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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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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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