Motif enrichment with pycisTarget using mouse liver ChIP-seq regions

[1]:

%matplotlib inline
import pycistarget
pycistarget.__version__

pycisTarget is a python module that allows to perform motif enrichment analysis and derive genome-wide cistromes implementing cisTarget (Herrmann et al., 2012; Imrichova et al., 2015). In addition, de novo cistromes can also be derived (via Homer (Heinz et al., 2010)) and pycisTarget also includes a novel approach to derive differentially enriched motifs and cistromes between one or more groups of regions, named Differentially Enriched Motifs (DEM).

0. Getting your input region sets

pycisTarget uses as input a dictionary containing the region set name as label and regions (as pyranges) as values. In this tutorial we will use 4 region sets, which correspond to the top 5K ChIP-seq peaks of Hnf4a, Foxa1, Cebpa and Onecut1 in the mouse liver (Ballester et al., 2014). We can easily read the data in the correct format using list comprehensension.

[2]:

import pyranges as pr
import os
path_to_region_sets = '/staging/leuven/stg_00002/lcb/cbravo/Liver/Multiome/pycistopic/GEMSTAT/ChIP/All_summits'
region_sets_files = ['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K.bed', 'Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K.bed', 'Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K.bed', 'Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K.bed']
region_sets = {x.replace('.bed', ''):pr.read_bed(os.path.join(path_to_region_sets, x)) for x in region_sets_files}

Apart from the cisTarget method, pycisTarget includes wrapper functions to use Homer (for de novo motif enrichment) and a new implementation relying in statistical testing between sets of regions using Cluster-Buster scores (DEM). We will first describe how to perform motif enrichment and form cistromes using Homer.

1. cisTarget

A. Creating cisTarget databases

To run cisTarget you will need to provide a ranking database (that is, a feather file with a dataframe with motifs as rows, genomic regions as columns and their ranked position [based on cis-regulatory module (CRM) score (Frith et al., 2003)] as values). We provide those databases for human (hg38, hg19), mouse (mm10, mm9) and fly (dm3, dm6) at https://resources.aertslab.org/cistarget/.

In addition, if you want to use other regions or genomes to build your databases, we provide a step-by-step tutorial and scripts at https://github.com/aertslab/create_cisTarget_databases. Below you can find the basic steps to do so:

[ ]:

%%bash
#### Variables
genome_fasta = 'PATH_TO_GENOME_FASTA'
region_bed = 'PATH_TO_BED_FILE_WITH_GENOMIC_REGIONS_FOR_DATABASE'
region_fasta = 'PATH_TO_FASTA_FILE_WITH_GENOMIC_REGIONS_FOR_DATABASE'
database_suffix = 'SUFFIX_FOR_DATABASE_FILE'
path_to_motif_collection = 'PATH_TO_MOTIF_COLLECTION_IN_CLUSTER_BUSTER_FORMAT'
motif_list = 'PATH_TO_FILE_WITH_MOTIFS_TO_SCORE'
n_cpu = 'NUMBER_OF_CORES'
#### Get fasta sequences
module load BEDTools # In our system, load BEDTools
bedtools getfasta -fi ${genome_fasta} -bed ${region_bed} > ${region_fasta}
#### Activate environment
my_conda_initialize # In our system, initialize conda
conda activate /staging/leuven/stg_00002/lcb/ghuls/software/miniconda3/envs/create_cistarget_databases
#### Set ${create_cistarget_databases_dir} to https://github.com/aertslab/create_cisTarget_databases
create_cistarget_databases_dir='/staging/leuven/stg_00002/lcb/ghuls/software/create_cisTarget_databases'
#### Score the motifs
${create_cistarget_databases_dir}/create_cistarget_motif_databases.py \
-f ${region_fasta} \
-M ${path_to_motif_collection} \
-m ${motif_list} \
-o ${database_suffix} \
-t ${n_cpu} \
-l \
-s 555
done
#### Create rankings
motifs_vs_regions_scores_feather = 'PATH_TO_MOTIFS_VS_REGIONS_SCORES_DATABASE'
${create_cistarget_databases_dir}/convert_motifs_or_tracks_vs_regions_or_genes_scores_to_rankings_cistarget_dbs.py -i ${motifs_vs_regions_scores_feather} -s 555

B. Running cisTarget

For running cisTarget there are some relevant parameters:

  • ctx_db: Path to the cisTarget database to use, or a preloaded cisTargetDatabase object. In this tutorial we will use the precomputed mm10 database (using SCREEN regions), available at https://resources.aertslab.org/cistarget/.

  • region_sets: The input sets of regions

  • specie: Specie to which region coordinates and database belong to. To annotate motifs to TFs using cisTarget annotations, possible values are ‘mus_musculus’, ‘homo_sapiens’ or ‘drosophila_melanogaster’. If any other value, motifs will not be annotated to a TF unless providing a customized annotation.

  • fraction_overlap: Minimum overlap fraction (in any direction) to map input regions to regions in the database. Default: 0.4.

  • auc_threshold: Threshold to calculate the AUC. For human and mouse we recommend to set it to 0.005 (default), for fly to 0.01.

  • nes_threshold: NES threshold to calculate the motif significant. Default: 3.0

  • rank_threshold: Percentage of regions to use as maximum rank to take into account for the region enrichment recovery curve. By default, we use 5% of the total number of regions in the database.

  • annotation: Annotation to use to form the cistromes. Default: [‘Direct_annot’, ‘Motif_similarity_annot’, ‘Orthology_annot’, ‘Motif_similarity_and_Orthology_annot’]. Since we are using the clustered motif database, we will not use motif similatiry annotations (which only rely on Tomtom q-values), since it is implicit on the clusters.

  • annotation_version : Motif collection version. Here we use the clustered v10 database (‘v10nr_clust’).

  • path_to_motif_annotations : File with motif annotations. These files are available at https://resources.aertslab.org/cistarget/motif2tf .

  • n_cpu: Number of cpus to use during calculations.

[3]:

# Load cistarget functions
from pycistarget.motif_enrichment_cistarget import *

[5]:

# Run, using precomputed database
cistarget_dict = run_cistarget(ctx_db = '/staging/leuven/stg_00002/icistarget-data/make_rankings/v10_clust/CTX_mm10/CTX_mm10_SCREEN3_no_bg_with_mask/CTX_mm10_SCREEN3_no_bg_with_mask.regions_vs_motifs.rankings.v2.feather',
                                                      region_sets = region_sets,
                                                      specie = 'mus_musculus',
                                                      auc_threshold = 0.005,
                                                      nes_threshold = 3.0,
                                                      rank_threshold = 0.05,
                                                      annotation = ['Direct_annot', 'Orthology_annot'],
                                                      annotation_version = 'v10nr_clust',
                                                      path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/cluster_motif_collection_V10_no_desso_no_factorbook/snapshots/motifs-v10-nr.mgi-m0.00001-o0.0.tbl',
                                                      n_cpu = 4,
                                                      _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

2022-08-04 09:14:15,645 cisTarget    INFO     Reading cisTarget database
(ctx_internal_ray pid=30473) 2022-08-04 09:14:41,873 cisTarget    INFO     Running cisTarget for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K which has 4924 regions
(ctx_internal_ray pid=30476) 2022-08-04 09:14:41,925 cisTarget    INFO     Running cisTarget for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K which has 4715 regions
(ctx_internal_ray pid=30475) 2022-08-04 09:14:42,008 cisTarget    INFO     Running cisTarget for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K which has 5019 regions
(ctx_internal_ray pid=30474) 2022-08-04 09:14:42,100 cisTarget    INFO     Running cisTarget for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K which has 3777 regions
(ctx_internal_ray pid=30473) 2022-08-04 09:14:54,544 cisTarget    INFO     Annotating motifs for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K
(ctx_internal_ray pid=30474) 2022-08-04 09:14:54,715 cisTarget    INFO     Annotating motifs for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(ctx_internal_ray pid=30476) 2022-08-04 09:14:55,520 cisTarget    INFO     Annotating motifs for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K
(ctx_internal_ray pid=30475) 2022-08-04 09:14:56,065 cisTarget    INFO     Annotating motifs for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
(ctx_internal_ray pid=30473) 2022-08-04 09:14:57,050 cisTarget    INFO     Getting cistromes for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K
(ctx_internal_ray pid=30474) 2022-08-04 09:14:57,333 cisTarget    INFO     Getting cistromes for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(ctx_internal_ray pid=30476) 2022-08-04 09:14:58,260 cisTarget    INFO     Getting cistromes for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K
(ctx_internal_ray pid=30475) 2022-08-04 09:14:58,938 cisTarget    INFO     Getting cistromes for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
2022-08-04 09:15:02,779 cisTarget    INFO     Done!

[5]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/cisTarget/cisTarget_dict.pkl', 'wb') as f:
  pickle.dump(cistarget_dict, f)

C. Exploring cisTarget results

We can load the results for exploration.

