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The Bio-Analytic Resource for Plant Biology

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Plant Genomics Databases

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1533))

Abstract

Bioinformatic tools have become part of the way plant researchers undertake investigations. Large data sets encompassing genomes, transcriptomes, proteomes, epigenomes, and other “-omes” that have been generated in the past decade may be easily accessed with such tools, such that hypotheses may be generated at the click of a mouse. In this chapter, we’ll cover the use of bioinformatic tools available at the Bio-Analytic Resource for Plant Biology at http://bar.utoronto.ca for exploring gene expression and coexpression patterns, undertaking promoter analyses, performing functional classification enrichment analyses for sets of genes, and examining protein-protein interactions. We also touch on some newer bioinformatic tools that allow integration of data from several sources for improved hypothesis generation, both for Arabidopsis and translationally. Most of the data sets come from Arabidopsis, but useful BAR tools for other species will be mentioned where appropriate.

This chapter encompasses updated material covering BAR tools in a book chapter by Miguel de Lucas, Nicholas Provart, and Siobhan Brady in Arabidopsis Protocols [1], along with additional material describing new or other BAR tools that weren’t covered in that chapter.

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References

  1. de Lucas M, Provart NJ, Brady SM (2014) Bioinformatic tools in arabidopsis research. In: Arabidopsis protocols. Springer, New York, NY, pp 97–136

    Chapter  Google Scholar 

  2. Toufighi K, Brady SM, Austin R et al (2005) The botany array resource: e-northerns, expression angling, and promoter analyses. Plant J 43:153–163. doi:10.1111/j.1365-313X.2005.02437.x

    Article  CAS  PubMed  Google Scholar 

  3. Winter D, Vinegar B, Nahal H et al (2007) An “Electronic Fluorescent Pictograph” browser for exploring and analyzing large-scale biological data sets. PLoS One 2:e718

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wang L, Czedik-Eysenberg A, Mertz RA et al (2014) Comparative analyses of C4 and C3 photosynthesis in developing leaves of maize and rice. Nat Biotechnol 32:1158–1165. doi:10.1038/nbt.3019

    Article  PubMed  Google Scholar 

  5. Patel RV, Nahal HK, Breit R, Provart NJ (2012) BAR expressolog identification: expression profile similarity ranking of homologous genes in plant species. Plant J 71:1038–1050. doi:10.1111/j.1365-313X.2012.05055.x

    Article  CAS  PubMed  Google Scholar 

  6. Finkelstein RR, Somerville CR (1990) Three classes of abscisic acid (ABA)-insensitive mutations of arabidopsis define genes that control overlapping subsets of ABA responses. Plant Physiol 94:1172–1179

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Brady SM, Provart NJ (2009) Web-queryable large-scale data sets for hypothesis generation in plant biology. Plant Cell 21:1034–1051. doi:10.1105/tpc.109.066050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Usadel B, Obayashi T, Mutwil M et al (2009) Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ 32:1633–1651. doi:10.1111/j.1365-3040.2009.02040.x

    Article  CAS  PubMed  Google Scholar 

  9. Schmid M, Davison TS, Henz SR et al (2005) A gene expression map of Arabidopsis thaliana development. Nat Genet 37:501–506. doi:10.1038/ng1543

    Article  CAS  PubMed  Google Scholar 

  10. Nakabayashi K, Okamoto M, Koshiba T et al (2005) Genome-wide profiling of stored mRNA in Arabidopsis thaliana seed germination: epigenetic and genetic regulation of transcription in seed. Plant J Cell Mol Biol 41:697–709. doi:10.1111/j.1365-313X.2005.02337.x

    Article  CAS  Google Scholar 

  11. Brady SM, Sarkar SF, Bonetta D, McCourt P (2003) The ABSCISIC ACID INSENSITIVE 3 (ABI3) gene is modulated by farnesylation and is involved in auxin signaling and lateral root development in Arabidopsis. Plant J 34:67–75. doi:10.1046/j.1365-313X.2003.01707.x

    Article  CAS  PubMed  Google Scholar 

  12. Laubinger S, Zeller G, Henz SR et al (2008) At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana. Genome Biol 9:R112. doi:10.1186/gb-2008-9-7-r112

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zeller G, Henz SR, Widmer CK et al (2009) Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole-genome tiling arrays. Plant J 58:1068–1082. doi:10.1111/j.1365-313X.2009.03835.x

