title: Spectacle: fast chromatin state annotation using spectral learning.
abstract: Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle.
authors.forename: Jimin
authors.initials: J
authors.lastname: Song
authors.forename: Kevin C
authors.initials: KC
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001327:Autoimmune Diseases / Q000235:genetics; D002460:Cell Line; D002843:Chromatin / Q000378:metabolism*; D004742:Enhancer Elements, Genetic; D044127:Epigenesis, Genetic; D005828:Genetics, Population; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D058977:Molecular Sequence Annotation; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011401:Promoter Regions, Genetic; D013045:Species Specificity; D024363:Transcription Initiation Site
chemical_list: D002843:Chromatin