Name: a protocol for using gene set enrichment analysis to identify the appropriate animal model for translational research
Uploaded: 2017-08-16T12:30:00
Description: Watch this Scientific Journal Video about A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research at JoVE.com

这项工作是由德国联邦研究所资助风险评估 (BfR)。

1.下载的 GSEA 软件和分子的签名数据库 <ol><li>去 GSEA 南广学院官网 (http://software.broadinstitute.org/gsea/index.jsp) 和注册，才能进入 GSEA 软件工具和分子签名数据库 (MSigDB)。</li>
	<li>下载 javaGSEA 桌面应用程序或替代软件选项 （例如，R 脚本）。 
	注意： 所有选项都实现完全相同的算法。GSEA 软件是免费提供给个人在学术界和工业界的内部研究目的。</li>
	<li>要进一步 GSEA 软件的详细信息，请?...

<ol>
	<li>Seok, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+responses+in+mouse+models+poorly+mimic+human+inflammatory+diseases.">Genomic responses in mouse models poorly mimic human inflammatory diseases.</a> Proc Natl Acad Sci U S A. 110 (9), 3507-3512 (2013).</li><li>Takao, K., Miyakawa, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+responses+in+mouse+models+greatly+mimic+human+inflammatory+diseases.">Genomic responses in mouse models greatly mimic human inflammatory diseases.</a> Proc Natl Acad Sci U S A. 112 (4), 1167-1172 (2015).</li><li>Weidner, C., Steinfath, M., Opitz, E., Oelgeschl&#228;ger, M., Sch&#246;nfelder, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Defining+the+optimal+animal+model+for+translational+research+using+gene+set+enrichment+analysis.">Defining the optimal animal model for translational research using gene set enrichment analysis.</a> EMBO Mol Med. 8 (8), 831-838 (2016).</li><li>Subramanian, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+set+enrichment+analysis:+a+knowledge-based+approach+for+interpreting+genome-wide+expression+profiles.">Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.</a> Proc Natl Acad Sci U S A. 102 (43), 15545-15550 (2005).</li><li>Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., Tanabe, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KEGG+as+a+reference+resource+for+gene+and+protein+annotation.">KEGG as a reference resource for gene and protein annotation.</a> Nucleic Acids Res. 44 (D1), D457-D462 (2016).</li><li>Kanehisa, M., Goto, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=KEGG:+kyoto+encyclopedia+of+genes+and+genomes.">KEGG: kyoto encyclopedia of genes and genomes.</a> Nucleic Acids Res. 28 (1), 27-30 (2000).</li><li>Fabregat, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Reactome+pathway+Knowledgebase.">The Reactome pathway Knowledgebase.</a> Nucleic Acids Res. 44 (D1), D481-D487 (2016).</li><li>Croft, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Reactome+pathway+knowledgebase.">The Reactome pathway knowledgebase.</a> Nucleic Acids Res. 42 (Database issue), D472-D477 (2014).</li><li>Nishimura, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BioCarta.">BioCarta.</a> Biotech Software &amp; Internet Report. 2 (3), 117-120 (2001).</li><li>Edgar, R., Domrachev, M., Lash, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+Expression+Omnibus:+NCBI+gene+expression+and+hybridization+array+data+repository.">Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.</a> Nucleic Acids Res. 30 (1), 207-210 (2002).</li><li>Kolesnikov, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ArrayExpress+update--simplifying+data+submissions.">ArrayExpress update--simplifying data submissions.</a> Nucleic Acids Res. 43 (Database issue), D1113-D1116 (2015).</li><li>Cohen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sepsis:+a+roadmap+for+future+research.">Sepsis: a roadmap for future research.</a> Lancet Infect Dis. 15 (5), 581-614 (2015).</li><li>Spinelli, L., Carpentier, S., Montanana Sanchis, F., Dalod, M., Vu Manh, T. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BubbleGUM:+automatic+extraction+of+phenotype+molecular+signatures+and+comprehensive+visualization+of+multiple+Gene+Set+Enrichment+Analyses.">BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses.</a> BMC Genomics. 16 (1), 814 (2015).</li></ol>

动物模型长申请疾病机制研究和发展的新的治疗策略。然而，动物模型的真怀疑开始蔓延后临床试验12失败。此外，适当的战略分析和解释临床前试验的大组学数据有争议讨论提出了相反的结论，应用不同的数据分析策略1 后来自相同的数据 ，2。因此，是高需求量进一步健全生物信息学技术复杂的组学数据分析系统定义的最优给定的?...

