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bioRxiv · 10.1101/2020.07.29.221010

MicroPhenoDB associates metagenomic data with pathogenic microbes, microbial core genes, and human disease phenotypes

Yao, G.·Zhang, W.·Yang, M.·Yang, H.·Wang, J.·Zhang, H.·Wei, L.·Xie, Z.·Li, W.
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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.

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Yao, G., Zhang, W., Yang, M., Yang, H., Wang, J., Zhang, H., Wei, L., Xie, Z., Li, W.. 2020-07-29. MicroPhenoDB associates metagenomic data with pathogenic microbes, microbial core genes, and human disease phenotypes. https://doi.org/10.1101/2020.07.29.221010

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