The Construction Of Metagenomic And Proteomic Data Analysis Tool

High-throughput sequencing technology is a leaping enhancement for biomedical research,and it is a revolutionary change in terms of traditional medicine and disease research,especially it plays an important role in the development and popularization of personalized medicine.This paper is mainly based on two types of high throughput data,introduces the different application of biomedical data in disease research.A comprehensive web server for disease prediction of 16S rRNA metagenomic datasets: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(OTU)clustering,diversity analysis,and disease risk prediction modeling.Furthermore,a support vector machine(SVM)-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/).Prognostic Biomarker Analysis of Ovarian Cancer Based on Proteomic Data:Ovarian cancer has the highest mortality among gynecological malignant tumors.Most ovarian patients are found in the advanced stage and have low 5-year survival rates,therefore,it is of great significance to find effective and accurate prognostic biomarkers for the treatment and prognosis of ovarian cancer.When analyzed 174 proteomic samples of ovarian cancer by various bioinformatics methods,13 protein markers were identified.Functional enrichment analysis indicated that these protein markers were significantly associated with PPAR pathway and cell adhesion process.Applying the 13 markers to multiple ovarian cancer datasets for prognosis assessment,these all showed stable and good predictive effects.All the results revealed,a set of 13 reliable biomarkers were found,and could effectively predict the prognosis and help to improve the survival quality of patients with ovarian cancer.