Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE
Transcription factors are crucial regulators of gene expression. Accurate quantitative definition of their intrinsic DNA binding preferences is critical to understanding their biological function. High-throughput in vitro technology has recently been used to deeply probe the DNA binding specificity...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2015-12-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/06397 |
| _version_ | 1828375807245942784 |
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| author | Todd R Riley Allan Lazarovici Richard S Mann Harmen J Bussemaker |
| author_facet | Todd R Riley Allan Lazarovici Richard S Mann Harmen J Bussemaker |
| author_sort | Todd R Riley |
| collection | DOAJ |
| description | Transcription factors are crucial regulators of gene expression. Accurate quantitative definition of their intrinsic DNA binding preferences is critical to understanding their biological function. High-throughput in vitro technology has recently been used to deeply probe the DNA binding specificity of hundreds of eukaryotic transcription factors, yet algorithms for analyzing such data have not yet fully matured. Here, we present a general framework (FeatureREDUCE) for building sequence-to-affinity models based on a biophysically interpretable and extensible model of protein-DNA interaction that can account for dependencies between nucleotides within the binding interface or multiple modes of binding. When training on protein binding microarray (PBM) data, we use robust regression and modeling of technology-specific biases to infer specificity models of unprecedented accuracy and precision. We provide quantitative validation of our results by comparing to gold-standard data when available. |
| first_indexed | 2024-04-14T07:51:55Z |
| format | Article |
| id | doaj.art-9872cee3b58d49de94f316757eaaa0d7 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-14T07:51:55Z |
| publishDate | 2015-12-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-9872cee3b58d49de94f316757eaaa0d72022-12-22T02:05:10ZengeLife Sciences Publications LtdeLife2050-084X2015-12-01410.7554/eLife.06397Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCETodd R Riley0Allan Lazarovici1Richard S Mann2Harmen J Bussemaker3Department of Biological Sciences, Columbia University, New York, United States; Department of Systems Biology, Columbia University, New York, United States; Department of Biology, University of Massachusetts Boston, Boston, United StatesDepartment of Biological Sciences, Columbia University, New York, United States; Department of Electrical Engineering, Columbia University, New York, United StatesDepartment of Systems Biology, Columbia University, New York, United States; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United StatesDepartment of Biological Sciences, Columbia University, New York, United States; Department of Systems Biology, Columbia University, New York, United StatesTranscription factors are crucial regulators of gene expression. Accurate quantitative definition of their intrinsic DNA binding preferences is critical to understanding their biological function. High-throughput in vitro technology has recently been used to deeply probe the DNA binding specificity of hundreds of eukaryotic transcription factors, yet algorithms for analyzing such data have not yet fully matured. Here, we present a general framework (FeatureREDUCE) for building sequence-to-affinity models based on a biophysically interpretable and extensible model of protein-DNA interaction that can account for dependencies between nucleotides within the binding interface or multiple modes of binding. When training on protein binding microarray (PBM) data, we use robust regression and modeling of technology-specific biases to infer specificity models of unprecedented accuracy and precision. We provide quantitative validation of our results by comparing to gold-standard data when available.https://elifesciences.org/articles/06397transcription factorprotein binding microarray technologybiophysical modelDNA binding specificity |
| spellingShingle | Todd R Riley Allan Lazarovici Richard S Mann Harmen J Bussemaker Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE eLife transcription factor protein binding microarray technology biophysical model DNA binding specificity |
| title | Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE |
| title_full | Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE |
| title_fullStr | Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE |
| title_full_unstemmed | Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE |
| title_short | Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE |
| title_sort | building accurate sequence to affinity models from high throughput in vitro protein dna binding data using featurereduce |
| topic | transcription factor protein binding microarray technology biophysical model DNA binding specificity |
| url | https://elifesciences.org/articles/06397 |
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