MicroPhenoDB associates metagenomic data with pathogenic microbes, microbial core genes, and human disease phenotypes
Guocai YaoiD, Wenliang ZhangiD, Minglei YangiD, Huan YangiD, Jianbo WangiD, Haiyue ZhangiD, Lai WeiiD, Zhi XieiD, Weizhong LiiD
Abstract
Microbes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Disease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. MicroPhenoDB offers data browsing, searching and visualization through user-friendly web interfaces and web service application programming interfaces. MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases. MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 51
- FWCI
- 1.78
- Field percentile
- top 14%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach
Sandra Dérozier, Robert Bossy, … Claire Nédellec·PLoS ONEPeer-reviewed·Jan 20, 2023
- PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research
Şenay Kafkas, Marwa Abdelhakim, … Robert Hoehndorf·Scientific DataPeer-reviewed·Jun 3, 2019
- MicrobiomeDB: a systems biology platform for integrating, mining and analyzing microbiome experiments.
F. Silva de Oliveira, J. Brestelli, … D. P. Beiting·bioRxiv·Aug 16, 2017
- Meta Analysis Of Microbiome Studies Identifies Shared And Disease-Specific Patterns
C. Duvallet, S. M. Gibbons, … E. J. Alm·bioRxiv·May 8, 2017
- Evaluating trait-based sets for taxonomic enrichment analysis applied to human microbiome data sets
Quang P. Nguyen, Anne Gatewood Hoen, Hildreth Robert Frost·bioRxiv·May 16, 2022
- Meta2DB: Curated Shotgun Metagenomic Feature Sets and Metadata for Health State Prediction
Car Reen Kok, Nisha J Mulakken, … Nicholas A. Be·BioinformaticsPeer-reviewed·Jul 1, 2026