-the wavelength of maximum absorbance-which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype. Here, we report a newly compiled database of all heterologously expressed opsin genes with λmax phenotypes that we call the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains 864 unique opsin genotypes and corresponding λmax phenotypes collected across all animals from 73 separate publications. We use VPOD data and deepBreaks to show regression-based machine learning (ML) models often reliably predict λmax, account for nonadditive effects of mutations on function, and identify functionally critical amino acid sites. The ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism's ecological niche, and may be used more broadly for de novo protein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes. |
| format | Artículo científico |
| id | pubmed_39460934 |
| institution | PubMed |
| language | en |
| publishDate | 2024 |
| publisher | GigaScience |
| record_format | pubmed |
| spellingShingle | Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD). Frazer, Seth A Baghbanzadeh, Mahdi Rahnavard, Ali Crandall, Keith A Oakley, Todd H Machine Learning Opsins Animals Phenotype Databases, Genetic Genetic Association Studies Genotype Humans Mutation Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD). Frazer, Seth A Baghbanzadeh, Mahdi Rahnavard, Ali Crandall, Keith A Oakley, Todd H Machine Learning Opsins Animals Phenotype Databases, Genetic Genetic Association Studies Genotype Humans Mutation Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families, including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including λmax-the wavelength of maximum absorbance-which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype. Here, we report a newly compiled database of all heterologously expressed opsin genes with λmax phenotypes that we call the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains 864 unique opsin genotypes and corresponding λmax phenotypes collected across all animals from 73 separate publications. We use VPOD data and deepBreaks to show regression-based machine learning (ML) models often reliably predict λmax, account for nonadditive effects of mutations on function, and identify functionally critical amino acid sites. The ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organism's ecological niche, and may be used more broadly for de novo protein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes. |
| title | Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD). |
| topic | Machine Learning Opsins Animals Phenotype Databases, Genetic Genetic Association Studies Genotype Humans Mutation |
| url | https://pubmed.ncbi.nlm.nih.gov/39460934/ |