[6]:

# Load
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/cisTarget/cisTarget_dict.pkl', 'rb')
cistarget_dict = pickle.load(infile)
infile.close()

To visualize motif enrichment results, we can use the cisTarget_results() function:

[7]:

cistarget_results(cistarget_dict, name='Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K')

[7]:
Logo Region_set Direct_annot Orthology_annot NES AUC Rank_at_max Motif_hits
metacluster_46.4 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe, Cebpb, Cebpd, Cebpg, Hlf, Cebpa Cebpe, Hes2, Cebpb, Ep300, Cebpd, Cebpg, Gatad2a, Cebpa, Dbp 29.343196 0.097521 55485.0 2661
homer__ATTGCGCAAC_CEBP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpb NaN 24.940297 0.083402 55526.0 2148
cisbp__M01815 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 18.255857 0.061966 55529.0 1913
swissregulon__mm__Cebpe Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 13.044653 0.045254 55394.0 1454
swissregulon__hs__CEBPB Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpb 11.735067 0.041054 55112.0 1303
transfac_pro__M01869 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpg NaN 10.872791 0.038289 55512.0 1477
transfac_pro__M04761 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hsf1 10.672929 0.037648 55521.0 1433
taipale_tf_pairs__GCM1_CEBPB_MTRSGGGNNNNNTTRCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Gcm1, Cebpb 10.544153 0.037235 9621.0 496
taipale_tf_pairs__GCM1_CEBPB_MTRSGGGNNNNNNTTRCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Gcm1, Cebpb 9.960710 0.035364 7159.0 372
taipale_tf_pairs__ATF4_CEBPB_NNATGAYGCAAYN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpb, Atf4 9.523663 0.033963 5333.0 266
taipale_tf_pairs__ATF4_CEBPD_NGATGATGCAATNN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpd, Atf4 9.413217 0.033608 16238.0 417
taipale_tf_pairs__CEBPG_ATF4_NNATGAYGCAAT_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Atf4, Cebpg 8.875060 0.031883 52734.0 974
taipale_tf_pairs__TEAD4_CEBPD_RGWATGYNNTTRCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tead4, Cebpd 8.650575 0.031163 55459.0 1425
metacluster_156.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Ddit3, Atf4, Cebpg Myc, Cebpg, Atf3, Ddit3, Atf4 8.590335 0.030970 55477.0 1098
taipale_tf_pairs__GCM1_CEBPB_ATRSGGGNNNNTTRCGYAAN_CAP_repr Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Gcm1, Cebpb 8.588150 0.030963 7559.0 322
taipale_tf_pairs__ATF4_TEF_RNMTGATGCAATN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Atf4, Tef 8.358196 0.030225 49968.0 905
transfac_pro__M12588 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Ddit3 8.168929 0.029618 55436.0 1160
taipale_tf_pairs__TEAD4_CEBPD_NTTRCGYAANNNNNNNNRGWATGY_CAP_repr Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tead4, Cebpd 7.872247 0.028667 13534.0 492
taipale_tf_pairs__FLI1_CEBPB_RNCGGANNTTGCGCAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Fli1, Cebpb 7.740382 0.028244 7500.0 308
taipale_tf_pairs__TEAD4_CEBPD_NTTRCGYAANNNNNNNRGWATGY_CAP_repr Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tead4, Cebpd 7.629948 0.027890 32982.0 874
taipale_tf_pairs__TEAD4_CEBPD_NTTRCGYAANNNNNNRGWATGY_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tead4, Cebpd 7.377786 0.027081 45260.0 1110
taipale_tf_pairs__ETV5_CEBPD_NSCGGANNTTRCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpd, Etv5 7.323667 0.026908 55501.0 1091
taipale_tf_pairs__FLI1_CEBPD_RNCGGANNTTGCGCAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Fli1, Cebpd 7.000281 0.025871 36615.0 805
dbtfbs__HLF_HepG2_ENCSR528PSI_merged_N1 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf 6.981791 0.025811 55483.0 1449
metacluster_156.3 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Hlf, Dbp, Tef, Nfil3 Tef, Gm4125, Hlf, Nfil3, Dbp 6.623988 0.024664 55311.0 1326
taipale_tf_pairs__TEAD4_CEBPB_NTTRCGYAANNNNNNGGAATGY_CAP_repr Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tead4, Cebpb 6.117250 0.023039 13175.0 395
taipale_tf_pairs__ERF_CEBPD_RSMGGAANTTGCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Erf, Cebpd 6.019584 0.022726 25715.0 612
transfac_pro__M00621 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpd NaN 6.005248 0.022680 55285.0 1061
cisbp__M01819 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Nfil3 NaN 5.540409 0.021189 55322.0 941
metacluster_46.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Ddit3, Cebpa Ddit3 5.461645 0.020936 55427.0 1010
taipale_tf_pairs__ETV2_CEBPD_RSCGGANNTTGCGYAAN_CAP_repr Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpd, Etv2 5.365398 0.020628 55474.0 951
transfac_pro__M04829 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Stat3 5.216519 0.020150 49441.0 747
tfdimers__MD00123 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN E2f1, Sox17 5.136474 0.019894 55251.0 814
cisbp__M00808 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Mypop NaN 4.597916 0.018166 23477.0 367
metacluster_46.5 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf, Tef 4.246750 0.017040 55392.0 1381
transfac_pro__M01872 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Dbp NaN 3.938958 0.016053 53619.0 813
transfac_pro__M05469 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Sall1 3.874759 0.015847 54755.0 619
swissregulon__hs__GMEB2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Gmeb2 3.757689 0.015472 30443.0 404
hocomoco__GMEB2_HUMAN.H11MO.0.D Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Gmeb2 3.088250 0.013325 18673.0 293
metacluster_39.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Hnf4a Hnf4a, Zfy2, Zfy1, Nr2f6, Mixl1, Hnf4g, Rxrg, Zfp644 3.080836 0.013301 52527.0 692
taipale_tf_pairs__ETV2_TEF_RSCGGAWNTTRCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Etv2, Tef 3.076843 0.013289 24472.0 338
taipale_tf_pairs__ELK1_TEF_NSCGGAWNTTACGTAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Elk1, Tef 3.043136 0.013181 37781.0 578

This table can also be easily exported to a html file:

[8]:

out_file = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/cisTarget/Cebpa_motif_enricment.html'
cistarget_dict['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'].motif_enrichment.to_html(open(out_file, 'w'), escape=False, col_space=80)

You can also access the regions enriched for each motif. You will find to entries in motif_hits (similarly for cistromes); in ‘Region_set’ you will find the coordinates as in the input regions, in ‘Database’ you will find the coordinates as in the database:

[9]:

cistarget_dict['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'].motif_hits['Region_set']['metacluster_46.4'][0:10]

[9]:

['chr7:88310722-88311223',
 'chr4:132078352-132078853',
 'chr7:16525901-16526402',
 'chr6:99266056-99266557',
 'chr1:20820207-20820708',
 'chr15:58214791-58215292',
 'chr7:99181713-99182214',
 'chr7:46719487-46719988',
 'chr13:49681875-49682376',
 'chr5:150599840-150600341']

To access cistromes (only available if motifs have been annotated):

[10]:

cistarget_dict['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'].cistromes['Region_set']['Cebpa_(2809r)'][0:10]

[10]:

['chr7:88310722-88311223',
 'chr4:132078352-132078853',
 'chr7:16525901-16526402',
 'chr6:99266056-99266557',
 'chr1:20820207-20820708',
 'chr15:58214791-58215292',
 'chr7:99181713-99182214',
 'chr7:46719487-46719988',
 'chr13:49681875-49682376',
 'chr5:150599840-150600341']

You can easily export cistromes to a bed file:

[11]:

from pycistarget.utils import *
cebpa_cistrome = cistarget_dict['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'].cistromes['Region_set']['Cebpa_(2809r)']
cebpa_cistrome_pr = pr.PyRanges(region_names_to_coordinates(cebpa_cistrome))
cebpa_cistrome_pr.to_bed(path='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/cisTarget/cebpa_cistrome_example.bed')

2. DEM

A. Creating your DEM databases

To run DEM you will need to provide a CRM scores database (that is, a feather file with a dataframe with motifs as rows, genomic regions as columns and their cis-regulatory module (CRM) score (Frith et al., 2003) as values). We provide those databases for human (hg38, hg19), mouse (mm10, mm9) and fly (dm3, dm6) at https://resources.aertslab.org/cistarget/.

In addition, if you want to use other regions or genomes to build your databases, we provide a step-by-step tutorial and scripts at https://github.com/aertslab/create_cisTarget_databases. The steps are the same as for creating a cisTarget database, without running the last step for ranking the regions. Below you can find the basic steps to do so:

[12]:

%%bash
#### Variables
genome_fasta = 'PATH_TO_GENOME_FASTA'
region_bed = 'PATH_TO_BED_FILE_WITH_GENOMIC_REGIONS_FOR_DATABASE'
region_fasta = 'PATH_TO_FASTA_FILE_WITH_GENOMIC_REGIONS_FOR_DATABASE'
database_suffix = 'SUFFIX_FOR_DATABASE_FILE'
path_to_motif_collection = 'PATH_TO_MOTIF_COLLECTION_IN_CLUSTER_BUSTER_FORMAT'
motif_list = 'PATH_TO_FILE_WITH_MOTIFS_TO_SCORE'
n_cpu = 'NUMBER_OF_CORES'
#### Get fasta sequences
module load BEDTools # In our system, load BEDTools
bedtools getfasta -fi ${genome_fasta} -bed ${region_bed} > ${region_fasta}
#### Activate environment
my_conda_initialize # In our system, initialize conda
conda activate /staging/leuven/stg_00002/lcb/ghuls/software/miniconda3/envs/create_cistarget_databases
#### Set ${create_cistarget_databases_dir} to https://github.com/aertslab/create_cisTarget_databases
create_cistarget_databases_dir='/staging/leuven/stg_00002/lcb/ghuls/software/create_cisTarget_databases'
#### Score the motifs
${create_cistarget_databases_dir}/create_cistarget_motif_databases.py \
-f ${region_fasta} \
-M ${path_to_motif_collection} \
-m ${motif_list} \
-o ${database_suffix} \
-t ${n_cpu} \
-l \
-s 555
done

B. Running DEM

For running DEM there are some relevant parameters:

  • dem_db: Path to the DEM database to use, or a preloaded DEMDatabase object (using the same region sets to be analyzed)

  • region_sets: The input sets of regions

  • specie: Specie to which region coordinates and database belong to. To annotate motifs to TFs using cisTarget annotations, possible values are ‘mus_musculus’, ‘homo_sapiens’ or ‘drosophila_melanogaster’. If any other value, motifs will not be annotated to a TF unless providing a customized annotation.

