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GRO_seq_TFSEE
Histone_TFSEE
gene_matrix
intervene_test
.gitmodules
ChIP.Rmd
E-MTAB-1086.sdrf.txt
ES_D0_H3K27ac_filtered_peaks.bed
ES_D0_Histone_enhancers.bed
ES_D0_Histone_enhancers_1kb.bed
ES_D0_gro-seq_enhancers.bed
ES_D0_gro-seq_enhancers_1kb.bed
ES_D10_H3K27ac_filtered_peaks.bed
ES_D10_Histone_enhancers.bed
ES_D10_Histone_enhancers_1kb.bed
ES_D10_gro-seq_enhancers.bed
ES_D10_gro-seq_enhancers_1kb.bed
ES_D2_H3K27ac_filtered_peaks.bed
ES_D2_Histone_enhancers.bed
ES_D2_Histone_enhancers_1kb.bed
ES_D2_gro-seq_enhancers.bed
ES_D2_gro-seq_enhancers_1kb.bed
ES_D5_H3K27ac_filtered_peaks.bed
ES_D5_Histone_enhancers.bed
ES_D5_Histone_enhancers_1kb.bed
ES_D5_gro-seq_enhancers.bed
ES_D5_gro-seq_enhancers_1kb.bed
ES_D7_H3K27ac_filtered_peaks.bed
ES_D7_Histone_enhancers.bed
ES_D7_Histone_enhancers_1kb.bed
ES_D7_gro-seq_enhancers.bed
ES_D7_gro-seq_enhancers_1kb.bed
ES_Histone_universe_enhancers.tsv
GRO_seq_tunning.xlsx
GRO_seq_tunning_updated.xlsx
Gro_percentages.png
H3K27ac_all_filtered_peaks.bed
H3K27ac_all_filtered_peaks.tsv
H3K27ac_distribution.png
H3K27ac_filtered_peaks.bed
H3K27ac_filtered_peaks.tsv
H3K4me1_all_filtered_peaks.bed
H3K4me1_all_filtered_peaks.tsv
H3K4me1_distribution.png
H3K4me1_filtered_peaks.bed
H3K4me1_filtered_peaks.tsv
H3K4me3_3kb_flanking.bed
H3K4me3_all_filtered_peaks.bed
H3K4me3_all_filtered_peaks.tsv
H3K4me3_distribution.png
H3K4me3_filtered_peaks.bed
H3K4me3_filtered_peaks.tsv
Histone_h3k27ac_filtered_peaks.bed
Histone_h3k27ac_filtered_peaks.tsv
Histone_h3k4me1_filtered_peaks.bed
Histone_h3k4me1_filtered_peaks.tsv
Histone_pe_filtered_peaks.bed
Histone_pe_filtered_peaks.tsv
Histone_percentages.png
Histone_putative_enhancers.bed
PMID_25842977_SraRunInfo.csv
README.md
RNA_distribution.png
Rna-seq_star.sh
SSP_distribution.png
SSP_filtered_peaks.bed
SSP_filtered_peaks.tsv
SSP_filtered_peaks_merged.bed
SSP_filtered_peaks_sum.tsv
SSP_sum.tsv
SUMMARY.xlsx
SUNP_distribution.png
SUNP_filtered_peaks.bed
SUNP_filtered_peaks.tsv
SUNP_sum.tsv
biotypes.txt
call-peaks-macs-single.sh
call-transcripts-timecourse-norep.R
call-transcripts-timecourse-norep.sh
call-transcripts.R
call-transcripts.sh
cutoff_analysis.py
cutoff_analysis_gro.py
define_histone_window.py
define_short_transcripts.py
define_small_transcripts.py
enhancers_1kb.bed
enhancers_renamed.bed
enhancers_to_gene.bed
excluded_3kb_flanking.bed
excluded_regions_processing.sh
extend_histone_1kb.py
fpkm.py
gencode.v19.annotation.gtf
gencode.v19.annotation_capped_sites.bed
gencode.v19.annotation_protein_coding.gtf
gencode.v19.annotation_protein_coding_ids.txt
gencode_tss_3kb_flanking.bed
gene-counts.sh
genomewideCorrs_above0.7_promoterPlusMinus500kb_withGeneNames_32celltypeCategories.bed8

Random Forest of Enhancer Prediction http://enhancer.ucsd.edu/renlab/RFECS_enhancer_prediction/

Methods:

  1. Call Peaks H3K27ac, H3K4me1 using threshold of 1e-2
  2. Call Peaks H3K4me3 using threshold of 1e-5
  3. Call Transcript units per cell using GRO-seq and GRO-HMM
  4. Merge H3K27ac, H3K4me1, H3K4me3 Peaks within 500bp and make universe filter for at least 1 RPKM in a cell
  5. Merge GRO-seq transcripts and filter for a. +/- 3kb from TSS of protein coding genes Gencode and H3K4me3 pekas b. Merge overlaping transcripts c. Filter for <=9kb Short-Short-Paired and Short-Unpaired d. make universe filter for at least 1 > RPKM SSP and > 2 RPM SUP in a cell
  6. Make Enhancer regions for motif search a. Short-Short-Paired +/- 500 bp center-overlap b. Short-Unpaired +/- bp TSS of transcript c. Histone data +/- bp 500 bp center of mark
  7. De novo motif analyses were performed using the command-line version of MEME (Bailey et al., 2009). The following parameters were used for motif prediction: (1) zero or one occurrence per sequence (- mod zoops); (2) number of motifs (-nmotifs 15); (3) minimum, maximum width of the motif (- minw 8, -maxw 15); and (4) search for motif in given strand and reverse complement strand (- revcomp). The predicted motifs from MEME were matched to known motifs using TOMTOM (Gupta et al., 2007).