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Survey of main tools for querying and analyzing TCGA Data

机译:调查和分析TCGA数据的主要工具

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The development of high-throughput methods for genome interrogation, such as microarrays and next-generation sequencing (NGS) has led to significant progress in the field of cancer genomics. However, the major obstacle to the extraction of knowledge is the fragmentation of the oncogenomics datasets, sourcing from the various genomic data repositories, into a multitude of different file formats and data types. An important advance in cancer genomics is represented by The Cancer Genome Atlas (TCGA) project, a coordinated effort to provide a comprehensive catalog of biomedical data about cancer. Although several tools for querying and analyzing TCGA data are developed and publicly available, they are often hard to use and the integration across data sets and data types remains limited. Moreover, researchers who want to combine these heterogeneous data often are forced to use several complementary tools lacking of interoperability. The contribution of this survey is double: to provide the researchers with an overview of the main technical and functional features of the most popular and innovative tools for querying and analyzing TCGA data and in addition to make available an easy to use guideline that helps the researchers in the choice of the tools best suited to their needs, hence focusing their efforts on the research goals rather than on the technical issues.
机译:用于基因组询问的高通量方法的发展,例如微阵列和下一代测序(NGS),已导致癌症基因组学领域的重大进展。但是,知识提取的主要障碍是肿瘤基因组学数据集的碎片化,这些数据集是从各种基因组数据存储库中采购到多种不同的文件格式和数据类型中的。癌症基因组图谱(TCGA)项目代表了癌症基因组学的一项重要进步,该项目旨在提供有关癌症生物医学数据的全面目录。尽管已经开发了多种用于查询和分析TCGA数据的工具并公开可用,但它们通常难以使​​用,并且跨数据集和数据类型的集成仍然受到限制。此外,想要合并这些异构数据的研究人员常常被迫使用缺乏互操作性的几种互补工具。该调查的贡献是双重的:为研究人员提供了用于查询和分析TCGA数据的最流行和创新工具的主要技术和功能特征的概述,此外还提供了易于使用的指南,可帮助研究人员选择最适合他们需求的工具,从而将他们的工作重点放在研究目标上,而不是技术问题上。

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