Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

Chua, Xin-Yi; Buckingham, Lawrence; Hogan, James M.; Novichkov, Pavel

doi:10.1016/j.procs.2015.05.189

Title: Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

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Abstract

The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond those of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.

Authors:

Chua, Xin-Yi ^[1]; Buckingham, Lawrence ^[2]; Hogan, James M. ^[2]; Novichkov, Pavel ^[3]

QFAB Bioinformatics, Inst. for Molecular Biosciences, Brisbane (Austrialia)
School of EECS, QUT, Brisbane (Australia)
Lawrence Berkeley National Lab., CA (United States)

Publication Date:: Mon Jun 01 00:00:00 EDT 2015

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1208653

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: Procedia Computer Science

Additional Journal Information:: Journal Volume: 51; Journal Issue: C; Journal ID: ISSN 1877-0509

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: Bioinformatics, Visualisation, Transcription, Regulatory Networks

Citation Formats


                    Chua, Xin-Yi, Buckingham, Lawrence, Hogan, James M., and Novichkov, Pavel. Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff.  United States: N. p., 2015. 
Web.  doi:10.1016/j.procs.2015.05.189.

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                    Chua, Xin-Yi, Buckingham, Lawrence, Hogan, James M., & Novichkov, Pavel. Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff.  United States.  https://doi.org/10.1016/j.procs.2015.05.189

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                    Chua, Xin-Yi, Buckingham, Lawrence, Hogan, James M., and Novichkov, Pavel. Mon .  
"Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff".  United States.  https://doi.org/10.1016/j.procs.2015.05.189.  https://www.osti.gov/servlets/purl/1208653.

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@article{osti_1208653,

  title        = {Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff},

  author       = {Chua, Xin-Yi and Buckingham, Lawrence and Hogan, James M. and Novichkov, Pavel},

  abstractNote = {The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond those of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.},

  doi          = {10.1016/j.procs.2015.05.189},

  journal      = {Procedia Computer Science},

  number       = C,

  volume       = 51,

  place        = {United States},

  year         = {Mon Jun 01 00:00:00 EDT 2015},

  month        = {Mon Jun 01 00:00:00 EDT 2015}

}

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