Kiwi: a tool for integration and visualization of network topology and gene-set analysis

BMC Bioinformatics

Jens Nielsen¹  Francesco Gatto¹  Leif Väremo¹ 

[1]Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden

关键词: Visualization tool;    Network analysis;    Transcriptomics;    Gene-set analysis;   

Others  :  1084400
DOI  :  10.1186/s12859-014-0408-9

 received in 2014-09-01, accepted in 2014-12-03,  发布年份 2014

【 摘 要 】

Background

The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures.

Results

Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network.

Conclusions

Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi webcite and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org webcite.

【 授权许可】

   
2014 Väremo et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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