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Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast

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  • Zing Tsung-Yeh Tsai
  • Shin-Han Shiu
  • Huai-Kuang Tsai

Abstract

Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA “intrinsic properties” (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.Author Summary: Identification of transcription factor binding sites based on sequence motifs is typically accompanied by a high false positive rate. Increasing evidence suggests that there are many other factors besides DNA sequence that may affect the binding and interaction of TFs with DNA. Through the integration of sequence motif, chromatin state, and DNA structure properties, we show that TF binding can be better predicted. Moreover, considering chromatin state and DNA structure properties simultaneously yields a significant improvement. While the binding of some TFs can be readily predicted using either chromatin state information or DNA structure, other TFs need both. Thus, our findings provide insights on how different histone modifications and DNA structure properties may influence the binding of a particular TF and thus how TFs regulate gene expression. These features are referred to as sequence “intrinsic properties” because they can be predicted from sequences alone. These intrinsic properties can be used to build a TF binding prediction model that has a similar performance to considering all features. Moreover, the intrinsic property model allows TFBS predictions not only across TFs, but also across DNA-binding domain families that are present in most eukaryotes, suggesting that the model likely can be used across species.

Suggested Citation

  • Zing Tsung-Yeh Tsai & Shin-Han Shiu & Huai-Kuang Tsai, 2015. "Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-22, August.
  • Handle: RePEc:plo:pcbi00:1004418
    DOI: 10.1371/journal.pcbi.1004418
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    1. Istvan Albert & Travis N. Mavrich & Lynn P. Tomsho & Ji Qi & Sara J. Zanton & Stephan C. Schuster & B. Franklin Pugh, 2007. "Translational and rotational settings of H2A.Z nucleosomes across the Saccharomyces cerevisiae genome," Nature, Nature, vol. 446(7135), pages 572-576, March.
    2. Leelavati Narlikar & Raluca Gordân & Alexander J Hartemink, 2007. "A Nucleosome-Guided Map of Transcription Factor Binding Sites in Yeast," PLOS Computational Biology, Public Library of Science, vol. 3(11), pages 1-10, November.
    3. Christopher T. Harbison & D. Benjamin Gordon & Tong Ihn Lee & Nicola J. Rinaldi & Kenzie D. Macisaac & Timothy W. Danford & Nancy M. Hannett & Jean-Bosco Tagne & David B. Reynolds & Jane Yoo & Ezra G., 2004. "Transcriptional regulatory code of a eukaryotic genome," Nature, Nature, vol. 431(7004), pages 99-104, September.
    4. Colin R. Lickwar & Florian Mueller & Sean E. Hanlon & James G. McNally & Jason D. Lieb, 2012. "Genome-wide protein–DNA binding dynamics suggest a molecular clutch for transcription factor function," Nature, Nature, vol. 484(7393), pages 251-255, April.
    5. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    6. Eran Segal & Yvonne Fondufe-Mittendorf & Lingyi Chen & AnnChristine Thåström & Yair Field & Irene K. Moore & Ji-Ping Z. Wang & Jonathan Widom, 2006. "A genomic code for nucleosome positioning," Nature, Nature, vol. 442(7104), pages 772-778, August.
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    1. Sahra Uygun & Cheng Peng & Melissa D Lehti-Shiu & Robert L Last & Shin-Han Shiu, 2016. "Utility and Limitations of Using Gene Expression Data to Identify Functional Associations," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-27, December.

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