Additional file 1: of Genome-wide enhancer annotations differ significantly in genomic distribution, evolution, and function
Creators: name: Benton, Mary
name: Talipineni, Sai
name: Kostka, Dennis
name: Capra, John
Publisher: name: figshare
Year: 2019
Subjects: subject: Space Science
subject: Medicine
subject: Pharmacology
subject: Biotechnology
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Developmental Biology
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: 60506 Virology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Computational Biology

description: Figure S1. Enhancer sets across all contexts considered differ in both number (A) and length (B): Gm12878; Heart. Figure S2. Enhancer sets have low amounts of overlap with each other in bp-wise comparisons. Figure S3. Enhancer sets overlap more than expected by chance in element-wise comparisons. Figure S4. Enhancer sets have low amounts of overlap with each other in element-wise comparisons. Figure S5. Enhancers identified by different methods differ in enrichment for base pair overlap with functional attributes. Figure S6. Enhancer identification strategies recognize different subsets of validated enhancers. Figure S7. K562 enhancer sets have similar low levels of enrichment for activating regions validated by Sharpr-MPRA. Figure S8. Even among the variants in each functional LD block (r2 &gt; 0.9) with the most enhancer set overlap, there is substantial disagreement between enhancer identification methods. Figure S9. Pairwise similarity for GO Molecular Function (MF) enrichments for enhancer sets based on JEME’s putative mappings to target genes in K562 (A), Gm12878 (B), liver (C), and heart (D). Figure S10. Pairwise similarity for GO Biological Process (BP) for enhancer sets based on JEME’s putative mappings to target genes in K562 (A), Gm12878 (B), liver (C), and heart (D). Figure S11. Pairwise similarity for GO Molecular Function (MF) enrichments from GREAT for liver enhancer sets. Figure S12. There is low pairwise similarity between GO Molecular Function (MF) enrichments calculated with GREAT for enhancer sets in the same context. Figure S13. There is low pairwise similarity between GO Biological Process (BP) enrichments calculated with GREAT for enhancer sets in the same context. Figure S14. Clustering enhancer sets on similarity of enriched transcription factor binding motifs illustrates different clustering of methods. Figure S15. Regions identified as enhancers by multiple methods do not have higher confidence scores than regions identified by a single method. Figure S16. Score distributions for K562 enhancer sets are similar between regions identified as enhancers by a single method and those identified by multiple methods: (A) H3K27acPlusH3K4me1, (B) H3K27acMinusH3K4me3, (C) DNasePlusHistone, (D) EncodeEnhancerlike, and (E) FANTOM. Figure S17. Score distributions for Gm12878 enhancer sets are similar between regions identified as enhancers by a single method and those identified by multiple methods: (A) H3K27acPlusH3K4me1, (B) H3K27acMinusH3K4me3, (C) DNasePlusHistone, (D) EncodeEnhancerlike, and (E) FANTOM. Figure S18. Score distributions for heart enhancer sets are similar between regions identified as enhancers by a single method and those identified by multiple methods: (A) H3K27acPlusH3K4me1, (B) H3K27acMinusH3K4me3, (C) DNasePlusHistone, (D) EncodeEnhancerlike, and (E) FANTOM. Figure S19. Enrichment for functional attributes is not significantly different between regions identified as enhancers by a single method and those identified by multiple methods when focusing on the top 100 predictions from each method. Figure S20. Same as Fig. S19, but considering the top 500 predictions from each method. Table S1. The average distance (in bp) to the closest TSS over all enhancers identified by each method in each cellular context. Table S2. Summary statistics for pairwise percent overlap, both in a base pair and element-wise comparison. Table S3. Number of observed VISTA heart positive and VISTA negative overlaps for each context and enhancer identification method. Table S4. Curated list of relevant GWAS phenotypes for liver (n = 50) and heart (n = 169). Table S5. Enrichments for overlap with context-specific SNPs in liver and heart. Table S6. Number of overlapping GWAS SNPs per enhancer identification method and context. Table S7. Enrichments for overlap with context-specific eQTL in liver and heart. Table S8. Number of overlapping GTEx eQTL per enhancer identification method and context. Table S9. Number of target genes mapped to each enhancer set by JEME. For K562 and Gm12878, p300 and GRO-cap are not included in this mapping. Table S11. Number of enhancers removed by length filtering. (DOCX 36740 kb)