Prioritizing biological pathways by recognizing context in time-series gene expression data

Jusang Lee, Kyuri Jo, Sunwon Lee, Jaewoo Kang, Sun Kim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Background: The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information. Results: In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. Conclusions: Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at

Original languageEnglish
Article number477
JournalBMC Bioinformatics
Publication statusPublished - 2016 Dec 23

Bibliographical note

Funding Information:
Publication charges for this research were funded by the BK21 Plus for Pioneers in Innovative Computing(Dept. of Computer Science and Engineering, SNU) funded by National Research Foundation of Korea(NRF) (21A20151113068). Also, this research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2012M3C4A7033341), and Collaborative Genome Program for Fostering New Post-Genome industry through the National Research Foundation of Korea(NRF) funded by the Ministry of Science ICT and Future Planning (NRF-2014M3C9A3063541).

Publisher Copyright:
© 2016 The Author(s).


  • Literature information
  • Pathway
  • Pathway analysis
  • Pathway prioritization
  • Time series

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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