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RankerGUI:一种基于排名的统计方法比较差异基因表达谱的计算框架。

RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics.

机构信息

High-Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, 80131 Napoli, Italy.

Department of Medicine, Immunology and Allergy Unit, Karolinska Institutet, 171 76 Stockholm, Sweden.

出版信息

Int J Mol Sci. 2019 Dec 3;20(23):6098. doi: 10.3390/ijms20236098.


DOI:10.3390/ijms20236098
PMID:31816915
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6929103/
Abstract

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the "RankerGUI pipeline", a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms' data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

摘要

比较不同实验条件下获得的高通量基因表达数据集是一项具有挑战性的任务。它提供了一个机会来探索细胞对各种生物事件(如疾病、环境条件和药物)的反应。需要有工具来允许整合和分析这些数据。我们开发了“RankerGUI 管道”,这是一个面向生物界的用户友好的 Web 应用程序。它允许用户使用各种基于排名的统计方法来比较来自不同来源的相同或不同生物状态的全差异基因表达谱。该管道模块集成了各种开源软件包,其中一些经过修改以扩展功能。主要模块包括排名超几何重叠、富集排名超几何重叠和距离计算。此外,在运行主模块之前,可以添加合并多个独立研究的差异表达谱等预处理步骤。输出图显示了完整差异表达谱之间的强度、模式和趋势。在本文中,我们描述了开发的管道的各个模块和功能。我们还展示了一个案例研究,演示了如何使用该管道比较来自基因表达综合数据库多个平台数据的差异表达谱。使用这些比较,我们研究了肾脏和肺癌中的基因表达模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/1839b16fd109/ijms-20-06098-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/acbe5e4fc5c1/ijms-20-06098-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/fc04ec1c2fd5/ijms-20-06098-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/215b00391e55/ijms-20-06098-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/bfc0ffd595a6/ijms-20-06098-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/5b5770a4c8b8/ijms-20-06098-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/1839b16fd109/ijms-20-06098-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/acbe5e4fc5c1/ijms-20-06098-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/fc04ec1c2fd5/ijms-20-06098-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/215b00391e55/ijms-20-06098-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/bfc0ffd595a6/ijms-20-06098-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/5b5770a4c8b8/ijms-20-06098-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/655f/6929103/1839b16fd109/ijms-20-06098-g006.jpg

相似文献

[1]
RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics.

Int J Mol Sci. 2019-12-3

[2]
Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures.

Nucleic Acids Res. 2010-9

[3]
Towards precise classification of cancers based on robust gene functional expression profiles.

BMC Bioinformatics. 2005-3-17

[4]
AtCAST3.0 update: a web-based tool for analysis of transcriptome data by searching similarities in gene expression profiles.

Plant Cell Physiol. 2015-1

[5]
Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis.

Brief Funct Genomics. 2015-3

[6]
Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data.

BMC Genomics. 2019-8-27

[7]
CANEapp: a user-friendly application for automated next generation transcriptomic data analysis.

BMC Genomics. 2016-1-13

[8]
Transcriptator: An Automated Computational Pipeline to Annotate Assembled Reads and Identify Non Coding RNA.

PLoS One. 2015-11-18

[9]
Guide: a desktop application for analysing gene expression data.

BMC Genomics. 2013-10-7

[10]
FunPat: function-based pattern analysis on RNA-seq time series data.

BMC Genomics. 2015

引用本文的文献

[1]
RedRibbon: A new rank-rank hypergeometric overlap for gene and transcript expression signatures.

Life Sci Alliance. 2024-2

[2]
GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data.

PeerJ Comput Sci. 2023-11-15

[3]
Cancer Progression Gene Expression Profiling Identifies the Urokinase Plasminogen Activator Receptor as a Biomarker of Metastasis in Cutaneous Squamous Cell Carcinoma.

Front Oncol. 2022-4-11

[4]
miRNA Expression Signatures of Therapy Response in Squamous Cell Carcinomas.

Cancers (Basel). 2020-12-28

本文引用的文献

[1]
RNA-Seq differential expression analysis: An extended review and a software tool.

PLoS One. 2017-12-21

[2]
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Nucleic Acids Res. 2017-7-3

[3]
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Nucleic Acids Res. 2013-4-24

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Bioinformatics. 2011-7-6

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ASCL1-coexpression profiling but not single gene expression profiling defines lung adenocarcinomas of neuroendocrine nature with poor prognosis.

Lung Cancer. 2011-7-6

[10]
Discovery of drug mode of action and drug repositioning from transcriptional responses.

Proc Natl Acad Sci U S A. 2010-8-2

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