RT Journal Article SR Electronic T1 A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types JF bioRxiv FD Cold Spring Harbor Laboratory SP 086025 DO 10.1101/086025 A1 Maxwell W. Libbrecht A1 Oscar Rodriguez A1 Zhiping Weng A1 Jeffrey A. Bilmes A1 Michael M. Hoffman A1 William S. Noble YR 2018 UL http://biorxiv.org/content/early/2018/04/26/086025.abstract AB 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.Author Summary Genome annotation algorithms are an effective class of tools for understanding the function of the genome. These algorithms take as input a set of genome-wide measurements about the activity at each base pair in a given tissue, such as where a given protein is binding or how accessible the DNA is to being read by a protein. The genome is then partitioned and each segment is assigned a label such that positions with the same label exhibit similar patterns in the input data. Such annotations are widely used for many applications, such as to understand the mechanism of impact of a given genetic variant. Here we present, to our knowledge, the most comprehensive set of genome annotations created so far, encompassing 164 human cell types and including 1,615 genomics data sets. These comprehensive annotations are made possible by a strategy that automates the previous interpretation step. Furthermore, we present several methodological innovations that make these genome annotations more useful.