SeqEnrich: A tool to predict transcription factor networks from co-expressed Arabidopsis and Brassica napus gene sets.

Michael G Becker, Philip L Walker, Nadège C Pulgar-Vidal, Mark F Belmonte
Author Information
  1. Michael G Becker: Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
  2. Philip L Walker: Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
  3. Nadège C Pulgar-Vidal: Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada.
  4. Mark F Belmonte: Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada. ORCID

Abstract

Transcription factors and their associated DNA binding sites are key regulatory elements of cellular differentiation, development, and environmental response. New tools that predict transcriptional regulation of biological processes are valuable to researchers studying both model and emerging-model plant systems. SeqEnrich predicts transcription factor networks from co-expressed Arabidopsis or Brassica napus gene sets. The networks produced by SeqEnrich are supported by existing literature and predicted transcription factor-DNA interactions that can be functionally validated at the laboratory bench. The program functions with gene sets of varying sizes and derived from diverse tissues and environmental treatments. SeqEnrich presents as a powerful predictive framework for the analysis of Arabidopsis and Brassica napus co-expression data, and is designed so that researchers at all levels can easily access and interpret predicted transcriptional circuits. The program outperformed its ancestral program ChipEnrich, and produced detailed transcription factor networks from Arabidopsis and Brassica napus gene expression data. The SeqEnrich program is ideal for generating new hypotheses and distilling biological information from large-scale expression data.

References

  1. Mol Plant. 2016 Jan 4;9(1):113-25 [PMID: 26363272]
  2. J Exp Bot. 2016 May;67(11):3561-71 [PMID: 27194740]
  3. Nucleic Acids Res. 2015 Jul 1;43(W1):W50-6 [PMID: 25904632]
  4. Plant Physiol. 2016 Apr;170(4):2218-31 [PMID: 26888061]
  5. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W64-70 [PMID: 20435677]
  6. Plant J. 2016 Nov;88(3):490-504 [PMID: 27401965]
  7. Science. 2014 Aug 22;345(6199):950-3 [PMID: 25146293]
  8. Methods Mol Biol. 2009;553:57-77 [PMID: 19588101]
  9. Genome Res. 2013 Aug;23(8):1319-28 [PMID: 23636944]
  10. Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W262-6 [PMID: 15980466]
  11. J Biol Chem. 2009 Oct 9;284(41):27998-8003 [PMID: 19674971]
  12. Nature. 2015 Jan 29;517(7536):571-5 [PMID: 25533953]
  13. BMC Genomics. 2004 Jul 05;5(1):39 [PMID: 15236668]
  14. Proc Natl Acad Sci U S A. 2013 Jun 25;110(26):10866-71 [PMID: 23754415]
  15. Bioinformatics. 2007 Jan 15;23(2):134-41 [PMID: 17098775]
  16. Nucleic Acids Res. 2003 Jan 1;31(1):224-8 [PMID: 12519987]
  17. Cell. 2016 May 19;165(5):1280-92 [PMID: 27203113]
  18. Nucleic Acids Res. 2011 Sep 1;39(16):7092-102 [PMID: 21622661]
  19. Plant Physiol. 2008 Mar;146(3):1182-92 [PMID: 18203871]
  20. Nucleic Acids Res. 2014 Jul;42(Web Server issue):W20-5 [PMID: 24860165]
  21. Plant J. 2017 May;90(3):573-586 [PMID: 28222234]
  22. Plant Mol Biol. 2014 Aug;85(6):589-99 [PMID: 24879533]
  23. Plant Signal Behav. 2015;10(6):e1010967 [PMID: 26107850]
  24. Proc Natl Acad Sci U S A. 2013 Jan 29;110(5):E435-44 [PMID: 23319655]
  25. BMC Genomics. 2008 Jan 27;9:44 [PMID: 18221561]
  26. BMC Genomics. 2014 Aug 03;15:642 [PMID: 25086704]
  27. J Genet Genomics. 2008 Feb;35(2):105-18 [PMID: 18407058]
  28. Gene. 2002 May 15;290(1-2):63-71 [PMID: 12062802]
  29. Plant Cell. 2003 Jul;15(7):1538-51 [PMID: 12837945]
  30. Plant J. 2003 Jul;35(2):193-205 [PMID: 12848825]
  31. Plant J. 2015 Apr;82(1):41-53 [PMID: 25684030]
  32. Proc Natl Acad Sci U S A. 2014 Feb 11;111(6):2367-72 [PMID: 24477691]
  33. Plant Physiol. 2007 Oct;145(2):317-29 [PMID: 17675552]
  34. Trends Plant Sci. 2012 Mar;17(3):163-71 [PMID: 22236699]
  35. PLoS One. 2015 Oct 20;10(10):e0141044 [PMID: 26484765]
  36. G3 (Bethesda). 2012 Sep;2(9):987-1002 [PMID: 22973536]
  37. BMC Bioinformatics. 2016 Jan 27;17:50 [PMID: 26817596]
  38. Nature. 2000 Dec 14;408(6814):796-815 [PMID: 11130711]
  39. DNA Res. 2013 Oct;20(5):437-48 [PMID: 23690543]
  40. Plant Cell Physiol. 2016 Jan;57(1):e12 [PMID: 26657893]
  41. Sci Rep. 2016 Apr 27;6:25164 [PMID: 27117388]
  42. Nucleic Acids Res. 2011 Jan;39(Database issue):D1118-22 [PMID: 21059685]
  43. Plant Physiol. 2004 Apr;134(4):1718-32 [PMID: 15084732]
  44. Plant Mol Biol. 2006 Jan;60(1):107-24 [PMID: 16463103]
  45. Plant Physiol. 2006 Feb;140(2):411-32 [PMID: 16407444]
  46. Bioinformatics. 2005 Jul 1;21(13):2933-42 [PMID: 15860560]
  47. Proc Natl Acad Sci U S A. 2016 Nov 15;113(46):E7307-E7316 [PMID: 27799549]

MeSH Term

Arabidopsis
Arabidopsis Proteins
Brassica napus
DNA, Plant
Gene Expression Regulation, Plant
Genes, Plant
Genomics
Plant Proteins
Protein Interaction Maps
Software
Transcription Factors
Transcriptional Activation

Chemicals

Arabidopsis Proteins
DNA, Plant
Plant Proteins
Transcription Factors

Word Cloud

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