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Xu X, Wu A, Zhang X, et al. MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets. Biophys Rep. 2017;2(5):106-115doi: 10.1007/s41048-016-0033-4.
Xu, X., Wu, A., Zhang, X., Su, M., Jiang, T., & Yuan, Z. M. (2016). MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets. Biophysics reports, 2(5), 106-115. https://doi.org/10.1007/s41048-016-0033-4
Xu, Xilin, et al. "MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets." Biophysics reports vol. 2,5 (2016): 106-115. doi: https://doi.org/10.1007/s41048-016-0033-4
Xu X, Wu A, Zhang X, Su M, Jiang T, Yuan ZM. MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets. Biophys Rep. 2016;2(5):106-115. doi: 10.1007/s41048-016-0033-4. Epub 2017 Jan 10. PMID: 28317014; PMCID: PMC5334392.
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Biophys Rep. 2016;2(5):106-115. doi: 10.1007/s41048-016-0033-4. Epub 2017 Jan 10.

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

Biophysics reports

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

Affiliations

  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.

PMID: 28317014 PMCID: PMC5334392 DOI: 10.1007/s41048-016-0033-4

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/).

Keywords: 16S rRNA; Disease prediction; Intestinal bowel syndrome; Metagenomics

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