    Article  CAS  PubMed  Google Scholar 

  14. Brady SM, Orlando DA, Lee J-Y et al (2007) A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318:801–806. doi:10.1126/science.1146265

    Article  CAS  PubMed  Google Scholar 

  15. Haslekås C, Grini PE, Nordgard SH et al (2003) ABI3 mediates expression of the peroxiredoxin antioxidant AtPER1 gene and induction by oxidative stress. Plant Mol Biol 53:313–326

    Article  PubMed  Google Scholar 

  16. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29. doi:10.1038/75556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Du Z, Zhou X, Ling Y et al (2010) agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res 38:W64–W70. doi:10.1093/nar/gkq310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Carbon S, Ireland A, Mungall CJ et al (2009) AmiGO: online access to ontology and annotation data. Bioinformatics 25:288–289. doi:10.1093/bioinformatics/btn615

    Article  CAS  PubMed  Google Scholar 

  19. Provart N, Zhu T (2003) A browser-based functional classification SuperViewer for Arabidopsis genomics. Curr Comput Mol Biol 2003:271–272

    Google Scholar 

  20. Thimm O, Bläsing O, Gibon Y et al (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J Cell Mol Biol 37:914–939

    Article  CAS  Google Scholar 

  21. Cui L, Wall PK, Leebens-Mack JH et al (2006) Widespread genome duplications throughout the history of flowering plants. Genome Res 16:738–749. doi:10.1101/gr.4825606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li L, Stoeckert CJ, Roos DS (2003) OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13:2178–2189. doi:10.1101/gr.1224503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Johnson D (2013) Examining the regulation of 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase in the arabidopsis thaliana shikimate pathway. MSc, University of Toronto

    Google Scholar 

  24. Wilkins O, Nahal H, Foong J et al (2009) Expansion and diversification of the Populus R2R3-MYB family of transcription factors. Plant Physiol 149:981–993

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Heazlewood JL, Verboom RE, Tonti-Filippini J et al (2007) SUBA: the arabidopsis subcellular database. Nucleic Acids Res 35:D213–D218. doi:10.1093/nar/gkl863

    Article  CAS  PubMed  Google Scholar 

  26. Geisler-Lee J, O’Toole N, Ammar R et al (2007) A predicted interactome for Arabidopsis. Plant Physiol 145:317–329. doi:10.1104/pp.107.103465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Klopffleisch K, Phan N, Augustin K et al (2011) Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis. Mol Syst Biol 7:532. doi:10.1038/msb.2011.66

    Article  PubMed  PubMed Central  Google Scholar 

  28. Nakamura S, Lynch TJ, Finkelstein RR (2001) Physical interactions between ABA response loci of Arabidopsis. Plant J 26:627–635

    Article  CAS  PubMed  Google Scholar 

  29. Waese J, Pasha A, Wang TT, et al (2016) Gene Slider: sequence logo interactive data-visualization for education and research. Bioinformatics, accepted. doi:10.1093/bioinformatics/btw525

  30. Haudry A, Platts AE, Vello E et al (2013) An atlas of over 90,000 conserved noncoding sequences provides insight into crucifer regulatory regions. Nat Genet 45:891–898. doi:10.1038/ng.2684

    Article  CAS  PubMed  Google Scholar 

  31. Schneider TD, Stephens RM (1990) Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18:6097–6100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Mathelier A, Zhao X, Zhang AW et al (2014) JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res 42:D142–D147. doi:10.1093/nar/gkt997

    Article  CAS  PubMed  Google Scholar 

  33. Weirauch MT, Yang A, Albu M et al (2014) Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158:1431–1443. doi:10.1016/j.cell.2014.08.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Grant CE, Bailey TL, Noble WS (2011) FIMO: scanning for occurrences of a given motif. Bioinformatics 27:1017–1018. doi:10.1093/bioinformatics/btr064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Higo K, Ugawa Y, Iwamoto M, Korenaga T (1999) Plant cis-acting regulatory DNA elements (PLACE) database: 1999. Nucleic Acids Res 27:297–300

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. AAustin RS, Hiu S, Waese J, et al (2016) New BAR Tools for Mining Expression Data and Exploring Cis-Elements inArabidopsis thaliana. Plant Journal, aceepted. doi: 10.1111/tpj.13261

