Integrated gene set analysis for microRNA studies.

Integrated gene set analysis for microRNA studies.
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DOI:
10.1093/bioinformatics/btw334
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发表时间:
2016-09-15
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
通讯作者:
Montaner D
Montaner D
中科院分区:
其他
文献类型:
--
作者:
Garcia-Garcia F;Panadero J;Dopazo J;Montaner D

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动机:目前,对miRNA表达数据的功能解释分三步进行:选择差异表达的miRNA,找到它们的靶基因,并进行基因组过度表达分析。尽管如此,这种方法的主要局限性已经在基因水平上描述过,而一些新的局限性出现在miRNA场景中。在这里,我们提出了一个增强的方法,建立在完善的基因集分析范式。在miRNA水平上差异表达的证据被转移到基因差异抑制评分中,该评分可以根据基因组或途径容易地解释。这种转移的指数解释了靶向同一基因的几种miRNA的累加效应,并且还包括病例和对照之间的抵消效应。这两个理想的特征共同允许对监管过程进行更准确的建模。结果如下:我们分析了来自20种不同癌症类型的高通量测序数据,并提供了通过miRNA作用的基因和基因本体论术语失调的详尽报告。可用性和实施:在Bioconductor图书馆mdgsa中实施了拟议的方法。http://bioconductor.org/packages/mdgsa.出于重现性目的,所有脚本均可在https://github.com/dmontaner-papers/gsa4mirna上获得,联系人:大卫. gmail.com补充信息:补充数据可在Bioinformatics online上获得。
Motivation: Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis. Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario. Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes. Results: We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action. Availability and Implementation: The proposed methodology was implemented in the Bioconductor library mdgsa. http://bioconductor.org/packages/mdgsa. For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna Contact: david.montaner@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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DOI: 10.1111/j.2517-6161.1995.tb02031.x
发表时间: 1995-01-01
影响因子: 5.8
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