  • contrasts: Type of contrast to perform. If ‘Other’, background regions will be taken from other region sets; if ‘Shuffle’ the background will consist of the scores on shuffled input sequences. You can also provide a list specifying the specific contrasts to make. We will show some examples of these modalities below. When using ‘Shuffle’, the cluster-buster path, the genome fasta and the path to the folder with the motifs to score (cluster-buster format) has to be provided.

  • fraction_overlap: Minimum overlap fraction (in any direction) to map input regions to regions in the database. Default: 0.4.

  • max_bg_regions: Maximum number of background regions to use. Default: None (all regions).

  • adjpval_thr: Maximum adjusted p-value to select motifs. Default: 0.05

  • log2fc_thr: Minimum LogFC between the regions set and te background to consider the motif as differentially enriched. Default: 1.

  • mean_fg_thr: Minimum mean CRM value in the foreground (region set) to consider the motif differentially enriched. Default: 0

  • motif_hit_thr: Minimum CRM value to consider a region a motif hit. If None (default), an optimal threshold will be calculated per motif by comparing foreground and background.

  • annotation_version : Motif collection version. Here we use the clustered v10 database (‘v10nr_clust’).

  • path_to_motif_annotations : File with motif annotations. These files are available at https://resources.aertslab.org/cistarget/motif2tf .

  • motif_annotation: Annotation to use to form the cistromes. Here we will only use the direct and orthology annotation as example. Default: [‘Direct_annot’, ‘Motif_similarity_annot’, ‘Orthology_annot’, ‘Motif_similarity_and_Orthology_annot’]

  • n_cpu: Number of cpus to use during calculations.

[6]:

# Load DEM functions
from pycistarget.motif_enrichment_dem import *

[7]:

DEM_dict = DEM(dem_db = '/staging/leuven/stg_00002/icistarget-data/make_rankings/v10_clust/CTX_mm10/CTX_mm10_SCREEN3_no_bg_with_mask/CTX_mm10_SCREEN3_no_bg_with_mask.regions_vs_motifs.scores.v2.feather',
    region_sets = region_sets,
    specie = 'mus_musculus',
    contrasts = 'Other',
    name = 'DEM',
    fraction_overlap = 0.4,
    max_bg_regions = 500,
    adjpval_thr = 0.05,
    log2fc_thr = 1,
    mean_fg_thr = 0,
    motif_hit_thr = None,
    cluster_buster_path = None,
    path_to_genome_fasta = None,
    path_to_motifs = None,
    annotation_version = 'v10nr_clust',
    path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/cluster_motif_collection_V10_no_desso_no_factorbook/snapshots/motifs-v10-nr.mgi-m0.00001-o0.0.tbl',
    motif_annotation = ['Direct_annot', 'Orthology_annot'],
    n_cpu = 4,
    tmp_dir = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget/tmp',
    _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

2022-08-04 09:15:22,876 DEM          INFO     Reading DEM database
2022-08-04 09:17:26,334 DEM          INFO     Creating contrast groups
(DEM_internal_ray pid=1603) 2022-08-04 09:17:33,557 DEM          INFO     Computing DEM for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=1605) 2022-08-04 09:17:33,648 DEM          INFO     Computing DEM for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=1606) 2022-08-04 09:17:33,672 DEM          INFO     Computing DEM for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=1604) 2022-08-04 09:17:33,791 DEM          INFO     Computing DEM for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
2022-08-04 09:17:46,089 DEM          INFO     Forming cistromes
2022-08-04 09:17:46,411 DEM          INFO     Done!

[14]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/DEM_dict_B.pkl', 'wb') as f:
  pickle.dump(DEM_dict, f)

C. Exploring DEM results

We can load the results for exploration.

[15]:

# Load
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/DEM_dict_B.pkl', 'rb')
DEM_dict = pickle.load(infile)
infile.close()

To visualize motif enrichment results, we can use the DEM_results() function:

[16]:

DEM_dict.DEM_results('Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K')

[16]:
Logo Contrast Direct_annot Orthology_annot Log2FC Adjusted_pval Mean_fg Mean_bg Motif_hit_thr Motif_hits
taipale_tf_pairs__ATF4_TEF_RNMTGATGCAATN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tef, Atf4 3.57887 0.000017 0.480499 0.040211 1.150 496.0
taipale_tf_pairs__CEBPG_ATF4_NNATGAYGCAAT_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpg, Atf4 3.574535 0.000001 0.522313 0.043842 1.500 521.0
taipale_tf_pairs__GCM1_CEBPB_MTRSGGGNNNNNTTRCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Gcm1, Cebpb 3.409684 0.033062 0.172734 0.016254 0.487 343.0
taipale_tf_pairs__TEAD4_CEBPD_NTTRCGYAANNNNNNRGWATGY_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tead4, Cebpd 3.361205 0.0 0.420022 0.040874 1.550 493.0
tfdimers__MD00123 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN E2f1, Sox17 2.677306 0.000008 0.536723 0.083908 2.150 476.0
taipale_tf_pairs__TEAD4_CEBPD_NTTRCGYAANNNNNNNRGWATGY_CAP_repr Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tead4, Cebpd 2.605569 0.000032 0.32604 0.053569 2.450 239.0
taipale_tf_pairs__TEAD4_CEBPD_RGWATGYNNTTRCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tead4, Cebpd 2.596006 0.0 0.64943 0.107413 0.395 1433.0
taipale_tf_pairs__ERF_CEBPD_RSMGGAANTTGCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpd, Erf 2.277135 0.034172 0.243821 0.050302 1.040 310.0
tfdimers__MD00288 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hmga1b, Sry, Hmga2 2.157576 0.009955 0.300005 0.067241 2.420 235.0
taipale_tf_pairs__FLI1_CEBPD_RNCGGANNTTGCGCAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Fli1, Cebpd 2.149682 0.000405 0.325289 0.073308 1.290 386.0
homer__ATTGCGCAAC_CEBP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpb NaN 2.124851 0.0 2.547318 0.584035 2.380 2299.0
metacluster_46.4 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Hlf, Cebpd, Cebpe, Cebpg, Cebpb, Cebpa Hes2, Cebpe, Cebpd, Ep300, Cebpg, Gatad2a, Dbp, Cebpb, Cebpa 2.058157 0.0 2.910528 0.698883 1.980 3165.0
taipale_tf_pairs__ETV5_CEBPD_NSCGGANNTTRCGYAAN_CAP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Etv5, Cebpd 1.986943 0.0 0.600852 0.151579 0.903 949.0
dbtfbs__HLF_HepG2_ENCSR528PSI_merged_N1 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf 1.885549 0.0 1.106676 0.299512 1.800 1293.0
swissregulon__hs__CEBPB Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpb 1.869025 0.0 1.894034 0.518508 1.170 2173.0
taipale_tf_pairs__ETV2_CEBPD_RSCGGANNTTGCGYAAN_CAP_repr Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Etv2, Cebpd 1.676178 0.0 0.64632 0.20224 1.330 817.0
transfac_pro__M04761 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hsf1 1.644374 0.0 1.911942 0.611602 0.887 2782.0
metacluster_156.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Ddit3, Cebpg, Atf4 Ddit3, Atf3, Cebpg, Atf4, Myc 1.639006 0.0 1.453472 0.466677 1.900 1389.0
metacluster_46.5 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf, Tef 1.586788 0.0 1.297947 0.432102 1.550 1661.0
cisbp__M01815 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.483739 0.0 2.501856 0.894566 2.160 2614.0
metacluster_156.3 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Dbp, Hlf, Tef, Nfil3 Gm4125, Hlf, Tef, Dbp, Nfil3 1.436273 0.0 1.31915 0.487453 1.330 1852.0
swissregulon__mm__Cebpe Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.434853 0.0 2.007923 0.742699 1.810 2326.0
metacluster_46.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Ddit3, Cebpa Ddit3 1.352718 0.0 1.434168 0.561554 1.480 1778.0
transfac_pro__M04829 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Stat3 1.293588 0.0 1.138097 0.46427 1.040 1102.0
transfac_pro__M01869 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpg NaN 1.19817 0.0 2.465139 1.074376 1.570 3141.0
tfdimers__MD00232 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Taf6, Cebpb, Tbp 1.131818 0.007504 0.442282 0.201831 0.765 791.0
transfac_pro__M00621 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpd NaN 1.083923 0.0 1.799703 0.848999 1.010 2847.0
transfac_pro__M05469 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Sall1 1.026501 0.000002 0.759713 0.372943 2.070 707.0

This table can also be easily exported to a html file:

[17]:

out_file = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/Cebpa_motif_enricment.html'
DEM_dict.motif_enrichment['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'].to_html(open(out_file, 'w'), escape=False, col_space=80)

You can also access the regions enriched for each motif. You will find to entries in motif_hits (similarly for cistromes); in ‘Region_set’ you will find the coordinates as in the input regions, in ‘Database’ you will find the coordinates as in the database:

[18]:

DEM_dict.motif_hits['Region_set']['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K']['homer__ATTGCGCAAC_CEBP'][0:10]

