TherMos : estimating protein-DNA binding energies from in vivo binding profiles
Accurately characterizing transcription factor (TF)-DNA affinity is a central goal of regulatory genomics. Although thermodynamics provides the most natural language for describing the continuous range of TF-DNA affinity, traditional motif discovery algorithms focus instead on classification paradig...
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sg-ntu-dr.10356-1009142023-02-28T17:04:55Z TherMos : estimating protein-DNA binding energies from in vivo binding profiles Sun, Wenjie Hu, Xiaoming Choo, Siew Hua Drechsel, Daniela Guillemot, Franc Jauch, Ralf Prabhakar, Shyam Lim, Michael H. K. Ng, Calista K. L. Castro, Diogo S. Kolatkar, Prasanna R. School of Biological Sciences DRNTU::Science::Biological sciences::Genetics Accurately characterizing transcription factor (TF)-DNA affinity is a central goal of regulatory genomics. Although thermodynamics provides the most natural language for describing the continuous range of TF-DNA affinity, traditional motif discovery algorithms focus instead on classification paradigms that aim to discriminate ‘bound’ and ‘unbound’ sequences. Moreover, these algorithms do not directly model the distribution of tags in ChIP-seq data. Here, we present a new algorithm named Thermodynamic Modeling of ChIP-seq (TherMos), which directly estimates a position-specific binding energy matrix (PSEM) from ChIP-seq/exo tag profiles. In cross-validation tests on seven genome-wide TF-DNA binding profiles, one of which we generated via ChIP-seq on a complex developing tissue, TherMos predicted quantitative TF-DNA binding with greater accuracy than five well-known algorithms. We experimentally validated TherMos binding energy models for Klf4 and Esrrb, using a novel protocol to measure PSEMs in vitro. Strikingly, our measurements revealed strong non-additivity at multiple positions within the two PSEMs. Among the algorithms tested, only TherMos was able to model the entire binding energy landscape of Klf4 and Esrrb. Our study reveals new insights into the energetics of TF-DNA binding in vivo and provides an accurate first-principles approach to binding energy inference from ChIP-seq and ChIP-exo data. Published version 2014-01-16T06:56:50Z 2019-12-06T20:30:36Z 2014-01-16T06:56:50Z 2019-12-06T20:30:36Z 2013 2013 Journal Article Sun, W., Hu, X., Lim, M. H. K., Ng, C. K. L., Choo, S. H., Castro, D. S., et al. (2013). TherMos: Estimating protein-DNA binding energies from in vivo binding profiles. Nucleic Acids Research, 41(11), 5555-5568. https://hdl.handle.net/10356/100914 http://hdl.handle.net/10220/18607 10.1093/nar/gkt250 23595148 en Nucleic acids research © 2013 The Author(s) Oxford University Press. This paper was published in Nucleic Acids Research and is made available as an electronic reprint (preprint) with permission of The Author(s). The paper can be found at the following official DOI: [http://dx.doi.org/10.1093/nar/gkt250 ]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf |
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DRNTU::Science::Biological sciences::Genetics Sun, Wenjie Hu, Xiaoming Choo, Siew Hua Drechsel, Daniela Guillemot, Franc Jauch, Ralf Prabhakar, Shyam Lim, Michael H. K. Ng, Calista K. L. Castro, Diogo S. Kolatkar, Prasanna R. TherMos : estimating protein-DNA binding energies from in vivo binding profiles |
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Accurately characterizing transcription factor (TF)-DNA affinity is a central goal of regulatory genomics. Although thermodynamics provides the most natural language for describing the continuous range of TF-DNA affinity, traditional motif discovery algorithms focus instead on classification paradigms that aim to discriminate ‘bound’ and ‘unbound’ sequences. Moreover, these algorithms do not directly model the distribution of tags in ChIP-seq data. Here, we present a new algorithm named Thermodynamic Modeling of ChIP-seq (TherMos), which directly estimates a position-specific binding energy matrix (PSEM) from ChIP-seq/exo tag profiles. In cross-validation tests on seven genome-wide TF-DNA binding profiles, one of which we generated via ChIP-seq on a complex developing tissue, TherMos predicted quantitative TF-DNA binding with greater accuracy than five well-known algorithms. We experimentally validated TherMos binding energy models for Klf4 and Esrrb, using a novel protocol to measure PSEMs in vitro. Strikingly, our measurements revealed strong non-additivity at multiple positions within the two PSEMs. Among the algorithms tested, only TherMos was able to model the entire binding energy landscape of Klf4 and Esrrb. Our study reveals new insights into the energetics of TF-DNA binding in vivo and provides an accurate first-principles approach to binding energy inference from ChIP-seq and ChIP-exo data. |
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School of Biological Sciences |
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School of Biological Sciences Sun, Wenjie Hu, Xiaoming Choo, Siew Hua Drechsel, Daniela Guillemot, Franc Jauch, Ralf Prabhakar, Shyam Lim, Michael H. K. Ng, Calista K. L. Castro, Diogo S. Kolatkar, Prasanna R. |
format |
Article |
author |
Sun, Wenjie Hu, Xiaoming Choo, Siew Hua Drechsel, Daniela Guillemot, Franc Jauch, Ralf Prabhakar, Shyam Lim, Michael H. K. Ng, Calista K. L. Castro, Diogo S. Kolatkar, Prasanna R. |
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Sun, Wenjie |
title |
TherMos : estimating protein-DNA binding energies from in vivo binding profiles |
title_short |
TherMos : estimating protein-DNA binding energies from in vivo binding profiles |
title_full |
TherMos : estimating protein-DNA binding energies from in vivo binding profiles |
title_fullStr |
TherMos : estimating protein-DNA binding energies from in vivo binding profiles |
title_full_unstemmed |
TherMos : estimating protein-DNA binding energies from in vivo binding profiles |
title_sort |
thermos : estimating protein-dna binding energies from in vivo binding profiles |
publishDate |
2014 |
url |
https://hdl.handle.net/10356/100914 http://hdl.handle.net/10220/18607 |
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1759853552280272896 |