Abstract
High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).
Highlights
A wide variety of microbes live in the human body
We developed a web-based platform called MetaDP, in which an automated analysis workflow was built for 16S rRNA sequences generated by both the 454 and Illumina platforms
MetaDP provides predefined workflows and can be used without registration. It begins with a straightforward process whereby a user uploads sequencing data
Summary
A wide variety of microbes live in the human body. These microbes exist in oral, nasopharynx, skin, gut, and many other regions of the body and play an important role in human health (Human Microbiome Project 2012; Sankar et al 2015). There is still significant uncertainty about the relationships between resident microbes and human diseases. Most microorganisms in the human body have remained uncultured. Traditional methods for the inspection and identification of the microbial species have significant limitations. Multiple metagenomics studies have revealed that microbial communities are associated with human diseases. Turnbaugh et al characterized the gut microbial communities of 154 individuals and found
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