iSeq: Web-Based RNA-seq Data Analysis and Visualization

  • Chao Zhang
  • Caoqi Fan
  • Jingbo Gan
  • Ping Zhu
  • Lei Kong
  • Cheng Li
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)


Transcriptome sequencing (RNA-seq) is becoming a standard experimental methodology for genome-wide characterization and quantification of transcripts at single base-pair resolution. However, downstream analysis of massive amount of sequencing data can be prohibitively technical for wet-lab researchers. A functionally integrated and user-friendly platform is required to meet this demand. Here, we present iSeq, an R-based Web server, for RNA-seq data analysis and visualization. iSeq is a streamlined Web-based R application under the Shiny framework, featuring a simple user interface and multiple data analysis modules. Users without programming and statistical skills can analyze their RNA-seq data and construct publication-level graphs through a standardized yet customizable analytical pipeline. iSeq is accessible via Web browsers on any operating system at

Key words

RNA-seq R-Shiny Gene expression analysis Gene ontology enrichment Data visualization 



We thank Yifang Liu for advice on Web server construction and the PKU Bioinformatics Core Discussion Group (Yangchen Zheng, Yong Peng) for testing and suggestions. This work was supported by funding from Peking-Tsinghua Center for Life Sciences and School of Life Sciences of Peking University, Natural Science Foundation of China (Key Research Grant 71532001), and Chinese National Key Projects of Research and Development (2016YFA0100103).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Chao Zhang
    • 1
  • Caoqi Fan
    • 2
  • Jingbo Gan
    • 2
  • Ping Zhu
    • 2
  • Lei Kong
    • 2
  • Cheng Li
    • 2
    • 3
  1. 1.PKU-Tsinghua-NIBS Graduate Program, School of Life SciencesPeking UniversityBeijingChina
  2. 2.Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Bioinformatics, School of Life SciencesPeking UniversityBeijingChina
  3. 3.Center for Statistical SciencePeking UniversityBeijingChina

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