[18]:

['chr4:53196410-53196911',
 'chr9:95477249-95477750',
 'chr17:53580191-53580692',
 'chr1:106267982-106268483',
 'chr5:99283569-99284070',
 'chr8:22054603-22055104',
 'chr5:102537694-102538195',
 'chr4:48132714-48133215',
 'chr4:156124035-156124536',
 'chr15:59643719-59644220']

To access cistromes (only available if motifs have been annotated):

[19]:

DEM_dict.cistromes['Region_set']['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K']['Cebpa_(3360r)'][0:10]

[19]:

['chr4:53196410-53196911',
 'chr9:25570286-25570787',
 'chr9:95477249-95477750',
 'chr1:106267982-106268483',
 'chr5:99283569-99284070',
 'chr4:76344051-76344552',
 'chr8:22054603-22055104',
 'chr17:53580191-53580692',
 'chr5:102537694-102538195',
 'chr12:7978369-7978870']

What is the length of this cistrome? We will compare how this changes with different settings below:

[20]:

len(DEM_dict.cistromes['Region_set']['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K']['Cebpa_(3360r)'])

[20]:

3360

You can easily export cistromes to a bed file:

[21]:

from pycistarget.utils import *
cebpa_cistrome = DEM_dict.cistromes['Region_set']['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K']['Cebpa_(3360r)']
cebpa_cistrome_pr = pr.PyRanges(region_names_to_coordinates(cebpa_cistrome))
cebpa_cistrome_pr.to_bed(path='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/cebpa_cistrome_example.bed')

D. Advanced usage

1. Thresholding on the mean foreground signal

Above you may have noticed some motifs with high LogFC values, but low signal in both foreground and background. To avoid them, you can set a threshold on the mean CRM value in the foreground with mean_fg_thr. Here we will set it to 1:

[8]:

DEM_dict = DEM(dem_db = '/staging/leuven/stg_00002/icistarget-data/make_rankings/v10_clust/CTX_mm10/CTX_mm10_SCREEN3_no_bg_with_mask/CTX_mm10_SCREEN3_no_bg_with_mask.regions_vs_motifs.scores.v2.feather',
    region_sets = region_sets,
    specie = 'mus_musculus',
    contrasts = 'Other',
    name = 'DEM',
    fraction_overlap = 0.4,
    max_bg_regions = 500,
    adjpval_thr = 0.05,
    log2fc_thr = 1,
    mean_fg_thr = 1,
    motif_hit_thr = None,
    n_cpu = 4,
    cluster_buster_path = None,
    path_to_genome_fasta = None,
    path_to_motifs = None,
    annotation_version = 'v10nr_clust',
    path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/cluster_motif_collection_V10_no_desso_no_factorbook/snapshots/motifs-v10-nr.mgi-m0.00001-o0.0.tbl',
    motif_annotation = ['Direct_annot', 'Orthology_annot'],
    tmp_dir = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget/tmp',
    _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

2022-08-04 09:18:24,202 DEM          INFO     Reading DEM database
2022-08-04 09:18:47,321 DEM          INFO     Creating contrast groups
(DEM_internal_ray pid=3083) 2022-08-04 09:18:54,925 DEM          INFO     Computing DEM for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=3084) 2022-08-04 09:18:54,911 DEM          INFO     Computing DEM for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=3085) 2022-08-04 09:18:54,941 DEM          INFO     Computing DEM for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=3086) 2022-08-04 09:18:55,092 DEM          INFO     Computing DEM for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
2022-08-04 09:19:07,185 DEM          INFO     Forming cistromes
2022-08-04 09:19:07,446 DEM          INFO     Done!

You will observe now that these motifs are gone:

[23]:

DEM_dict.DEM_results('Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K')

[23]:
Logo Contrast Direct_annot Orthology_annot Log2FC Adjusted_pval Mean_fg Mean_bg Motif_hit_thr Motif_hits
homer__ATTGCGCAAC_CEBP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpb NaN 2.124851 0.0 2.547318 0.584035 2.380 2299.0
metacluster_46.4 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Hlf, Cebpd, Cebpe, Cebpg, Cebpb, Cebpa Hes2, Cebpe, Cebpd, Ep300, Cebpg, Gatad2a, Dbp, Cebpb, Cebpa 2.058157 0.0 2.910528 0.698883 1.980 3165.0
dbtfbs__HLF_HepG2_ENCSR528PSI_merged_N1 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf 1.885549 0.0 1.106676 0.299512 1.800 1293.0
swissregulon__hs__CEBPB Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpb 1.869025 0.0 1.894034 0.518508 1.170 2173.0
transfac_pro__M04761 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hsf1 1.644374 0.0 1.911942 0.611602 0.887 2782.0
metacluster_156.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Ddit3, Cebpg, Atf4 Ddit3, Atf3, Cebpg, Atf4, Myc 1.639006 0.0 1.453472 0.466677 1.900 1389.0
metacluster_46.5 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf, Tef 1.586788 0.0 1.297947 0.432102 1.550 1661.0
cisbp__M01815 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.483739 0.0 2.501856 0.894566 2.160 2614.0
metacluster_156.3 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Dbp, Hlf, Tef, Nfil3 Gm4125, Hlf, Tef, Dbp, Nfil3 1.436273 0.0 1.31915 0.487453 1.330 1852.0
swissregulon__mm__Cebpe Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.434853 0.0 2.007923 0.742699 1.810 2326.0
metacluster_46.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Ddit3, Cebpa Ddit3 1.352718 0.0 1.434168 0.561554 1.480 1778.0
transfac_pro__M04829 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Stat3 1.293588 0.0 1.138097 0.46427 1.040 1102.0
transfac_pro__M01869 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpg NaN 1.19817 0.0 2.465139 1.074376 1.570 3141.0
transfac_pro__M00621 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpd NaN 1.083923 0.0 1.799703 0.848999 1.010 2847.0

The Cebpa cistrome has the same length:

[24]:

len(DEM_dict.cistromes['Region_set']['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K']['Cebpa_(3360r)'])

[24]:

3360

And save this object:

[25]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/DEM_dict_D1.pkl', 'wb') as f:
  pickle.dump(DEM_dict, f)

2. Using a fixed threshold for the motif hits

You may have also noticed that cistromes are larger compared to Homer or cisTarget, and this will largely depend on your background (cistromes will be formed by those regions that are more enriched for that motif compared to that background). You can also set a fixed threshold to consider a motif a hit with motif_hit_thr. Here we will set it to 3.

[9]:

DEM_dict = DEM(dem_db = '/staging/leuven/stg_00002/icistarget-data/make_rankings/v10_clust/CTX_mm10/CTX_mm10_SCREEN3_no_bg_with_mask/CTX_mm10_SCREEN3_no_bg_with_mask.regions_vs_motifs.scores.v2.feather',
    region_sets = region_sets,
    specie = 'mus_musculus',
    contrasts = 'Other',
    name = 'DEM',
    fraction_overlap = 0.4,
    max_bg_regions = 500,
    adjpval_thr = 0.05,
    log2fc_thr = 1,
    mean_fg_thr = 1,
    motif_hit_thr = 3,
    n_cpu = 4,
    cluster_buster_path = None,
    path_to_genome_fasta = None,
    path_to_motifs = None,
    annotation_version = 'v10nr_clust',
    path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/cluster_motif_collection_V10_no_desso_no_factorbook/snapshots/motifs-v10-nr.mgi-m0.00001-o0.0.tbl',
    motif_annotation = ['Direct_annot', 'Orthology_annot'],
    tmp_dir = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget/tmp',
    _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

2022-08-04 09:19:30,235 DEM          INFO     Reading DEM database
2022-08-04 09:19:53,620 DEM          INFO     Creating contrast groups
(DEM_internal_ray pid=19721) 2022-08-04 09:20:01,295 DEM          INFO     Computing DEM for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=19720) 2022-08-04 09:20:01,401 DEM          INFO     Computing DEM for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=19722) 2022-08-04 09:20:01,378 DEM          INFO     Computing DEM for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=19723) 2022-08-04 09:20:01,544 DEM          INFO     Computing DEM for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
2022-08-04 09:20:13,338 DEM          INFO     Forming cistromes
2022-08-04 09:20:13,567 DEM          INFO     Done!

You will notice now that the number of motif hits per motif is generally lower.

