BioProject

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. More...

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (Decomposition and Classification of Epigenomic Tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences between tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic and prognostic strategies. Overall design: Examination of histone modifications H3K27ac, H3K4me3, and H3K4me1 with controls in fresh-frozen tissues from 27 uterine leiomyoma samples from 25 patients, and matched healthy myometrium for 21 of these patients. Basecalls were performed using RTA v.2.4.11 on Nextseq instrument. Reads were adapter trimmed using TrimGalore v0.4.4 with parameters --illumina --stringency 13. After adapter trimming reads were aligned to the hg19 genome using Bowtie2 v2.3.2 with parameters --end-to-end --sensitive --score-min L,-1.5,-0.3 --no-unal. Aligned reads were filtered for PCR duplicates using Picard v2.10.1 and then further filtered using samtools v1.7 with parameters -b -F 3588 -q 13. The filtered reads were sorted and converted to bed format. Lastly reads overlapping repeat regions identified by RepeatMaser or segmental duplication regions identified by Variant Tools were removed using bedtools v2.26.0 subtract -A. The code for generating the processed files from the filtered bed files can be found at https://github.com/jssong-lab/DeCET. Peak calling for histone modification ChIP-seq was performed relative to a corresponding control using MACS2 (options –broad -g hs –broad-cutoff 0.05) after removing reads overlapping repeat regions. Less...