Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD) | bioRxiv

Background 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. Results Here, we report a newly compiled database of all heterologously expressed opsin genes with λmax phenotypes called the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD) | bioRxiv Skip to main content New Results Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD) View ORCID Profile Seth A. Frazer , View ORCID Profile Mahdi Baghbanzadeh , View ORCID Profile Ali Rahnavard , View ORCID Profile Keith A. Crandall , View ORCID Profile Todd H. Oakley doi: https://doi.org/10.1101/2024.02.12.579993 Seth A. Frazer 1 Ecology, Evolution, and Marine Biology, University of California, Santa Barbara

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