[27]:

DEM_dict.DEM_results('Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K')

[27]:
Logo Contrast Direct_annot Orthology_annot Log2FC Adjusted_pval Mean_fg Mean_bg Motif_hit_thr Motif_hits
homer__ATTGCGCAAC_CEBP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpb NaN 2.124851 0.0 2.547318 0.584035 3.0 1940.0
metacluster_46.4 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Hlf, Cebpd, Cebpe, Cebpg, Cebpb, Cebpa Hes2, Cebpe, Cebpd, Ep300, Cebpg, Gatad2a, Dbp, Cebpb, Cebpa 2.058157 0.0 2.910528 0.698883 3.0 2340.0
dbtfbs__HLF_HepG2_ENCSR528PSI_merged_N1 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf 1.885549 0.0 1.106676 0.299512 3.0 780.0
swissregulon__hs__CEBPB Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpb 1.869025 0.0 1.894034 0.518508 3.0 1379.0
transfac_pro__M04761 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hsf1 1.644374 0.0 1.911942 0.611602 3.0 1377.0
metacluster_156.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Ddit3, Cebpg, Atf4 Ddit3, Atf3, Cebpg, Atf4, Myc 1.639006 0.0 1.453472 0.466677 3.0 846.0
metacluster_46.5 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf, Tef 1.586788 0.0 1.297947 0.432102 3.0 775.0
cisbp__M01815 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.483739 0.0 2.501856 0.894566 3.0 1911.0
metacluster_156.3 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Dbp, Hlf, Tef, Nfil3 Gm4125, Hlf, Tef, Dbp, Nfil3 1.436273 0.0 1.31915 0.487453 3.0 689.0
swissregulon__mm__Cebpe Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.434853 0.0 2.007923 0.742699 3.0 1315.0
metacluster_46.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Ddit3, Cebpa Ddit3 1.352718 0.0 1.434168 0.561554 3.0 851.0
transfac_pro__M04829 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Stat3 1.293588 0.0 1.138097 0.46427 3.0 778.0
transfac_pro__M01869 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpg NaN 1.19817 0.0 2.465139 1.074376 3.0 1736.0
transfac_pro__M00621 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpd NaN 1.083923 0.0 1.799703 0.848999 3.0 1110.0

The length of the cistromes is lower too:

[28]:

len(DEM_dict.cistromes['Region_set']['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K']['Cebpa_(2488r)'])

[28]:

2488

Let’s save this object:

[29]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/DEM_dict_D2.pkl', 'wb') as f:
  pickle.dump(DEM_dict, f)

3. Using a shuffled background

It is possible that you don’t have a background (for example, if you only have a ChIP-seq experiment). You can also use shuffled regions (from your input) as background by setting contrasts to ‘Shuffle’. You will need to have Cluster-Buster installed to use this option.

[10]:

os.putenv('CBUST_HOME','/data/leuven/software/biomed/skylake_centos7/2018a/software/Cluster-Buster/20220421-GCCcore-6.4.0')
os.environ["PATH"] += os.pathsep + '/data/leuven/software/biomed/skylake_centos7/2018a/software/Cluster-Buster/20220421-GCCcore-6.4.0/bin:'
DEM_dict = DEM(dem_db = '/staging/leuven/stg_00002/icistarget-data/make_rankings/v10_clust/CTX_mm10/CTX_mm10_SCREEN3_no_bg_with_mask/CTX_mm10_SCREEN3_no_bg_with_mask.regions_vs_motifs.scores.v2.feather',
    region_sets = region_sets,
    specie = 'mus_musculus',
    contrasts = 'Shuffle',
    name = 'DEM',
    max_bg_regions = 100,
    adjpval_thr = 0.05,
    log2fc_thr = 1,
    mean_fg_thr = 2.5, #You may need to increase the detection threshold here, otherwise you may see a lot of G repeats
    n_cpu = 4,
    fraction_overlap = 0.4,
    cluster_buster_path = '/data/leuven/software/biomed/skylake_centos7/2018a/software/Cluster-Buster/20220421-GCCcore-6.4.0/bin/cbust',
    path_to_genome_fasta = '/staging/leuven/res_00001/genomes/mus_musculus/mm10_ucsc/fasta/mm10.fa',
    path_to_motifs = '/staging/leuven/stg_00002/lcb/cbravo/cluster_motif_collection_V10_no_desso_no_factorbook/cluster_buster/',
    annotation_version = 'v10nr_clust',
    path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/cluster_motif_collection_V10_no_desso_no_factorbook/snapshots/motifs-v10-nr.mgi-m0.00001-o0.0.tbl',
    motif_annotation = ['Direct_annot', 'Orthology_annot'],
    tmp_dir = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/tmp',
    _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

2022-08-04 09:20:27,317 DEM          INFO     Reading DEM database
2022-08-04 09:20:52,439 DEM          INFO     Creating contrast groups
2022-08-04 09:20:52,443 DEM          INFO     Generating and scoring shuffled background
2022-08-04 09:20:58,295 Cluster-Buster INFO     Scoring sequences
2022-08-04 09:22:07,487 Cluster-Buster INFO     Done!
2022-08-04 09:22:07,543 DEM          INFO     Generating and scoring shuffled background
2022-08-04 09:22:12,910 Cluster-Buster INFO     Scoring sequences
2022-08-04 09:22:41,457 Cluster-Buster INFO     Done!
2022-08-04 09:22:41,512 DEM          INFO     Generating and scoring shuffled background
2022-08-04 09:22:46,153 Cluster-Buster INFO     Scoring sequences
2022-08-04 09:23:12,511 Cluster-Buster INFO     Done!
2022-08-04 09:23:12,567 DEM          INFO     Generating and scoring shuffled background
2022-08-04 09:23:15,634 Cluster-Buster INFO     Scoring sequences
2022-08-04 09:23:43,120 Cluster-Buster INFO     Done!
(DEM_internal_ray pid=23238) 2022-08-04 09:23:50,847 DEM          INFO     Computing DEM for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=23242) 2022-08-04 09:23:50,912 DEM          INFO     Computing DEM for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=23240) 2022-08-04 09:23:50,956 DEM          INFO     Computing DEM for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=23239) 2022-08-04 09:23:51,128 DEM          INFO     Computing DEM for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
2022-08-04 09:24:04,530 DEM          INFO     Forming cistromes
2022-08-04 09:24:04,839 DEM          INFO     Done!

Let’s see the results now:

[11]:

DEM_dict.DEM_results('Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K')

[11]:
Logo Contrast Direct_annot Orthology_annot Log2FC Adjusted_pval Mean_fg Mean_bg Motif_hit_thr Motif_hits
homer__ATTGCGCAAC_CEBP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpb NaN 2.532608 0.0 2.547322 0.440243 1.56 2771.0
metacluster_46.4 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpg, Cebpa, Cebpb, Cebpd, Cebpe, Hlf Ep300, Cebpg, Cebpa, Cebpb, Hes2, Gatad2a, Cebpd, Dbp, Cebpe 2.422879 0.0 2.910528 0.542766 1.60 3416.0
transfac_pro__M12588 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Ddit3 1.860392 0.0 2.645379 0.728541 1.43 3269.0
transfac_pro__M09737 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Zfp644 1.824506 0.0 2.768588 0.781677 1.88 2792.0
swissregulon__hs__EZH2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Ezh2 1.775441 0.0 2.676924 0.781943 1.58 3074.0
hocomoco__SMAD3_HUMAN.H11MO.0.B Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Smad3 1.712154 0.0 2.745296 0.837876 2.06 2667.0
transfac_pro__M12659 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Rora 1.707419 0.0 2.525895 0.773448 1.31 3244.0
cisbp__M01815 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.639925 0.0 2.501861 0.80278 1.41 3217.0
transfac_pro__M01721 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Pura NaN 1.609624 0.0 2.591059 0.849048 1.55 3244.0
swissregulon__hs__CUX1 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cux1 1.588562 0.0 2.901514 0.964761 1.74 3205.0
transfac_pro__M09729 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Creb3 1.524944 0.0 2.594553 0.901589 2.39 2112.0
transfac_pro__M12689 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Zbtb39 1.392545 0.0 3.151474 1.200372 2.25 2946.0
transfac_pro__M09763 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Nfic 1.387777 0.0 2.675388 1.022408 1.75 2961.0
transfac_pro__M12352 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Zbtb44 1.345064 0.0 2.532602 0.996925 1.34 3376.0
hocomoco__HAND1_HUMAN.H11MO.1.D Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hand1 1.243063 0.0 2.755776 1.164246 2.42 2452.0
transfac_pro__M09746 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hnrnpul1 1.166264 0.0 2.930853 1.305911 2.32 2685.0
transfac_pro__M09726 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Gatad2a 1.07463 0.0 2.907862 1.380632 2.13 2904.0

The length of the cistromes is lower too:

[13]:

len(DEM_dict.cistromes['Region_set']['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K']['Cebpa_(3378r)'])

[13]:

3378

Let’s save this object:

[15]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/DEM_dict_D3.pkl', 'wb') as f:
  pickle.dump(DEM_dict, f)

4. Specifying contrasts

It is possible that you want to make specific contrast between region sets. You can do this by passing a list to contrast (each slot will be a contrast, first slot with it will be the foreground and second the background). For example, here we will perform two contrasts: 1) Cebpa versus Onecut and 2) Cebpa versus Onecut and Hnf4a.

[16]:

DEM_dict = DEM(dem_db = '/staging/leuven/stg_00002/icistarget-data/make_rankings/v10_clust/CTX_mm10/CTX_mm10_SCREEN3_no_bg_with_mask/CTX_mm10_SCREEN3_no_bg_with_mask.regions_vs_motifs.scores.v2.feather',
    region_sets = region_sets,
    specie = 'mus_musculus',
    contrasts = [[['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'], ['Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K']], [['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'], ['Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K', 'Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K']]],
    name = 'DEM',
    fraction_overlap = 0.4,
    max_bg_regions = 500,
    adjpval_thr = 0.05,
    log2fc_thr = 1,
    mean_fg_thr = 1,
    motif_hit_thr = 3,
    n_cpu = 4,
    cluster_buster_path = None,
    path_to_genome_fasta = None,
    path_to_motifs = None,
    annotation_version = 'v10nr_clust',
    path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/cluster_motif_collection_V10_no_desso_no_factorbook/snapshots/motifs-v10-nr.mgi-m0.00001-o0.0.tbl',
    motif_annotation = ['Direct_annot', 'Orthology_annot'],
    tmp_dir = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget/tmp',
    _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

2022-08-04 09:24:59,453 DEM          INFO     Reading DEM database
2022-08-04 09:25:21,915 DEM          INFO     Creating contrast groups
(DEM_internal_ray pid=24473) 2022-08-04 09:25:29,749 DEM          INFO     Computing DEM for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=24472) 2022-08-04 09:25:29,796 DEM          INFO     Computing DEM for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K_Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
2022-08-04 09:25:40,199 DEM          INFO     Forming cistromes
2022-08-04 09:25:40,293 DEM          INFO     Done!

