代謝體系統生物學分析與工具及脂質知識庫

Abstract

藉由腫瘤缺氧代謝體分析確認過往由轉錄體分析所得到認知，透過整合轉錄體與代謝體可擴展現有知識。藉由整合跨體學分析，瞭解整合性分析軟體需要廣泛相關工具知識與背景知識，因此開發了系統生物學工具3Omics讓使用者可快速進行整合性分析。完成整合性分析後，為了解決生物詮釋遇到的問題，開發了脂質百科以加速耗費大量人力閱讀文獻過程，提供脂質之生醫相關資訊。 目前針對腫瘤缺氧整體性分析仍然侷限於單一體學，以系統生物學結合多個體學資料能增進我們對生物系統的知識。因此我們以代謝體與文獻中轉錄體探討時序性變化，以提供生物系統應對腫瘤缺氧之系統層面視角。從代謝體分析、轉錄體學與代謝體學代謝途徑分析結果確認了過往轉錄體學認知，擴展現有腫瘤缺氧已知模型上的知識，同時也呈現了整合性分析效果。 藉由整合腫瘤缺氧狀況下之轉錄體與代謝體，顯示整合性分析需要相當廣泛的工具與相關知識及許多背景觀念。因此我們開發系統生物學工具3Omics，藉由提供五種常用分析，快速整合三種人類轉錄體、蛋白體學、代謝體學資料，進行跨體學整合、或單一體學內分析。當使用者輸入三種中兩種資料，3Omics會以文字探勘PubMed資料庫方式以補充未提供之資料。3Omics簡化整合分析流程，讓使用者可執行一鍵式整合分析。 研究者完成整合性系統生物學分析後，接著步驟則是生物學詮釋。為解釋生物學詮釋目前所遇到的問題，以脂質體分析作為範例。從單一實驗中即可偵測出上百種脂質表現與變化。因同一種脂質類別內之脂質具有高度化學結構以及名稱相似性，使得研究者難以了解所偵測得之脂質功能或相關疾病。因此研究者需要以脂質名稱搜尋並閱讀相關文獻，才能找出相關知識。為了加速此耗費大量人力閱讀文獻過程，我們藉由基於UMLS系統開發脂質百科，使用文字探勘期刊文獻，獲取並提供脂質之生醫相關資訊。

The majority of researches of tumor hypoxia have been limited to just single omics level. Combining multiple omics data by systems biology approaches can improve our knowledge in biological systems. Here, we investigated the temporal changes of the metabolome with transcriptome from literature to provide a more comprehensive insight on the system level in response to hypoxia. Pathway analysis of metabolomic and transcriptomic data confirm understandings from previous transcriptomic analyses and expand the knowledge from existing models between transcriptome and metabolome of tumor hypoxia demonstrating the power of integrated omics analysis. Through integrating omics data of tumor hypoxia, it is known that integrating multiple omics data requires an intensive knowledge of tools and background concepts. 3Omics was developed to rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining commonly used analyses. If only two of three omics datasets are input, 3Omics supplements the missing transcript, protein or metabolite information related to the input by text-mining PubMed database. 3Omics simplifies data analysis and enabling users to perform a one-click integrated analysis. After researchers finished the integrated analyses of systems biology, the next step is the biological interpretation. To explain the problem of biological interpretation, lipidomics analyses are used as an example. Hundreds of lipid species can be detected in a single experiment. It is hard to know the associated functions or diseases of each lipid in the lipidomics studies for the highly similar name and chemical structure of each lipid class. Researchers need to find and read associated references from searching names of the lipid. To accelerate the labor-intensive process of reading references, we developed LipidPedia for providing associated biomedical information by using text-mining strategy in literatures based on UMLS.