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LR-DNase: Predicting TF binding from DNase-seq data

View ORCID ProfileArjan van der Velde, View ORCID ProfileMichael Purcaro, William Stafford Noble, Zhiping Weng
doi: https://doi.org/10.1101/082594
Arjan van der Velde
1Program in Bioinformatics and Integrative Biology, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA
2Bioinformatics Program, Boston University, Boston, MA 02215, USA
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  • For correspondence: zhiping.weng@umassmed.edu arjan.vandervelde@umassmed.edu
Michael Purcaro
1Program in Bioinformatics and Integrative Biology, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA
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William Stafford Noble
3Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
4Department of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA
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Zhiping Weng
1Program in Bioinformatics and Integrative Biology, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA
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  • For correspondence: zhiping.weng@umassmed.edu arjan.vandervelde@umassmed.edu
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ABSTRACT

Transcription factors play a key role in the regulation of gene expression. Hypersensitivity to DNase I cleavage has long been used to gauge the accessibility of genomic DNA for transcription factor binding and as an indicator of regulatory genomic locations. An increasing amount of ChIP-seq data on a large number of TFs is being generated, mostly in a small number of cell types. DNase-seq data are being produced for hundreds of cell types. We aimed to develop a computational method that could combine ChIP-seq and DNase-seq data to predict TF binding sites in a wide variety of cell types. We trained and tested a logistic regression model, called LR-DNase, to predict binding sites for a specific TF using seven features derived from DNase-seq and genomic sequence. We calculated the area under the precision-recall curve at a false discovery rate cutoff of 0.5 for the LR-DNase model, a number of logistic regression models with fewer features, and several existing state-of-the-art TF binding prediction methods. The LR-DNase model outperformed existing unsupervised and supervised methods. Additionally, for many TFs, a model that uses only two features, DNase-seq reads and motif score, was sufficient to match the performance of the best existing methods.

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Posted October 24, 2016.
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LR-DNase: Predicting TF binding from DNase-seq data
Arjan van der Velde, Michael Purcaro, William Stafford Noble, Zhiping Weng
bioRxiv 082594; doi: https://doi.org/10.1101/082594
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LR-DNase: Predicting TF binding from DNase-seq data
Arjan van der Velde, Michael Purcaro, William Stafford Noble, Zhiping Weng
bioRxiv 082594; doi: https://doi.org/10.1101/082594

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