Let’s see the results now comparing with Onecut:

[17]:

DEM_dict.DEM_results('Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K')

[17]:
Logo Contrast Direct_annot Orthology_annot Log2FC Adjusted_pval Mean_fg Mean_bg Motif_hit_thr Motif_hits
dbtfbs__HLF_HepG2_ENCSR528PSI_merged_N1 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K NaN Hlf 2.210142 0.0 1.106676 0.239167 3.0 780.0
metacluster_46.4 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Cebpg, Cebpa, Cebpb, Cebpd, Cebpe, Hlf Ep300, Cebpg, Cebpa, Cebpb, Hes2, Gatad2a, Cebpd, Dbp, Cebpe 2.018376 0.0 2.910528 0.718423 3.0 2340.0
homer__ATTGCGCAAC_CEBP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Cebpb NaN 1.823287 0.0 2.547322 0.719813 3.0 1940.0
metacluster_46.5 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K NaN Tef, Hlf 1.760758 0.0 1.297947 0.383015 3.0 775.0
swissregulon__hs__CEBPB Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K NaN Cebpb 1.74152 0.0 1.894033 0.566419 3.0 1379.0
transfac_pro__M04761 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K NaN Hsf1 1.720374 0.0 1.911941 0.580217 3.0 1377.0
metacluster_156.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Atf4, Ddit3, Cebpg Cebpg, Myc, Atf4, Ddit3, Atf3 1.665894 0.0 1.45347 0.45806 3.0 846.0
metacluster_156.3 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Dbp, Tef, Nfil3, Hlf Gm4125, Nfil3, Dbp, Tef, Hlf 1.62854 0.0 1.319148 0.426633 3.0 689.0
cisbp__M01815 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.514219 0.0 2.501861 0.875866 3.0 1911.0
swissregulon__mm__Cebpe Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.40163 0.0 2.007918 0.76 3.0 1315.0
metacluster_46.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Cebpa, Ddit3 Ddit3 1.395899 0.0 1.434166 0.544995 3.0 851.0
transfac_pro__M04829 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K NaN Stat3 1.206516 0.0 1.138095 0.493152 3.0 778.0
transfac_pro__M01869 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Cebpg NaN 1.061128 0.0 2.465138 1.181435 3.0 1736.0
transfac_pro__M12588 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K NaN Ddit3 1.050354 0.0 2.645379 1.27732 3.0 1772.0
tfdimers__MD00518 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K NaN Pou5f1, Myb 1.030543 0.0 1.110707 0.54372 3.0 642.0
transfac_pro__M00621 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K_VS_Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K Cebpd NaN 1.0292 0.0 1.799702 0.881821 3.0 1110.0

Let’s save this object:

[18]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/DEM_dict_D4.pkl', 'wb') as f:
  pickle.dump(DEM_dict, f)

5. Balancing promoter content

Finally it is possible to balance the proportion of promoters between foreground and background to avoid overrepresentation of the promoter sequences signal. You only need to provide the promoter annotation.

[34]:

# Retrive promoter annotation from biomart
import pybiomart as pbm
promoter_space = 500
dataset = pbm.Dataset(name='mmusculus_gene_ensembl',  host='http://nov2020.archive.ensembl.org/')
annot = dataset.query(attributes=['chromosome_name', 'transcription_start_site', 'strand', 'external_gene_name', 'transcript_biotype'])
annot.columns = ['Chromosome', 'Start', 'Strand', 'Gene', 'Transcript_type']
annot['Chromosome'] = annot['Chromosome'].astype('str')
filterf = annot['Chromosome'].str.contains('CHR|GL|JH|MT')
annot = annot[~filterf]
annot['Chromosome'] = annot['Chromosome'].str.replace(r'(\b\S)', r'chr\1')
annot = annot[annot.Transcript_type == 'protein_coding']
annot = annot.dropna(subset = ['Chromosome', 'Start'])

[39]:

DEM_dict = DEM(dem_db = '/staging/leuven/stg_00002/icistarget-data/make_rankings/v10_clust/CTX_mm10/CTX_mm10_SCREEN3_no_bg_with_mask/CTX_mm10_SCREEN3_no_bg_with_mask.regions_vs_motifs.scores.v2.feather',
    region_sets = region_sets,
    specie = 'mus_musculus',
    contrasts = 'Other',
    name = 'DEM',
    fraction_overlap = 0.4,
    max_bg_regions = 500,
    adjpval_thr = 0.05,
    log2fc_thr = 1,
    mean_fg_thr = 1,
    motif_hit_thr = None,
    genome_annotation= annot, # Add genome_annotation
    promoter_space = 500,
    cluster_buster_path = None,
    path_to_genome_fasta = None,
    path_to_motifs = None,
    annotation_version = 'v10nr_clust',
    path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/cbravo/cluster_motif_collection_V10_no_desso_no_factorbook/snapshots/motifs-v10-nr.mgi-m0.00001-o0.0.tbl',
    motif_annotation = ['Direct_annot', 'Orthology_annot'],
    n_cpu = 4,
    tmp_dir = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget/tmp',
    _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

2022-08-04 09:39:55,535 DEM          INFO     Reading DEM database
2022-08-04 09:40:17,093 DEM          INFO     Creating contrast groups
(DEM_internal_ray pid=33197) 2022-08-04 09:40:26,597 DEM          INFO     Computing DEM for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=33194) 2022-08-04 09:40:26,722 DEM          INFO     Computing DEM for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=33195) 2022-08-04 09:40:26,721 DEM          INFO     Computing DEM for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K
(DEM_internal_ray pid=33196) 2022-08-04 09:40:26,824 DEM          INFO     Computing DEM for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
2022-08-04 09:40:37,990 DEM          INFO     Forming cistromes
2022-08-04 09:40:38,190 DEM          INFO     Done!

Let’s see the results now comparing with Onecut:

[40]:

DEM_dict.DEM_results('Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K')

[40]:
Logo Contrast Direct_annot Orthology_annot Log2FC Adjusted_pval Mean_fg Mean_bg Motif_hit_thr Motif_hits
dbtfbs__HLF_HepG2_ENCSR528PSI_merged_N1 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hlf 2.195518 0.0 1.106676 0.241604 2.250 1058.0
homer__ATTGCGCAAC_CEBP Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpb NaN 2.173246 0.0 2.547322 0.56477 2.400 2284.0
metacluster_46.4 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpg, Cebpa, Cebpb, Cebpd, Cebpe, Hlf Ep300, Cebpg, Cebpa, Cebpb, Hes2, Gatad2a, Cebpd, Dbp, Cebpe 2.116813 0.0 2.910528 0.671039 1.900 3225.0
transfac_pro__M04761 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Hsf1 1.891702 0.0 1.911941 0.515247 1.060 2598.0
swissregulon__hs__CEBPB Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Cebpb 1.859936 0.0 1.894033 0.521784 2.150 1760.0
metacluster_46.5 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Tef, Hlf 1.847229 0.0 1.297947 0.360733 2.030 1364.0
metacluster_156.3 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Dbp, Tef, Nfil3, Hlf Gm4125, Nfil3, Dbp, Tef, Hlf 1.667336 0.0 1.319148 0.415313 0.984 2184.0
metacluster_156.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Atf4, Ddit3, Cebpg Cebpg, Myc, Atf4, Ddit3, Atf3 1.575318 0.0 1.45347 0.48774 1.680 1523.0
cisbp__M01815 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.504281 0.0 2.501861 0.88192 1.830 2843.0
swissregulon__mm__Cebpe Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpe NaN 1.482787 0.0 2.007918 0.718427 1.650 2475.0
transfac_pro__M04829 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Stat3 1.471578 0.0 1.138095 0.410383 0.715 1557.0
metacluster_46.2 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpa, Ddit3 Ddit3 1.37845 0.0 1.434166 0.551626 0.479 2922.0
transfac_pro__M01869 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpg NaN 1.172004 0.0 2.465138 1.094039 2.310 2403.0
transfac_pro__M12588 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K NaN Ddit3 1.073521 0.0 2.645379 1.256973 2.270 2430.0
transfac_pro__M00621 Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K Cebpd NaN 1.047998 0.0 1.799702 0.870406 1.220 2576.0

Let’s save this object:

[41]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/DEM/DEM_dict_D5.pkl', 'wb') as f:
  pickle.dump(DEM_dict, f)

3. Homer

First we need to load the functions needed for Homer:

[42]:

# Load homer functions
from pycistarget.motif_enrichment_homer import *

A. Running Homer

For running Homer there are some relevant parameters:

  • homer_path: Path to the executable Homer files. Homer has to be also accessible in the python paths too.

  • region_sets: The input sets of regions

  • outdir: Output directory

  • genome: Genome assembly (equivalent to the genome parameter in Homer). Several species and genomes are supported, including human (hg18, hg19, hg38) and mouse (mm8, mm9, mm10), among others. Alternatively, it can be a path to custom genome fasta files.

  • size: Fragment size to use for motif finding (by default, ‘given’, which is the whole region).

  • mask: Whether to mask repeat regions

  • denovo: Whether to perform de novo motif discovery. This will increase the running time considerably. If running de novo motif enrichment, you can use meme with a motif collection of interest to identify potential TFs linked to de novo motifs. If False, Homer will only be run for known motifs.