  37. Joshi HJ, Hirsch-Hoffmann M, Baerenfaller K et al (2011) MASCP gator: an aggregation portal for the visualization of arabidopsis proteomics data. Plant Physiol 155:259–270. doi:10.1104/pp.110.168195

    Article  CAS  PubMed  Google Scholar 

  38. Kelley LA, Mezulis S, Yates CM et al (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858. doi:10.1038/nprot.2015.053

    Article  CAS  PubMed  Google Scholar 

  39. Krishnakumar V, Hanlon MR, Contrino S et al (2015) Araport: the Arabidopsis information portal. Nucleic Acids Res 43:D1003–D1009. doi:10.1093/nar/gku1200

    Article  PubMed  Google Scholar 

  40. Schones DE, Smith AD, Zhang MQ (2007) Statistical significance of cis-regulatory modules. BMC Bioinformatics 8:19. doi:10.1186/1471-2105-8-19

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lloyd J, Meinke D (2012) A comprehensive dataset of genes with a loss-of-function mutant phenotype in arabidopsis1[W][OA]. Plant Physiol 158:1115–1129. doi:10.1104/pp.111.192393

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tufte ER, Graves-Morris PR (1983) The visual display of quantitative information. Graphics Press, Cheshire, CT

    Google Scholar 

  43. Dean G, Cao Y, Xiang D et al (2011) Analysis of gene expression patterns during seed coat development in arabidopsis. Mol Plant 4:1074–1091. doi:10.1093/mp/ssr040

    Article  CAS  PubMed  Google Scholar 

  44. Wilkins O, Waldron L, Nahal H et al (2009) Genotype and time of day shape the Populus drought response. Plant J 60:703–715. doi:10.1111/j.1365-313X.2009.03993.x

    Article  CAS  PubMed  Google Scholar 

  45. Champigny MJ, Sung WW, Catana V et al (2013) RNA-Seq effectively monitors gene expression in Eutrema salsugineum plants growing in an extreme natural habitat and in controlled growth cabinet conditions. BMC Genomics 14:578. doi:10.1186/1471-2164-14-578

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Li P, Ponnala L, Gandotra N et al (2010) The developmental dynamics of the maize leaf transcriptome. Nat Genet 42:1060–1067. doi:10.1038/ng.703

    Article  CAS  PubMed  Google Scholar 

  47. Tran F, Penniket C, Patel RV et al (2013) Developmental transcriptional profiling reveals key insights into Triticeae reproductive development. Plant J 74:971–988. doi:10.1111/tpj.12206

    Article  CAS  PubMed  Google Scholar 

  48. Ortiz-Ramírez C, Hernandez-Coronado M, Thamm A et al (2016) A transcriptome atlas of Physcomitrella patens provides insights into the evolution and development of land plants. Mol Plant 9:205. doi:10.1016/j.molp.2015.12.002

    Article  PubMed  Google Scholar 

  49. Ho C-L, Wu Y, Shen H et al (2012) A predicted protein interactome for rice. Rice 5:15. doi:10.1186/1939-8433-5-15

    Article  PubMed  PubMed Central  Google Scholar 

  50. Austin RS, Vidaurre D, Stamatiou G et al (2011) Next-generation mapping of Arabidopsis genes. Plant J 67:715–725. doi:10.1111/j.1365-313X.2011.04619.x

    Article  CAS  PubMed  Google Scholar 

  51. Ilic K, Berleth T, Provart NJ (2004) BlastDigester – a web-based program for efficient CAPS marker design. Trends Genet 20:280–283. doi:10.1016/j.tig.2004.04.012

    Article  CAS  PubMed  Google Scholar 

  52. Taylor J, Provart NJ (2006) CapsID: a web-based tool for developing parsimonious sets of CAPS molecular markers for genotyping. BMC Genet 7:27. doi:10.1186/1471-2156-7-27

    Article  PubMed  PubMed Central  Google Scholar 

  53. Provart NJ, Alonso J, Assmann SM et al (2016) 50 years of Arabidopsis research: highlights and future directions. New Phytol 209:921–944. doi:10.1111/nph.13687

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Nicholas J. Provart .

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Waese, J., Provart, N.J. (2017). The Bio-Analytic Resource for Plant Biology. In: van Dijk, A. (eds) Plant Genomics Databases. Methods in Molecular Biology, vol 1533. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6658-5_6

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  • DOI: https://doi.org/10.1007/978-1-4939-6658-5_6

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6656-1

  • Online ISBN: 978-1-4939-6658-5

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