BioMGCore: A toolkit for detection of biological metabolites in microbiome
The keyword matching and gene proximity principles were used to accurately identify core gene clusters in microbial genomes. The metabolic gene clusters can be classified into different taxonomic groups according to Domain, Phylum, Class, Order, Family, Genus, and Species. BioMGCore can achieve batch statistics for secondary metabolites prediction.
Authors
- Shulei Jia (ORCID: https://orcid.org/0000-0003-2483-0300)
- Guang Yang (ORCID: https://orcid.org/0000-0003-1036-8397)
- Qian Cui (ORCID: https://orcid.org/0000-0003-1236-7523)
- Guohao Yu (ORCID: https://orcid.org/0000-0002-6921-2728)
- Xueping Huang (ORCID: https://orcid.org/0000-0003-2803-2277)
- Kexin Guo (ORCID: https://orcid.org/0000-0002-0459-5027)
Institutions
- Tianjin Medical University (CN)
- College of Marin (US)
Publication Details
- Journal
- iMetaOmics.
- Published
- 2025-06-22
- DOI
- https://doi.org/10.1002/imo2.70036
- Citations
- 2
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- Government of Jiangsu Province
- Natural Science Foundation of Tianjin City
- Tianjin Medical University
- Priority Academic Program Development of Jiangsu Higher Education Institutions