Journal: PLoS ONE

Article Title: Accurate Promoter and Enhancer Identification in 127 ENCODE and Roadmap Epigenomics Cell Types and Tissues by GenoSTAN

doi: 10.1371/journal.pone.0169249

Figure Lengend Snippet: GenoSTAN was learned on all 127 cell types and tissues (GenoSTAN-127) using the five core marks H3K4me1, H3K4me3, H3K36me3, H3K27me3, H3K9me3 and an input control (ChromHMM-15 was learned on the same data). To improve accuracy additional histone modifications H3K27ac, H3K9ac and DNase-Seq were used to learn another model (GenoSTAN-20) on a subset of 20 cell types and tissues, where the marks were available. (A) Performance of chromatin states in recovering FANTOM5 CAGE tags in 127 cell types. CAGE tags were verlapped with chromatin states wihout the use of cell type information. Cumulative FDR and recall are calculated by subsequently adding states (in order of increasing FDR). (B) Performance of chromatin states in recovering GRO-cap transcription start sites in two cell types where GRO-cap data was available. (C) The same as in (B) for ENCODE HOT regions for five cell types where annotation of HOT regions was available. (D) Recall of FANTOM5 promoters and enhancers by predicted promoters and enhancersis plotted to assess how well models distinguish promoters from enhancers. (E) The fraction of predicted enhancer segments bound by individual TFs is shown for different studies. GenoSTAN enhancers are more frequently bound by TFs than those from other studies.

Article Snippet: This approach leverages extensive genome-wide datasets of chromatin-immunoprecipitation followed by sequencing (ChIP-Seq) of transcription factors (TFs), histone modifications, or Cap analysis gene expression (CAGE) that have been generated by collaborative projects such as ENCODE [ , ], NIH Roadmap Epigenomics [ ], BLUEPRINT [ ] and FANTOM [ , ].

Techniques: Control