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SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining

  • Kei-ichiro Takahashi
  • David A. duVerle
  • Sohiya Yotsukura
  • Ichigaku Takigawa
  • Hiroshi Mamitsuka
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1807)

Abstract

Biclustering extracts coexpressed genes under certain experimental conditions, providing more precise insight into the genetic behaviors than one-dimensional clustering. For understanding the biological features of genes in a single bicluster, visualizations such as heatmaps or parallel coordinate plots and tools for enrichment analysis are widely used. However, simultaneously handling many biclusters still remains a challenge. Thus, we developed a web service named SiBIC, which, using maximal frequent itemset mining, exhaustively discovers significant biclusters, which turn into networks of overlapping biclusters, where nodes are gene sets and edges show their overlaps in the detected biclusters. SiBIC provides a graphical user interface for manipulating a gene set network, where users can find target gene sets based on the enriched network. This chapter provides a user guide/instruction of SiBIC with background of having developed this software. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/sibic/faces/index.jsp.

Key words

Gene expression Biclustering Frequent itemset mining Gene set network Gene enrichment analysis 

Notes

Acknowledgements

Part of this research has been supported by MEXT KAKENHI #16H02868 and #17H01783, ACCEL (JPMJAC1503) and PRESTO of JST, FiDiPro of Tekes and AIPSE programme, Academy of Finland.

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Copyright information

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

Authors and Affiliations

  • Kei-ichiro Takahashi
    • 1
  • David A. duVerle
    • 2
  • Sohiya Yotsukura
    • 1
  • Ichigaku Takigawa
    • 3
  • Hiroshi Mamitsuka
    • 4
    • 5
  1. 1.Bioinformatics Center, Institute for Chemical ResearchKyoto UniversityUjiJapan
  2. 2.Department of Computational Biology and Medical Sciences, Graduate School of Frontier SciencesUniversity of TokyoKashiwaJapan
  3. 3.Division of Computer Science and Information Technology, Graduate School of Information Science and TechnologyHokkaido UniversitySapporo, HokkaidoJapan
  4. 4.Bioinformatics Center, Institute for Chemical ResearchKyoto UniversityUjiJapan
  5. 5.Department of Computer ScienceAalto UniversityEspooFinland

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