  • length: Motif length for the de novo motif discovery.

  • n_cpu: Number of cores to use

  • meme_path: Path to the executable MEME files. MEME has to be also accessible in the python paths too.

  • meme_collection_path : Path to the motif collection in meme format. We recommend to use the cisTarget motif collection.

  • annotation_version : Motif collection version. Here we use the unclustered v10 database (‘v10’).

  • path_to_motif_annotations : File with motif annotations. These files are available at https://resources.aertslab.org/cistarget/motif2tf .

  • cistrome_annotation : Annotations to assign motifs to TFs (direct, and/or by motif similarity or orthology)

[43]:

# Set correct path to run HOMER
import os
os.putenv('HOMER_HOME','/data/leuven/software/biomed/haswell_centos7/2018a/software/HOMER/4.10.4-foss-2018a')
os.environ["PATH"] += os.pathsep + '/data/leuven/software/biomed/haswell_centos7/2018a/software/HOMER/4.10.4-foss-2018a/bin:'
homer_path='/data/leuven/software/biomed/haswell_centos7/2018a/software/HOMER/4.10.4-foss-2018a/bin/'
# Choose the output directory for the results
outdir='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/'
# Select your genome
genome='mm10'
# Set correct path to MEME for de novo motif annotation - Only needed if using de novo annotation!
# We have tomtom installed in our image, so we dont need to add additional paths
meme_collection_path = '/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/scenicplus_motif_collection.meme'
meme_path='/opt/meme/bin/'
# Run
homer_dict=run_homer(homer_path,
                     region_sets,
                     outdir,
                     genome,
                     size='given',
                     mask=True,
                     denovo=True,
                     length='8,10,12',
                     n_cpu=4,
                     meme_path = meme_path,
                     meme_collection_path = meme_collection_path,
                     annotation_version = 'v10',
                     path_to_motif_annotations = '/staging/leuven/stg_00002/lcb/icistarget/data/motif2tf_project/motif_to_tf_db_data/snapshots/motifs-v10-nr.mgi-m0.00001-o0.0.tbl',
                     cistrome_annotation = ['Direct_annot', 'Orthology_annot'],
                     _temp_dir='/scratch/leuven/313/vsc31305/ray_spill')

(homer_ray pid=33838) 2022-08-04 09:41:08,646 Homer        INFO     Running Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(homer_ray pid=33838) 2022-08-04 09:41:08,647 Homer        INFO     Running Homer for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K with /data/leuven/software/biomed/haswell_centos7/2018a/software/HOMER/4.10.4-foss-2018a/bin/findMotifsGenome.pl /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/regions_bed/Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K.bed mm10 /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K -preparsedDir /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K -size given -len 8,10,12 -mask -keepFiles
(homer_ray pid=33839) 2022-08-04 09:41:08,790 Homer        INFO     Running Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K
(homer_ray pid=33839) 2022-08-04 09:41:08,791 Homer        INFO     Running Homer for Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K with /data/leuven/software/biomed/haswell_centos7/2018a/software/HOMER/4.10.4-foss-2018a/bin/findMotifsGenome.pl /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/regions_bed/Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K.bed mm10 /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K -preparsedDir /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Hnf4a_ERR235763_summits_order_by_score_extended_250bp_top5K -size given -len 8,10,12 -mask -keepFiles
(homer_ray pid=33840) 2022-08-04 09:41:08,786 Homer        INFO     Running Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K
(homer_ray pid=33840) 2022-08-04 09:41:08,786 Homer        INFO     Running Homer for Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K with /data/leuven/software/biomed/haswell_centos7/2018a/software/HOMER/4.10.4-foss-2018a/bin/findMotifsGenome.pl /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/regions_bed/Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K.bed mm10 /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K -preparsedDir /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Foxa1_ERR235786_summits_order_by_score_extended_250bp_top5K -size given -len 8,10,12 -mask -keepFiles
(homer_ray pid=33841) 2022-08-04 09:41:08,879 Homer        INFO     Running Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K
(homer_ray pid=33841) 2022-08-04 09:41:08,880 Homer        INFO     Running Homer for Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K with /data/leuven/software/biomed/haswell_centos7/2018a/software/HOMER/4.10.4-foss-2018a/bin/findMotifsGenome.pl /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/regions_bed/Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K.bed mm10 /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K -preparsedDir /staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K -size given -len 8,10,12 -mask -keepFiles
(homer_ray pid=33838) 2022-08-04 10:38:13,434 Homer        INFO     Annotating motifs for Onecut1_ERR235752_summits_order_by_score_extended_250bp_top5K
(homer_ray pid=33838) 2022-08-04 10:38:13,434 Homer        INFO     Annotating known motifs

[44]:

# Save
import pickle
with open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Homer_dict.pkl', 'wb') as f:
  pickle.dump(homer_dict, f)

B. Exploring Homer results

We can load the results for exploration.

[4]:

# Load
import pickle
infile = open('/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial_old/pycistarget_tutorial/Homer/Homer_dict.pkl', 'rb')
homer_dict = pickle.load(infile)
infile.close()

To visualize motif enrichment results, we can use the homer_results() function:

[45]:

homer_results(homer_dict, 'Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K', results='known')

[45]:
/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K - Homer Known Motif Enrichment Results

Homer Known Motif Enrichment Results (/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K)

