eBiota: ab initio design microbial communities from large-scale seed pools using deep learning and optimization algorithm with microbial community-scale metabolic models

This repository contains the full results of our paper: eBiota: ab initio design microbial communities from large-scale seed pools using deep learning and optimization algorithm with microbial community-scale metabolic models. Authors: Xiaoqing Jiang#, Jiaheng Hou#, Haoyu Zhang#, Jinyuan Guo, Shaohua Gu, Yulin Liao, Xinrun Yang, Peter X. Geng, Yiyan Zhou, Qian Guo, Chunhui Wang, Mo Li, Zhong Wei*, and Huaiqiu Zhu* The results including: (1) GEM.tar.gz: The eBiota-GEM dataset, containing 21,514 Genome-Scale Metabolic Models (GEMs) constructed using CarveMe based on RefSeq complete genomes. (2) Baterial_evaluation.tar.gz: The evaluation of the ability to uptake substrates and secret productions for all 21,514 GEMs. (3) Community_results.tar.gz: The results calculated from eBiota-GEM includes various combinations for two-bacterial consortia, covering strain IDs, substrates, products, yields, dual-bacterial growth, single-bacterial growth, co-occurrence predictions, interactions and total production. (4) DeepCooc_files.tar.gz: The parameters of DeepCooc, required by eBiota platform.