README.md

@@ -4,7 +4,7 @@ This directory contains the scripts for identification of TFs maintaining multip
 ,using TFSEE.
 
 
-## Dependencies
+## Dependencies for TFSEE
 
 This code requires python 2.7+ to run.
 
@@ -27,9 +27,10 @@ pip install -r requirements.txt
 
 #### Pipeline Description
 
-This pipeline uses data from the following analysis to be used as input.
+##### Pre-processing Steps
 
 1. De novo identification of enhancers using GRO-seq and [groHMM](http://www.bioconductor.org/packages/release/bioc/html/groHMM.html)
+or ChIP-seq (H3K4me1 and H3K27ac)
 
 2. Normalize Enhancer Expression using GRO-seq: For each cell line, quantify the GRO-seq reads, RPKM, that fall within a 1 kb region around the center of the overlap for paired enhancer transcripts or from the 5′ end of unpaired enhancer transcripts
 
@@ -38,6 +39,8 @@ This pipeline uses data from the following analysis to be used as input.
 4. Motif Predictions: De novo motif analyses on a 1 kb region of expressed enhancers for each cell line using [MEME](http://meme-suite.org/) and  matched to known motifs using TOMTOM and  [JASPAR](http://jaspar.genereg.net/)
 
 5. Normalize Transcription Factor Expression using RNA-seq:  For each cell line, quantify the RNA-seq reads, FPKM, for each  transcription factor that is a binding target for the motifs
+* RNA-seq analysis: RNA-seq_star.sh
+* FPKM processing RNA-seq: rnaseq_processing.sh
 
 6. Calculate TFSEE score to determining cell-type specific enhancer activity, generating:
 * unsupervised hierarchical clustering
@@ -45,15 +48,41 @@ This pipeline uses data from the following analysis to be used as input.
 * boxplot representations
 * rank order TF plots
 
+#### Data Source
 
-### Scripts
+All dta available from NCBI’s Gene Expression Omnibus [@url:https://www.ncbi.nlm.nih.gov/geo/] or EMBL-EBI’s ArrayExpress [@url:http://www.ebi.ac.uk/arrayexpress/] repositories using the accession numbers listed:
+
+| Assay | Accessions |
+| :--------------------: | :------------------------------------------------------------: |
+| GRO-seq | GSM1316306, GSM1316313, GSM1316320, GSM1316327, GSM1316334 |
+| H3K4me3 ChIP-seq | ERR208008, ERR208014, ERR207998, ERR20798, ERR207999 |
+| H3K4me1 ChIP-seq | GSM1316302, GSM1316303, GSM1316309, GSM1316316, GSM1316317, GSM1316310, GSM1316323, GSM1316324, GSM1316330, GSM1316331 |
+| H3K27ac ChIP-seq | GSM1316300, GSM1316301, GSM1316307, GSM1316308, GSM1316314, GSM1316315, GSM1316321, GSM1316322, GSM1316328, GSM1316329 |
+| Input ChIP-seq | ERR208001, ERR208012, ERR207984, ERR208011, ERR207986, GSM1316304, GSM1316305, GSM1316311, GSM1316312, GSM1316318, GSM1316319, GSM1316325, GSM1316326, GSM1316332, GSM1316333 |
+| RNA-seq | ERR266333, ERR266335, ERR266337, ERR266338, ERR266341, ERR266342, ERR266344, ERR266346, ERR266349, ERR266351 |
+
+
+### Main Scripts
 
 - Compute TFSEE to identify cognate transcription factors are under 'analysis'
+ * Applicable to either enhancer method:
+    * Get H3K4me3 peaks: h3k4me3_processing.sh
+    * Get H3K27ac peaks: h3k27ac_processing.sh
+    * Get H3K4me1 peaks: h3k4me1_processing.sh
+    * Exclude regions based on H3K4me3 and promoters: excluded_regions_processing.sh
+    * RNA-seq analysis: RNA-seq_star.sh
+    * FPKM processing RNA-seq: rnaseq_processing.sh
  * TFSEE using GRO-seq:
-   * GRO_seq_TFSEE:
-     * matrix_analysis.py: TFSEE score integration
-     * rank_order.py: Rank order TF's clusters
+      * Tune GroHMM: tune-hmm.sh
+      * Call Transcripts: call-transcripts.sh
+      * Make universe of Enhancers: groseq_processing.sh
+      * GRO_seq_TFSEE:
+         * TFSEE pre-processing: tfsee_processing.sh
+         * TFSEE score integration: matrix_analysis.py
+         * Rank order TF's clusters: rank_order.py
  * TFSEE using histone modifications ChIP-seq:
-   * GRO_seq_TFSEE:
-     * matrix_analysis.py: TFSEE score integration
-     * rank_order.py: Rank order TF's clusters
+      * Make universe of Enhancers: histone_centered_processing.sh
+      * Histone_TFSEE:
+         * TFSEE pre-processing: tfsee_processing.sh
+         * TFSEE score integration: matrix_analysis.py
+         * Rank order TF's clusters: rank_order.py