DNA Motif Databases and Their Uses

Gary D. Stormo1

1 Washington University School of Medicine, St. Louis, Missouri
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 2.15
DOI:  10.1002/0471250953.bi0215s51
Online Posting Date:  September, 2015
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library

Abstract

Transcription factors (TFs) recognize and bind to specific DNA sequences. The specificity of a TF is usually represented as a position weight matrix (PWM). Several databases of DNA motifs exist and are used in biological research to address important biological questions. This overview describes PWMs and some of the most commonly used motif databases, as well as a few of their common applications. © 2015 by John Wiley & Sons, Inc.

Keywords: transcription factors; DNA motifs; position weight matrices; binding site predictions

     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Table of Contents

  • Introduction
  • DNA Motifs
  • Databases of DNA Motifs
  • Uses of DNA Motifs
  • Conclusion
  • Literature Cited
  • Figures
     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Materials

GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Figures

Videos

Literature Cited

Literature Cited
  Abe, N., Dror, I., Yang, L., Slattery, M., Zhou, T., Bussemaker, H.J., Rohs, R., and Mann, R.S. 2015. Deconvolving the recognition of DNA shape from sequence. Cell 161:307‐318. doi: 10.1016/j.cell.2015.02.008.
  Bailey, T.L. 2002. Discovering novel sequence motifs with MEME. Curr. Protoc. Bioinform. 00:2.4.1‐2.4.35.
  Bussemaker, H.J. 2015. Recent progress in understanding transcription factor binding specificity. Brief. Funct. Genomics 14:1‐2. doi: 10.1093/bfgp/elu050.
  Feng, J., Liu, T. and Zhang, Y. 2011. Using MACS to identify peaks from ChIP‐Seq data. Curr. Protoc. Bioinform. 34:2.14.1‐2.14.14.
  Hume, M.A., Barrera, L.A., Gisselbrecht, S.S., and Bulyk, M.L., 2015. UniPROBE, update 2015: New tools and content for the online database of protein‐binding microarray data on protein‐DNA interactions. Nucleic Acids Res. 43:D117‐D122. doi: 10.1093/nar/gku1045.
  Ji, H., Jiang, H., Ma, W. and Wong, W.H. 2011. Using CisGenome to analyze ChIP‐chip and ChIP‐seq data. Curr Protoc Bioinform. 33:2.13.1‐2.13.45.
  Mathelier, A. and Wasserman, W.W. 2013. The next generation of transcription factor binding site prediction. PLoS Comput. Biol. 9:e1003214. doi: 10.1371/journal.pcbi.1003214.
  Mathelier, A., Zhao, X., Zhang, A.W., Parcy, F., Worsley‐Hunt, R., Arenillas, D.J., Buchman, S., Chen, C.Y., Chou, A., Ienasescu, H., Lim, J., Shyr, C., Tan, G., Zhou, M., Lenhard, B., Sandelin, A., and Wasserman, W.W. 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.
  Narasimhan, K., Lambert, S.A., Yang, A.W., Riddell, J., Mnaimneh, S., Zheng, H., Albu, M., Najafabadi, H.S., Reece‐Hoyes, J.S., Fuxman Bass, J.I., Walhout, A.J., Weirauch, M.T., and Hughes, T.R. 2015. Mapping and analysis of transcription factor sequence specificities. eLife 4:e06967. doi: 10.1038/nature11212.
  Neph, S., Vierstra, J., Stergachis, A.B., Reynolds, A.P., Haugen, E., Vernot, B., Thurman, R.E., John, S., Sandstrom, R., Johnson, A.K., Maurano, M.T., Humbert, R., Rynes, E., Wang, H., Vong, S., Lee, K., Bates, D., Diegel, M., Roach, V., Dunn, D., Neri, J., Schafer, A., Hansen, R.S., Kutyavin, T., Giste, E., Weaver, M., Canfield, T., Sabo, P., Zhang, M., Balasundaram, G., Byron, R., MacCoss, M.J., Akey, J.M., Bender, M.A., Groudine, M., Kaul, R., and Stamatoyannopoulos, J.A. 2012. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489:83‐90. doi: 10.1038/nature11212.
  Stormo, G.D. 2013. Modeling the specificity of protein‐DNA interactions. Quant. Biol. 1:115‐130. doi: 10.1007/s40484‐013‐0012‐4.
  Stormo, G.D. and Zhao, Y. 2010. Determining the specificity of protein‐DNA interactions. Nat. Rev. Genet 11:751‐760.doi: 10.1038/nrm3005.
  Weirauch, M.T., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T.R., Saez‐Rodriguez, J., Cokelaer, T., Vedenko, A., Talukder, S.; DREAM5 Consortium, Bussemaker, H.J., Morris, Q.D., Bulyk, M.L., Stolovitzky, G., and Hughes, T.R. 2013. Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31:126‐134. doi: 10.1038/nbt.2486.
  Weirauch, M.T., Yang, A., Albu, M., Cote, A.G., Montenegro‐Montero, A., Drewe, P., Najafabadi, H.S., Lambert, S.A., Mann, I., Cook, K., Zheng, H., Goity, A., van Bakel, H., Lozano, J.C., Galli, M., Lewsey, M.G., Huang, E., Mukherjee, T., Chen, X., Reece‐Hoyes, J.S., Govindarajan, S., Shaulsky, G., Walhout, A.J., Bouget, F.Y., Ratsch, G., Larrondo, L.F., Ecker, J.R., and Hughes, T.R. 2014. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158:1431‐1443. doi: 10.1016/j.cell.2014.08.009.
  Zhao, Y. and Stormo, G.D. 2011. Quantitative analysis demonstrates most transcription factors require only simple models of specificity. Nat. Biotechnol. 29:480‐483. doi: 10.1038/nbt.1893.
  Zhou, T., Shen, N., Yang, L., Abe, N., Horton, J., Mann, R.S., Bussemaker, H.J., Gordân, R., and Rohs, R. 2015. Quantitative modeling of transcription factor binding specificities using DNA shape. Proc. Nat. Acad. Sci. U S A 112:4654‐4659. doi: 10.1073/pnas.1422023112.
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library