GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis

被引:3
|
作者
Helmy, Mohamed [1 ,2 ]
Agrawal, Rahul [3 ]
Ali, Javed [3 ]
Soudy, Mohamed [4 ]
Bui, Thuy Tien [1 ]
Selvarajoo, Kumar [1 ,5 ,6 ]
机构
[1] ASTAR, Bioinformat Inst BII, Singapore, Singapore
[2] Lakehead Univ, Dept Comp Sci, Thunder Bay, ON, Canada
[3] Indian Inst Technol IIT Kharagpur, Dept Geol & Geophys, Kharagpur, India
[4] Children Canc Hosp CCHE 57357, Prote & Metabol Unit, Cairo, Egypt
[5] ASTAR, Singapore Inst Food & Biotechnol Innovat SIFBI, Singapore, Singapore
[6] Natl Univ Singapore NUS, Synthet Biol Clin & Technol Innovat SynCTI, Singapore, Singapore
来源
FRONTIERS IN BIOINFORMATICS | 2021年 / 1卷
关键词
OMICS data; gene expression analysis; bioinformatics; microarray; RNA-seq; transcriptomics; data analytics; RNA-SEQ ANALYSIS; ENRICHMENT ANALYSIS; SINGLE CELLS; VISUALIZATION; PACKAGE;
D O I
10.3389/fbinf.2021.693836
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists.
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页数:14
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