广泛用于动物模型来研究人类疾病，因为它们对人类在遗传学、 解剖学和生理学方面的假定相似性。此外，动物模型经常作为临床治疗的守门人，可以对转化医学研究的成功产生巨大的影响。谨慎选择最优的动物模型可以减少误导的动物研究的数量。最近，为转化型研究的动物模型的相关性有争议的讨论，特别是因为分析从人类的炎症性疾病和相关的小鼠模型中获得相同的数据集导致相互矛盾的结论1，2。这次讨论组学数据分析期间展示了一个基本的问题： 标准化系统的数据分析方法需减少有偏见的基因选择，增加的物种间比较3的鲁棒性。
传统上，转录组学数据 （和其他组学数据） 分析在级别上进行单基因和包括基因选择基于严格禁产参数初始步骤 (例如，褶皱变化 &#62; 2.0，p 值 &#60; 0.05)。然而，最初截止参数的设置往往是主观的、 任意的和生理上有正当理由，和甚至可以导致相反结论1，2。此外，基因的初步选择一般限于几个高度-和氪基因分析，因此不具有足够的灵敏度，要包括的大多数基因表达的差异程度较轻。
随着基因组学时代在本世纪初的兴起和增长的知识的生物学通路和上下文，替代统计方法被开发，允许绕过单基因水平分析的局限性。基因集富集分析 (GSEA)4，这是被广泛接受的转录组学数据分析方法之一，利用先验定义组的基因 （例如，信号转导通路，近端位置对染色体等.）。GSEA 首先映射所有检测到未筛选的基因到预期的基因集 （例如，路径），不论其个人的转变，在表达式中。因此，这种方法还包括适度下调的基因，否则会丢失与单基因水平的分析。在基因组内的表达式中的添加剂更改随后可使用运行总和统计信息。
尽管其广泛应用在医学研究中，GSEA 和相关设置的浓缩方法不不言而喻考虑复杂的组学数据分析。在这里，我们描述协议比较组学数据来自于人体样本与那些从小鼠模型以确定翻译研究的理想模式。我们展示协议基于集合用于模仿人类的炎性疾病的小鼠模型的适用性。然而，这个分析管道不局限于人-鼠比较和修正对进一步研究的问题。

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a protocol for using gene set enrichment analysis to identify the appropriate animal model for translational research

最近的研究表明人类疾病转录组数据集与数据集从小鼠模型使用传统基因到基因比较技术导致相互矛盾的结论关于相关性的动物模型的转化研究。不同的基因表达分析差异的主要原因是任意筛选差异表达基因。此外，单个基因不同物种和经常的平台之间的比较被受到限制技术的差异，导致人类与动物模型的数据之间的误解 con/不一致。因此，需要系统的数据分析的标准化的方法。为了克服主观基因筛选和无效基因到基因比较，我们最近表明基因集的富集分析 (GSEA)，潜力来避免这些问题。因此，我们制定的 GSEA 来区分合适与不合适的动物模型，为转化型研究使用标准化的协议。本议定书不适于预测如何设计新模型系统是-先验的因为它需要现有实验组学数据。但是，议定书 》 描述了如何选择最合适的动物模型，从而避免不必要的动物实验和误导翻译研究以标准化的方式解释现有的数据。

论证了的 GSEA 工作流和模范数据的屏幕截图。图 1显示了包含感兴趣的转录组数据的基因表达数据文件。为每一个描述性的表型文件是必修课，是图 2中所示。带注释的基因集 （例如，通路） 基因设置的数据库文件 (图 3) 中定义。图 4显示了分步协议 GSEA 软件工具的使用。<stro...

Watch this Scientific Journal Video about A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research at JoVE.com

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

我们提供一个标准化的协议用于基因集的浓缩转录组数据分析，找出理想的小鼠模型的转化研究。 该协议可以用 DNA 微阵列和 RNA 测序数据，并且可以进一步扩展到其他组学数据如果数据可用。

一个协议来使用基因集富集分析以确定合适的动物模型转化型研究

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison ...

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