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1.
Figure 5

Figure 5. Comparison of enhancer predictions using RFECS, ChromaGenSVM, CSIANN and Chromia.. From: RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State.

A.) In CD4. True positive rates were measured as overlap with either DNase-I hypersensitive sites (DHS), p300 or CBP binding sites, while false positives were measured as overlap with UCSC TSS. B.) In H1. True positive rates were measured as overlap with either DNase-I hypersensitive sites (DHS), p300 or transcription factor binding sites such as NANOG, OCT4 and SOX2, while false positives were measured as overlap with UCSC TSS.

Nisha Rajagopal, et al. PLoS Comput Biol. 2013 March;9(3):e1002968.
2.
Figure 1

Figure 1. Histone modification patterns at distal p300 binding sites in H1 and IMR90.. From: RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State.

A.)Chromatin states for p300 binding sites in H1 cells. B.)Chromatin states for p300 binding sties identified in IMR90 cells, identified by clustering using ChromaSig [48]. The heatmap shows RPKM-normalized histone modification levels in 100 bp bins from −5 to +5 kb along p300 binding sites overlapping DHS and distal to known TSS.

Nisha Rajagopal, et al. PLoS Comput Biol. 2013 March;9(3):e1002968.
3.
Figure 6

Figure 6. Enhancer predictions in ENCODE cell-lines using RFECS.. From: RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State.

A.)Validation Rate in the 12 cell-types measured by overlap with DNase-I HS, B.)Misclassification Rate in the cell-types measured as overlap of UCSC TSS, C.)Average false discovery rate (FDR) over the 22 autosomal chromosomes for each cell-type plotted as a function of voting percentage of trees, D.)Validation rate and misclassification rate for each cell-type at a FDR of 5% with number of enhancer predictions shown above the bar.

Nisha Rajagopal, et al. PLoS Comput Biol. 2013 March;9(3):e1002968.
4.
Figure 4

Figure 4. Validation rate and Misclassification rate of enhancers predicted using RFECS in H1 and IMR90.. From: RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State.

A.) Validation Rate in H1 measured by overlap with DNase-I HS, p300, NANOG, OCT4 or SOX2, B.) Misclassification Rate in H1 measured as overlap of UCSC TSS, C.) Validation Rate in IMR90 measured by overlap with DNase-I HS or p300, D.) Misclassification Rate in IMR90 measured as overlap of UCSC TSS, versus total number of enhancers determined by taking different enrichment cutoffs, are shown for all 24 modifications (red), predicted minimal set of H3K4me1/H3K4me2/H3K4me3 (green) and conventionally used marks H3K4me1/H3K4me3 (black) or H3K4me1/H3K4me3/H3K27ac (blue). E.) Comparison of average validation rates for enhancer predictions using all combinations of 3 histone modifications for 2 replicates of H1.

Nisha Rajagopal, et al. PLoS Comput Biol. 2013 March;9(3):e1002968.
5.
Figure 3

Figure 3. Out-of-bag variable importance of histone modifications in enhancer prediction.. From: RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State.

The average variable of histone modifications across 5 cross-sections of data in 2 sets of replicates as well as averaged replicates using all 24 modifications in A.)H1 and B.)IMR90 cells. Out-of-bag variable importance was calculated from the random-forest based classification of p300 binding sites against TSS+genomic background. Robust appearance of H3K4me1, H3K4me3 and H3K4me2 among the most important marks across replicates and cell types, indicates these may form a minimal set for prediction of enhancers. Differences observed in correlation clustering of the same 24 modifications in C.)H1 and D.)IMR90 explain some of the differences in ordering of variables in the two cell types. Same non-black colors of modifications indicate clusters that co-occur in both cell-types.

Nisha Rajagopal, et al. PLoS Comput Biol. 2013 March;9(3):e1002968.
6.
Figure 2

Figure 2. Performance of RFECS for enhancer predictions in H1 and IMR90 cells.. From: RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State.

Area under the 5-fold cross-validated ROC curve decreases with increase in number of trees stabilizing gradually in A.)H1 and B.)IMR90 cells. C.)Validation Rate of enhancer predicted in H1 cells, as measured by overlap with DNase-I HS and binding sites of p300, NANOG, OCT4 and SOX2. D.)Misclassification Rate of enhancer predicted using RFECS in H1 as measured as overlap of UCSC TSS, E.)Validation Rate of enhancers predicted by RFECS in IMR90 as measured by overlap with DNase-I HS or p300 binding sites in the same cells. F.)Misclassification Rate of enhancers predicted by RFECS in IMR90 as measured by overlap with UCSC TSS, versus total number of enhancers (upto 40000 enhancers) determined by taking different enrichment cutoffs, are shown for forest trained in the same cell type (⋅red), forest trained in other cell type and predictions made on modifications with averaged RPKM (⋅black), replicate 1 only (⋅blue), and replicate 2 only (⋅green). Training on one replicate and prediction on the other replicate of the same cell-type are indicated by asterisks.

Nisha Rajagopal, et al. PLoS Comput Biol. 2013 March;9(3):e1002968.

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