iGEAK: an interactive gene expression analysis kit for seamless workflow using the R/shiny platform

BackgroundThe use of microarrays and RNA-seq technologies is ubiquitous for transcriptome analyses in modern biology. With proper analysis tools, the differential gene expression analysis process can be significantly accelerated. Many open-source programs provide cutting-edge techniques, but these often require programming skills and lack intuitive and interactive or graphical user interfaces. To avoid bottlenecks impeding seamless analysis processing, we have developed an Interactive Gene Expression Analysis Kit, we term iGEAK, focusing on usability and interactivity. iGEAK is designed to be a simple, intuitive, light-weight that contrasts with heavy-duty programs.ResultsiGEAK is an R/Shiny-based client-side desktop application, providing an interactive gene expression data analysis pipeline for microarray and RNA-seq data. Gene expression data can be intuitively explored using a seamless analysis pipeline consisting of sample selection, differentially expressed gene prediction, protein-protein interaction, and gene set enrichment analyses. For each analysis step, users can easily alter parameters to mine more relevant biological information.ConclusioniGEAK is the outcome of close collaboration with wet-bench biologists who are eager to easily explore, mine, and analyze new or public microarray and RNA-seq data. We designed iGEAK as a gene expression analysis pipeline tool to provide essential analysis steps and a user-friendly interactive graphical user interface. iGEAK enables users without programing knowledge to comfortably perform differential gene expression predictions and downstream analyses. iGEAK packages, manuals, tutorials, sample datasets are available at the iGEAK project homepage (https://sites.google.com/view/iGEAK).

[1]  Mikko Koski,et al.  Chipster: user-friendly analysis software for microarray and other high-throughput data , 2011, BMC Genomics.

[2]  Charity W. Law,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[3]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[4]  Claudia Angelini,et al.  RNASeqGUI: a GUI for analysing RNA-Seq data , 2014, Bioinform..

[5]  Chris T. A. Evelo,et al.  User-friendly solutions for microarray quality control and pre-processing on ArrayAnalysis.org , 2013, Nucleic Acids Res..

[6]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Qin Zhu,et al.  PIVOT: platform for interactive analysis and visualization of transcriptomics data , 2016, BMC Bioinformatics.

[8]  Yan Li,et al.  DEApp: an interactive web interface for differential expression analysis of next generation sequence data , 2017, Source Code for Biology and Medicine.

[9]  André Yoshiaki Kashiwabara,et al.  Sequence motif finder using memetic algorithm , 2018, BMC Bioinformatics.

[10]  Piero Carninci,et al.  DEIVA: a web application for interactive visual analysis of differential gene expression profiles , 2017, BMC Genomics.

[11]  Peter F. Edemekong,et al.  Health Insurance Portability and Accountability Act , 2020 .

[12]  Jonathan W. Nelson,et al.  The START App: a web‐based RNAseq analysis and visualization resource , 2016, Bioinform..

[13]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[14]  A. Nekrutenko,et al.  Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences , 2010, Genome Biology.

[15]  Guangchuang Yu,et al.  ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. , 2016, Molecular bioSystems.