3.8 Article

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

Journal

FRONTIERS IN BIOINFORMATICS
Volume 1, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fbinf.2021.693836

Keywords

OMICS data; gene expression analysis; bioinformatics; microarray; RNA-seq; transcriptomics; data analytics

Funding

  1. The authors thank Derek Smith for comments. The web interface was partially funded by BII core budget, IAF-PP grant to SIFBI, A*STAR and partially through the Google Summer of Code (GSoC'20 and '21) programs to RA, MS, and JA for the National Resourc [GSoC'20]
  2. BII core budget
  3. JA for the National Resources for Network Biology (NRNB), United States

Ask authors/readers for more resources

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have significantly impacted biomedical research. GeneCloudOmics is a user-friendly web server that extends the functionality of previous tools, allowing rapid execution of various bioinformatics tasks with comprehensive data analytics tools.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

3.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available