University of Massachusetts Medical School Faculty Publications

UMMS Affiliation

Program in Bioinformatics and Integrative Biology

Publication Date

4-26-2018

Document Type

Article Preprint

Disciplines

Cells | Computational Biology | Genetic Phenomena | Genomics

Abstract

Semi-automated genome annotation methods such as Segway enable understanding of chromatin activity. Here we present chromatin state annotations of 164 human cell types using 1,615 genomics data sets. To produce these annotations, we developed a fully-automated annotation strategy in which we train separate unsupervised annotation models on each cell type and use a machine learning classifier to automate the state interpretation step. Using these annotations, we developed a measure of the functional importance of each genomic position called the "functionality score," which allows us to aggregate information across cell types into a multi-cell type view. This score provides a measure of importance directly attributable to a specific activity in a specific set of cell types. In contrast to evolutionary conservation, this measure is not biased to detect only elements shared with related species. Using the functionality score, we combined all our annotations into a single cell type-agnostic encyclopedia that catalogs all human functional regulatory elements, enabling easy and intuitive interpretation of the effect of genome variants on phenotype, such as in disease-associated, evolutionarily conserved or positively selected loci. These resources, including cell type-specific annotations, enyclopedia, and a visualization server, are available at http://noble.gs.washington.edu/proj/encyclopedia.

Keywords

DNA, Semi-automated genome annotation, chromatin, encyclopedia, genomics

Rights and Permissions

The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY 4.0 International license.

DOI of Published Version

10.1101/086025

Source

bioRxiv 086025; doi: https://doi.org/10.1101/086025. Link to preprint on bioRxiv service.

Journal/Book/Conference Title

bioRxiv

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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