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

Biophysics Reports, Dec 2016

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/​).

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

MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets Xilin Xu 1 2 Aiping Wu 1 Xinlei Zhang 0 Mingming Su 3 Taijiao Jiang 1 Zhe-Ming Yuan 2 0 Suzhou Geneworks Technology Company Limited , Suzhou 215123 , China 1 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 2 Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University , Changsha 410128 , China 3 Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College , Beijing 100005 , China 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 - 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). To date, there is still significant uncertainty about the relationships between resident microbes and human diseases. Xilin Xu, Aiping Wu have contributed equally to this work. Most microorganisms in the human body have remained uncultured. Therefore, traditional methods for the inspection and identification of the microbial species have significant limitations. In 1998, Handelsman et al. first put forward the concept of the ‘‘metagenome’’ (Handelsman et al. 1998), and defined it as the genes and genomes of all of the microorganisms in an environmental sample. With the rapid development of highthroughput sequencing technology and the establishment of numerous microbial databases, metagenomics has become an emerging topic of interest in biomedical research. Recently, 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 that obesity was associated with phylum-level change in the microbiota and reduction of bacterial diversity (Turnbaugh et al. 2009). Pushalkar et al. studied five saliva microbial samples and found fifteen unique phylotypes in three oral squamous cell carcinoma subjects (Pushalkar et al. 2011). The relationships between microorganisms and some other diseases have also been investigated, such as oral diseases (Belda-Ferre et al. 2012), neurological diseases (Hsiao et al. 2013), rheumatoid arthritis (Scher et al. 2013), and Crohn’s disease (Gevers et al. 2014). Furthermore, some computational models have been constructed for disease classification and prediction based on metagenomic data. Qin et al. analyzed the differences between type 2 diabetes (T2D) patients and non-diabetic controls in 345 Chinese gut microbial samples. The researchers chose 50 gene markers to develop a T2D classifier model and used it for risk assessment and monitoring of T2D (Qin et al. 2012). Saulnier et al. compared the gut microbiomes of healthy children and pediatric patients with irritable bowel syndrome (IBS), and found some differences in the microbial communities in these two sample sets, which might suggest a novel technique for the diagnosis of pediatric patients with functional bowel disorders (Saulnier et al. 2011). Moreover, Qin et al. developed a support vector machine (SVM) model and indicated that microbiota-targeted biomarkers may serve as new tools for disease diagnoses (Qin et al. 2014). These prediction models indicate that metagenomics data can perhaps play an important role in the prevention and early diagnosis of disease. Although numerous tools and methods have been developed to investigate the relationship between microbes and human diseases, there is still an absence of a general automated workflow from raw data to disease prediction. Some metagenomic data analysis tools, such as QIIME (Caporaso et al. 2010a, b), mother (Schl (...truncated)


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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, pp. 106-115, Volume 2, Issue 5-6, DOI: 10.1007/s41048-016-0033-4