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A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data

View ORCID ProfileQiongshi Lu, View ORCID ProfileYiming Hu, View ORCID ProfileJiehuan Sun, View ORCID ProfileYuwei Cheng, View ORCID ProfileKei-Hoi Cheung, View ORCID ProfileHongyu Zhao
doi: https://doi.org/10.1101/018093
Qiongshi Lu
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Yiming Hu
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Jiehuan Sun
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Yuwei Cheng
Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
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Kei-Hoi Cheung
Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA VA Connecticut Healthcare System, West Haven, CT, USA
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Hongyu Zhao
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA VA Connecticut Healthcare System, West Haven, CT, USA
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  • For correspondence: hongyu.zhao@yale.edu
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Abstract

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu

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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 April 15, 2015.
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A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data
Qiongshi Lu, Yiming Hu, Jiehuan Sun, Yuwei Cheng, Kei-Hoi Cheung, Hongyu Zhao
bioRxiv 018093; doi: https://doi.org/10.1101/018093
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A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data
Qiongshi Lu, Yiming Hu, Jiehuan Sun, Yuwei Cheng, Kei-Hoi Cheung, Hongyu Zhao
bioRxiv 018093; doi: https://doi.org/10.1101/018093

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