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msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding

Anil Raj, Heejung Shim, Yoav Gilad, Jonathan K. Pritchard, Matthew Stephens
doi: https://doi.org/10.1101/012013
Anil Raj
1Department of Genetics, Stanford University, Stanford, CA, 94305
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  • For correspondence: rajanil@stanford.edu hjshim@gmail.com gilad@uchicago.edu pritch@stanford.edu mstephens@uchicago.edu
Heejung Shim
2Department of Human Genetics, University of Chicago, Chicago, IL, 60637
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  • For correspondence: rajanil@stanford.edu hjshim@gmail.com gilad@uchicago.edu pritch@stanford.edu mstephens@uchicago.edu
Yoav Gilad
2Department of Human Genetics, University of Chicago, Chicago, IL, 60637
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  • For correspondence: rajanil@stanford.edu hjshim@gmail.com gilad@uchicago.edu pritch@stanford.edu mstephens@uchicago.edu
Jonathan K. Pritchard
1Department of Genetics, Stanford University, Stanford, CA, 94305
3Department Biology, Stanford University, Stanford, CA, 94305
4Howard Hughes Medical Institute
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  • For correspondence: rajanil@stanford.edu hjshim@gmail.com gilad@uchicago.edu pritch@stanford.edu mstephens@uchicago.edu
Matthew Stephens
2Department of Human Genetics, University of Chicago, Chicago, IL, 60637
5Department of Statistics, University of Chicago, Chicago, IL, 60637
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  • For correspondence: rajanil@stanford.edu hjshim@gmail.com gilad@uchicago.edu pritch@stanford.edu mstephens@uchicago.edu
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Abstract

Motivation: Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework underestimates the substantial variation in the DNase I cleavage profiles across factor-bound genomic locations and across replicate measurements of chromatin accessibility.

Results: In this work, we adapt a multi-scale modeling framework for inhomogeneous Poisson processes to better model the underlying variation in DNase I cleavage patterns across genomic locations bound by a transcription factor. In addition to modeling variation, we also model spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-Seq peaks for those factors. Finally, we propose an extension to this framework that allows for a more flexible background model and evaluate the additional gain in accuracy achieved when the background model parameters are estimated using DNase-seq data from naked DNA. The proposed model can also be applied to paired-end ATAC-seq and DNase-seq data in a straightforward manner.

Availability: msCentipede, a Python implementation of an algorithm to infer transcription factor binding using this model, is made available at https://github.com/rajanil/msCentipede

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 29, 2014.
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msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding
Anil Raj, Heejung Shim, Yoav Gilad, Jonathan K. Pritchard, Matthew Stephens
bioRxiv 012013; doi: https://doi.org/10.1101/012013
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msCentipede: Modeling heterogeneity across genomic sites improves accuracy in the inference of transcription factor binding
Anil Raj, Heejung Shim, Yoav Gilad, Jonathan K. Pritchard, Matthew Stephens
bioRxiv 012013; doi: https://doi.org/10.1101/012013

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