Jointly characterizing epigenetic dynamics across multiple human cell types.
Advanced sequencing technologies have generated a plethora of data for many chromatin marks in multiple tissues and cell types, yet there is lack of a generalized tool for optimal utility of those data. A major challenge is to quantitatively model the epigenetic dynamics across both the genome and many cell types for understanding their impacts on differential gene regulation and disease. We introduce IDEAS, an integrative and discriminative epigenome annotation system, for jointly characterizing epigenetic landscapes in many cell types and detecting differential regulatory regions. A key distinction between our method and existing state-of-the-art algorithms is that IDEAS integrates epigenomes of many cell types simultaneously in a way that preserves the position-dependent and cell type-specific information at fine scales, thereby greatly improving segmentation accuracy and producing comparable annotations across cell types.
Dept. of Statistics, Penn State University, 325 Thomas Building, University Park, PA 16803, USA yzz2@psu.edu.
Yu
Y
Zhang
Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, Penn State University, 101 Huck Life Sciences Building, University Park, PA 16802, USA.
Lin
L
An
Dept. of Biochemistry and Molecular Biology, Penn State School of Medicine, 500 University Drive, MC H171, Hershey, PA 17033, USA.
Feng
F
Yue
Dept. of Biochemistry and Molecular Biology, Penn State University, 304 Wartik Laboratory, University Park, PA 16802, USA.
Ross C
RC
Hardison
D002460:Cell Line; D030541:Databases, Genetic; D044127:Epigenesis, Genetic; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D019655:Quantitative Trait, Heritable