Abstract
Physical interactions between transcription factors and specific DNA sites are essential for gene regulation. Recent progress in genome-wide in vivo techniques, like chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-SEQ), enables plant researchers to generate genome-wide, high-resolution DNA-binding maps of transcription factors. These new types of data require the use of advanced bioinformatic tools in order to understand the molecular mechanisms of functional specificity and target gene regulation by transcription factors. Here, we will review the use of a genome browser to visualize genome-wide DNA-binding maps of plant transcription factors along with other publicly available data and the program MEME to determine DNA sequence motifs in the bound regions. We also describe a tool for functional classification of target genes using GO annotations. Analysis of transcriptional regulatory networks requires the integration of multiple types of data, and this chapter aims at giving an overview about different bioinformatic approaches for meta-analysis and data integration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Park, P. J. (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. 10, 669ā680.
Morohashi, K., and Grotewold, E. (2009) A systems approach reveals regulatory circuitry for Arabidopsis trichome initiation by the GL3 and GL1 selectors. PLoS Genet. 5, e1000396.
Zheng, Y., Ren, N., Wang, H., Stromberg, A. J., and Perry, S. E. (2009) Global identification of targets of the Arabidopsis MADS domain protein AGAMOUS-Like15. Plant Cell 21, 2563ā2577.
Kaufmann, K., MuiƱo, J. M., Jauregui, R., Airoldi, C. A., Smaczniak, C., Krajewski, P., and Angenent, G. C. (2009) Target genes of the MADS transcription factor SEPALLATA3: integration of developmental and hormonal pathways in the Arabidopsis flower. PloS Biol. 7, e1000090.
Kaufmann, K., Wellmer, F., MuiƱo, J. M., Ferrier, T., Wuest, S. E., Kumar, V., Serrano-Mislata, A., Madueno, F., Krajewski, P., Meyerowitz, E. M., Angenent, G. C., and Riechmann, J. L. (2010) Orchestration of floral initiation by APETALA1. Science (New York, NY) 328, 85ā89.
Mathieu, J., Yant, L. J., Murdter, F., Kuttner, F., and Schmid, M. (2009) Repression of flowering by the miR172 target SMZ. PLoS Biol. 7, e1000148.
Morohashi, K., Xie, Z., and Grotewold, E. (2009) Gene-specific and genome-wide ChIP approaches to study plant transcriptional networks. Methods Mol. Biol. (Clifton, NJ) 553, 3ā12.
Kaufmann, K., MuiƱo, J. M., Osteras, M., Farinelli, L., Krajewski, P., and Angenent, G. C. (2010) Chromatin immunoprecipitation (ChIP) of plant transcription factors followed by sequencing (ChIP-SEQ) or hybridization to whole genome arrays (ChIP-CHIP). Nat. Protoc. 5, 457ā472.
Ji, H., Jiang, H., Ma, W., Johnson, D. S., Myers, R. M., and Wong, W. H. (2008) An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat. Biotechnol. 26, 1293ā1300.
MuiƱo, J. M., Hoogstraat, M., van Ham, R. C. H. J., and van Dijk, A. D. J. (2011) PRI-CAT: A web-tool for the analysis, storage and visualization of plant ChIP-seq experiments. Nucleic Acids Res. (In press)
Bouvet, P. (2001) Determination of nucleic acid recognition sequences by SELEX. Methods Mol. Biol. (Clifton, NJ) 148, 603ā610.
Berger, M. F., and Bulyk, M. L. (2009) Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors. Nat. Protoc. 4, 393ā411.
Jolma, A., Kivioja, T., Toivonen, J., Cheng, L., Wei, G., Enge, M., Taipale, M., Vaquerizas, J. M., Yan, J., Sillanpaa, M. J., Bonke, M., Palin, K., Talukder, S., Hughes, T. R., Luscombe, N. M., Ukkonen, E., and Taipale, J. (2010) Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res. 20, 861ā873.
Matys, V., Kel-Margoulis, O. V., Fricke, E., Liebich, I., Land, S., Barre-Dirrie, A., Reuter, I., Chekmenev, D., Krull, M., Hornischer, K., Voss, N., Stegmaier, P., Lewicki-Potapov, B., Saxel, H., Kel, A. E., and Wingender, E. (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34, D108āD110.
Portales-Casamar, E., Thongjuea, S., Kwon, A. T., Arenillas, D., Zhao, X., Valen, E., Yusuf, D., Lenhard, B., Wasserman, W. W., and Sandelin, A. (2010) JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res. 38, D105āD110.
Hestand, M. S., van Galen, M., Villerius, M. P., van Ommen, G. J., den Dunnen, J. T., and āt Hoen, P. A. (2008) CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated genes. BMC Bioinformatics 9, 495.
Frith, M. C., Fu, Y., Yu, L., Chen, J. F., Hansen, U., and Weng, Z. (2004) Detection of functional DNA motifs via statistical over-representation. Nucleic Acids Res. 32, 1372ā1381.
Chekmenev, D. S., Haid, C., and Kel, A. E. (2005) P-Match: transcription factor binding site search by combining patterns and weight matrices. Nucleic Acids Res. 33, W432āW437.
Haudry, Y., Ramialison, M., Paten, B., Wittbrodt, J., and Ettwiller, L. (2010) Using Trawler_standalone to discover overrepresented motifs in DNA and RNA sequences derived from various experiments including chromatin immunoprecipitation. Nat. Protoc. 5, 323ā334.
Bellora, N., Farre, D., and Mar Alba, M. (2007) PEAKS: identification of regulatory motifs by their position in DNA sequences. Bioinformatics (Oxford, England) 23, 243ā244.
Bailey, T. L., Williams, N., Misleh, C., and Li, W. W. (2006) MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res. 34, W369āW373.
Foat, B. C., Morozov, A. V., and Bussemaker, H. J. (2006) Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics (Oxford, England) 22, e141āe149.
Maere, S., Heymans, K., and Kuiper, M. (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics (Oxford, England) 21, 3448ā3449.
Carbon, S., Ireland, A., Mungall, C. J., Shu, S., Marshall, B., and Lewis, S. (2009) AmiGO: online access to ontology and annotation data. Bioinformatics (Oxford, England) 25, 288ā289.
Brady, S. M., and Provart, N. J. (2009) Web-queryable large-scale data sets for hypothesis generation in plant biology. Plant Cell. 21, 1034ā1051.
Nielsen, C. B., Cantor, M., Dubchak, I., Gordon, D., and Wang, T. (2010) Visualizing genomes: techniques and challenges. Nat. Methods 7, S5āS15.
http://java.sum.com/j2se/1.5.0/docs/api/java/util/regex/Pattern.html.
Tarailo-Graovac, M., and Chen, N. (2009) Using RepeatMasker to identify repetitive elements in genomic sequences. Current Protocols in Bioinformatics 4.10.1ā4.10.14.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2011 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
MuiƱo, J.M., Angenent, G.C., Kaufmann, K. (2011). Visualizing and Characterizing In Vivo DNA-Binding Events and Direct Target Genes of Plant Transcription Factors. In: Yuan, L., Perry, S. (eds) Plant Transcription Factors. Methods in Molecular Biology, vol 754. Humana Press. https://doi.org/10.1007/978-1-61779-154-3_17
Download citation
DOI: https://doi.org/10.1007/978-1-61779-154-3_17
Published:
Publisher Name: Humana Press
Print ISBN: 978-1-61779-153-6
Online ISBN: 978-1-61779-154-3
eBook Packages: Springer Protocols