MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets

Xilin Xu; Aiping Wu; Xinlei Zhang; Mingming Su; Taijiao Jiang; Zhe-Ming Yuan

doi:10.1007/s41048-016-0033-4

Volume 2 Issue 5-6

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Xilin Xu, Aiping Wu, Xinlei Zhang, Mingming Su, Taijiao Jiang, Zhe-Ming Yuan. MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets. Biophysics Reports, 2016, 2(5-6): 106-115. doi: 10.1007/s41048-016-0033-4

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Xilin Xu, Aiping Wu, Xinlei Zhang, Mingming Su, Taijiao Jiang, Zhe-Ming Yuan. MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets. Biophysics Reports, 2016, 2(5-6): 106-115. doi: 10.1007/s41048-016-0033-4

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MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets

doi: 10.1007/s41048-016-0033-4

1 Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University, Changsha 410128, China
2 Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005
Suzhou Institute of Systems Medicine, Suzhou 215123, China
3 Suzhou Geneworks Technology Company Limited, Suzhou 215123, China
4 Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China

Funds: This work was supported by the Science and Technology Planning Projects of Changsha, China (K1406018-21 to ZY).

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Corresponding author: Taijiao Jiang, Zhe-Ming Yuan
Received Date: 05 September 2016
Rev Recd Date: 08 November 2016
Publish Date: 31 August 2016

Abstract

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/).
- Disease prediction,
- 16S rRNA,
- Metagenomics,
- Intestinal bowel syndrome

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References(38)

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