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A practical data processing workflow for multi-OMICS projects

机译:多OMICS项目的实用数据处理工作流程

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摘要

Multi-OMICS approaches aim on the integration of quantitative data obtained for different biological molecules in order to understand their interrelation and the functioning of larger systems. This paper deals with several data integration and data processing issues that frequently occur within this context. To this end, the data processing workflow within the PROFILE project is presented, a multi-OMICS project that aims on identification of novel biomarkers and the development of new therapeutic targets for seven important liver diseases. Furthermore, a software called CrossPlatformCommander is sketched, which facilitates several steps of the proposed workflow in a semi-automatic manner. Application of the software is presented for the detection of novel biomarkers, their ranking and annotation with existing knowledge using the example of corresponding Transcriptomics and Proteomics data sets obtained from patients suffering from hepatocellular carcinoma. Additionally, a linear regression analysis of Transcriptomics vs. Proteomics data is presented and its performance assessed. It was shown, that for capturing profound relations between Transcriptomics and Proteomics data, a simple linear regression analysis is not sufficient and implementation and evaluation of alternative statistical approaches are needed. Additionally, the integration of multivariate variable selection and classification approaches is intended for further development of the software. Although this paper focuses only on the combination of data obtained from quantitative Proteomics and Transcriptomics experiments, several approaches and data integration steps are also applicable for other OMICS technologies. Keeping specific restrictions in mind the suggested workflow (or at least parts of it) may be used as a template for similar projects that make use of different high throughput techniques. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
机译:Multi-OMICS方法旨在整合从不同生物分子获得的定量数据,以了解它们之间的相互关系和大型系统的功能。本文讨论了在这种情况下经常发生的几种数据集成和数据处理问题。为此,提出了PROFILE项目中的数据处理工作流程,这是一个多OMICS项目,旨在识别新型生物标志物并开发针对7种重要肝病的新治疗靶标。此外,草绘了一个名为CrossPlatformCommander的软件,该软件以半自动方式简化了所建议工作流程的几个步骤。介绍了该软件的应用,以检测新的生物标志物,使用已有肝癌患者的相应转录组学和蛋白质组学数据集为例,利用现有知识对它们进行排名和注释。此外,提出了转录组学与蛋白质组学数据的线性回归分析,并评估了其性能。结果表明,为了捕捉转录组学和蛋白质组学数据之间的深刻关系,简单的线性回归分析是不够的,需要替代统计方法的实施和评估。另外,多变量选择和分类方法的集成旨在进一步开发软件。尽管本文仅关注从定量蛋白质组学和转录组学实验获得的数据的组合,但几种方法和数据集成步骤也适用于其他OMICS技术。牢记特定的限制,建议的工作流程(或其至少一部分)可以用作使用不同高吞吐量技术的类似项目的模板。本文是名为“后识别时代的计算蛋白质组学”的特刊的一部分。客座编辑:马丁·埃塞纳赫和克里斯蒂安·斯蒂芬。

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