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Combines a graphical user interface (GUI) and an automated server-side analysis pipeline that is platform-independent, making it suitable for any server architecture. The GUI runs on a PC or Mac and seamlessly connects to the server to provide full GUI control of RNA-sequencing (RNA-seq) project analysis. The server-side analysis pipeline contains a framework that is implemented on a Linux server through completely automated installation of software components and reference files. Analysis with CANEapp is also fully automated and performs differential gene expression analysis and novel noncoding RNA discovery through alternative workflows (Cuffdiff and R packages edgeR and DESeq2). CANEapp adapts to any server architecture by effectively using available resources and thus handles large amounts of data efficiently. We believe that CANEapp will serve both biologists with no computational experience and bioinformaticians as a simple, timesaving but accurate and powerful tool to analyze large RNA-seq datasets and will provide foundations for future development of integrated and automated high-throughput genomics data analysis tools. Due to its inherently standardized pipeline and combination of automated analysis and platform-independence, CANEapp is an ideal for large-scale collaborative RNA-seq projects between different institutions and research groups.

Software type:
Pipeline, Suite
Interface:
Graphical user interface
Restrictions to use:
None
Operating system:
Mac OS, Windows
Programming languages:
Java, Python
License:
GNU General Public License version 2.0
Computer skills:
Medium
Stability:
Stable
Requirements:
Java 7 or higher, Python 2.6 or higher, a UNIX server with at least 30 GB of memory and 100 GB of disc space
Source code URL:
https://github.com/DmitryVel/CANEapp
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Institution(s)

Department of Psychiatry, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Biochemistry & Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USA

Funding source(s)

This work was supported by the National Institutes of Health, National Institute of Neurological Disorders and Stroke [R01NS081208-01A1].

  • (Velmeshev et al., 2016) CANEapp: a user-friendly application for automated next generation transcriptomic data analysis. BMC genomics.
    PMID: 26758513
  • (Poplawski et al., 2015) Systematically evaluating interfaces for RNA-seq analysis from a life scientist perspective. Briefings in bioinformatics.
    PMID: 26108229

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