Original protein sequence data of various bacteria for a novel machine learning-based method of bacterial shape identification

Accurately identifying genes responsible for specific functions is a cornerstone of biological research, but current methods are often limited to single-species analyses. Here, we present a novel method termed "Genomic and Phenotype-based machine learning for Gene Identification" (GPGI), which leverages large-scale cross-species genomic and phenotypic data for functional gene discovery. The uploaded dataset comprises the complete protein sequences encoded by all 3,750 bacterial species used in the early stages of this method. These sequences were obtained from NCBI RefSeq and have been deposited in accordance with the journal's requirements to facilitate access for other interested researchers.