Homer de novo Motif Results
Gene Ontology Enrichment Results
Known Motif Enrichment Results (txt file)
Total Target Sequences = 4595, Total Background Sequences = 44731
RankMotifNameP-valuelog P-pvalueq-value (Benjamini)# Target Sequences with Motif% of Targets Sequences with Motif# Background Sequences with Motif% of Background Sequences with MotifMotif File SVG
1 T G C A A G C T A C G T C T A G G A T C C T A G G A T C G T C A C T G A A G T C CEBP(bZIP)/ThioMac-CEBPb-ChIP-Seq(GSE21512)/Homer1e-1326-3.054e+030.00002719.059.17%5045.711.28%motif file (matrix) svg
2 T C A G G A C T C A G T C T G A A G C T C T A G G A C T T G C A C T G A A G T C HLF(bZIP)/HSC-HLF.Flag-ChIP-Seq(GSE69817)/Homer1e-669-1.541e+030.00002216.048.23%6249.013.97%motif file (matrix) svg
3 T C G A A C G T A C G T C T G A G A T C T C A G G A C T G T C A C G T A A G C T G T C A C T A G A G C T A C G T T C G A NFIL3(bZIP)/HepG2-NFIL3-ChIP-Seq(Encode)/Homer1e-646-1.489e+030.00001926.041.92%4777.910.68%motif file (matrix) svg
4 C T A G T C G A C G A T C T A G G C A T C A G T C T A G G A T C C G T A G T C A CEBP:AP1(bZIP)/ThioMac-CEBPb-ChIP-Seq(GSE21512)/Homer1e-532-1.227e+030.00002013.043.81%6161.913.78%motif file (matrix) svg
5 T G C A A G C T C A T G C G T A A G C T A C T G G A T C G T C A C G T A A G C T Atf4(bZIP)/MEF-Atf4-ChIP-Seq(GSE35681)/Homer1e-335-7.727e+020.0000947.020.61%2008.94.49%motif file (matrix) svg
6 T C G A G C A T A C G T C T A G G T A C T C G A G C A T T G A C T C G A A C G T Chop(bZIP)/MEF-Chop-ChIP-Seq(GSE35681)/Homer1e-217-5.017e+020.0000684.014.89%1569.13.51%motif file (matrix) svg
7 T G A C C G A T C T G A C T A G C T A G A C G T A T G C T G C A T C G A C T G A C T A G C A T G A C G T A G T C C G T A PPARa(NR),DR1/Liver-Ppara-ChIP-Seq(GSE47954)/Homer1e-94-2.176e+020.00001548.033.69%9195.620.56%motif file (matrix) svg
8 G T A C G C T A T C A G C T G A C T A G C A T G A G C T G A T C T G C A T C G A C T G A A C T G C A G T A G T C G A T C G C T A HNF4a(NR),DR1/HepG2-HNF4a-ChIP-Seq(GSE25021)/Homer1e-86-1.996e+020.0000875.019.04%4230.79.46%motif file (matrix) svg
9 A G C T A G C T C A T G C T G A G T A C A G T C A G C T A G C T C A G T C T A G RARa(NR)/K562-RARa-ChIP-Seq(Encode)/Homer1e-82-1.908e+020.00002930.063.76%22170.549.58%motif file (matrix) svg
10 T A C G A C T G A G C T G T A C C G T A T C G A C T G A A C T G C A T G A C G T A G T C C G T A COUP-TFII(NR)/K562-NR2F1-ChIP-Seq(Encode)/Homer1e-82-1.893e+020.00001820.039.61%11860.626.52%motif file (matrix) svg
11 A G T C T G C A T C G A C T G A A C T G C A T G A C G T A T G C G T C A T A C G Erra(NR)/HepG2-Erra-ChIP-Seq(GSE31477)/Homer1e-77-1.791e+020.00002441.053.12%17634.539.44%motif file (matrix) svg
12 T A C G T C G A G A C T A C T G C T G A A G T C T C A G G A C T T G A C C T G A Atf1(bZIP)/K562-ATF1-ChIP-Seq(GSE31477)/Homer1e-75-1.727e+020.0000988.021.50%5313.011.88%motif file (matrix) svg
13 A G C T C T G A C T A G C T A G A C T G T A G C T G C A T C G A C T G A C T A G C A T G A C G T A T G C T C G A RXR(NR),DR1/3T3L1-RXR-ChIP-Seq(GSE13511)/Homer1e-70-1.612e+020.00001508.032.82%9607.921.49%motif file (matrix) svg
14 C G A T T A C G T G C A G T A C G A T C G A C T A G C T A C G T A T C G G T A C G A T C G T A C G A T C G T C A PPARE(NR),DR1/3T3L1-Pparg-ChIP-Seq(GSE13511)/Homer1e-69-1.606e+020.00001324.028.81%8093.418.10%motif file (matrix) svg
15 T C G A T C A G T C G A A C T G C A T G A C G T A G T C C T G A COUP-TFII(NR)/Artia-Nr2f2-ChIP-Seq(GSE46497)/Homer1e-60-1.396e+020.00001942.042.26%13734.630.71%motif file (matrix) svg
16 G C A T T C A G C T G A A T C G A C T G C G A T G A T C C T G A THRb(NR)/Liver-NR1A2-ChIP-Seq(GSE52613)/Homer1e-60-1.383e+020.00003476.075.65%28793.764.39%motif file (matrix) svg
17 T C A G T C A G A C G T G T A C G C T A T C A G C T G A A C T G A C T G A G C T A G T C C G T A EAR2(NR)/K562-NR2F6-ChIP-Seq(Encode)/Homer1e-57-1.319e+020.00001612.035.08%10954.324.50%motif file (matrix) svg
18 G C T A T C G A C G T A C T A G A G C T G T C A G T C A C G T A A G T C C G T A FOXA1(Forkhead)/LNCAP-FOXA1-ChIP-Seq(GSE27824)/Homer1e-56-1.290e+020.00001531.033.32%10302.823.04%motif file (matrix) svg
19 A T G C G A C T A C G T C T A G A C G T A C G T A C G T C T G A G A T C G C T A A G C T C G T A Foxa2(Forkhead)/Liver-Foxa2-ChIP-Seq(GSE25694)/Homer1e-55-1.282e+020.00001140.024.81%7047.015.76%motif file (matrix) svg
20 A C G T C T A G A G C T A C G T A C G T C T G A A G T C G A C T A G C T C G T A FOXM1(Forkhead)/MCF7-FOXM1-ChIP-Seq(GSE72977)/Homer1e-55-1.273e+020.00001361.029.62%8884.219.87%motif file (matrix) svg
21 G C T A T C G A C G T A C T A G A G C T G T C A G T C A C G T A A G T C C G T A FOXA1(Forkhead)/MCF7-FOXA1-ChIP-Seq(GSE26831)/Homer1e-54-1.266e+020.00001303.028.36%8410.218.81%motif file (matrix) svg
22 A G C T A C G T A C T G A T G C A G T C C G T A C T G A T A C G NF1-halfsite(CTF)/LNCaP-NF1-ChIP-Seq(Unpublished)/Homer1e-49-1.137e+020.00002064.044.92%15343.334.31%motif file (matrix) svg
23 G C A T C G T A C G A T G A C T A C T G C T G A G A C T G A T C Hnf6b(Homeobox)/LNCaP-Hnf6b-ChIP-Seq(GSE106305)/Homer1e-48-1.115e+020.00001061.023.09%6649.314.87%motif file (matrix) svg
24 A T G C C T G A A T C G T A C G A G T C C G A T T C A G C G A T C T A G A G C T G T C A G T C A C G T A A G T C C G T A T A C G C T G A Fox:Ebox(Forkhead,bHLH)/Panc1-Foxa2-ChIP-Seq(GSE47459)/Homer1e-47-1.085e+020.00001393.030.32%9471.421.18%motif file (matrix) svg
25 A G T C T A G C G A C T A C G T C T A G A C G T A C G T A C G T C T G A A G T C G C T A G A C T C G T A C T A G A C T G Foxa3(Forkhead)/Liver-Foxa3-ChIP-Seq(GSE77670)/Homer1e-41-9.616e+010.0000525.011.43%2717.66.08%motif file (matrix) svg
26 C A T G G A C T T A C G G T C A G T A C G A T C G A C T A G C T A T C G T C G A T A C G T A G C ERRg(NR)/Kidney-ESRRG-ChIP-Seq(GSE104905)/Homer1e-39-9.200e+010.00001081.023.53%7124.715.93%motif file (matrix) svg
27 C T A G C A G T C G T A A C G T A G T C A C T G C G T A A G C T A G T C G A T C HNF6(Homeobox)/Liver-Hnf6-ChIP-Seq(ERP000394)/Homer1e-39-9.154e+010.0000695.015.13%4035.89.03%motif file (matrix) svg
28 A G T C G A C T C A G T A C T G C T A G T G A C G C T A A T G C G C A T A T C G C G A T A C T G G A T C G T A C G T C A C T G A NF1(CTF)/LNCAP-NF1-ChIP-Seq(Unpublished)/Homer1e-36-8.447e+010.0000561.012.21%3110.76.96%motif file (matrix) svg
29 G C T A A C T G T C G A C T G A C T G A A C G T T A G C C T G A C G T A C G A T Cux2(Homeobox)/Liver-Cux2-ChIP-Seq(GSE35985)/Homer1e-33-7.773e+010.0000565.012.30%3223.47.21%motif file (matrix) svg
30 A G C T T G A C C G A T C G A T C T A G A C G T C A G T C A G T G C T A A G T C FOXK1(Forkhead)/HEK293-FOXK1-ChIP-Seq(GSE51673)/Homer1e-31-7.316e+010.00001244.027.07%8869.119.83%motif file (matrix) svg
31 C G A T C T A G A C G T G T C A C G T A C G T A A G T C C G T A Foxo3(Forkhead)/U2OS-Foxo3-ChIP-Seq(E-MTAB-2701)/Homer1e-30-7.123e+010.0000961.020.91%6495.814.53%motif file (matrix) svg
32 G C A T G C A T C T G A A C G T C T G A A C G T C G T A C G T A C G T A A G T C G T C A G T C A Foxf1(Forkhead)/Lung-Foxf1-ChIP-Seq(GSE77951)/Homer1e-30-7.027e+010.00001065.023.18%7391.716.53%motif file (matrix) svg
33 C A T G A G C T T A C G G T C A G T A C T A G C A G C T G A C T A T C G T C G A Esrrb(NR)/mES-Esrrb-ChIP-Seq(GSE11431)/Homer1e-30-6.954e+010.0000854.018.59%5638.312.61%motif file (matrix) svg
34 A G T C A C G T A C T G A G C T A C G T A C G T G T C A A G T C Foxo1(Forkhead)/RAW-Foxo1-ChIP-Seq(Fan_et_al.)/Homer1e-29-6.757e+010.00002024.044.05%16056.335.91%motif file (matrix) svg
35 C G A T C T A G A C G T A C G T A C G T C G T A A G C T C G A T A G C T C G T A C T A G T A G C FoxD3(forkhead)/ZebrafishEmbryo-Foxd3.biotin-ChIP-seq(GSE106676)/Homer1e-29-6.715e+010.00001095.023.83%7711.917.25%motif file (matrix) svg
36 T C A G C T G A C T A G C A T G A C G T A T G C C T G A C T G A C T G A C T A G C A T G A C G T A T G C C T G A TR4(NR),DR1/Hela-TR4-ChIP-Seq(GSE24685)/Homer1e-29-6.699e+010.0000227.04.94%951.92.13%motif file (matrix) svg
37 T A G C C T A G T C G A G A C T A C T G C T G A A G T C T C A G G C A T T G A C C T G A A G C T Atf7(bZIP)/3T3L1-Atf7-ChIP-Seq(GSE56872)/Homer1e-28-6.676e+010.0000612.013.32%3736.88.36%motif file (matrix) svg
38 C G T A G C T A C G A T C T A G A C G T G T C A C G T A C G T A A G T C C G T A T G C A T A C G FoxL2(Forkhead)/Ovary-FoxL2-ChIP-Seq(GSE60858)/Homer1e-26-6.134e+010.0000976.021.24%6817.815.25%motif file (matrix) svg
39 T A C G T A G C G C T A C G A T C T A G A C G T C A G T C A G T G C T A A G T C G T C A G C A T FOXK2(Forkhead)/U2OS-FOXK2-ChIP-Seq(E-MTAB-2204)/Homer1e-26-6.051e+010.0000829.018.04%5598.512.52%motif file (matrix) svg
40 A G C T A G T C A G T C A C G T C T A G A C G T A C G T A C G T C G T A A G T C G A T C C G T A FOXP1(Forkhead)/H9-FOXP1-ChIP-Seq(GSE31006)/Homer1e-24-5.736e+010.0000596.012.97%3756.48.40%motif file (matrix) svg
41 C T A G A C G T G C A T T C G A G T C A G C A T A T C G C G T A C A G T A G C T C T G A T G C A HNF1b(Homeobox)/PDAC-HNF1B-ChIP-Seq(GSE64557)/Homer1e-24-5.683e+010.0000227.04.94%1029.02.30%motif file (matrix) svg
42 A C G T C T A G C G T A A G T C G T A C A C G T A C G T A C G T G T C A G T A C T G A C G A C T Nur77(NR)/K562-NR4A1-ChIP-Seq(GSE31363)/Homer1e-24-5.662e+010.0000302.06.57%1542.73.45%motif file (matrix) svg
43 A G T C C G A T A C T G A T C G T G A C G C T A C A T G A T C G T G A C C G A T A C T G T A G C G T A C G T C A Tlx?(NR)/NPC-H3K4me1-ChIP-Seq(GSE16256)/Homer1e-23-5.371e+010.0000563.012.25%3553.77.95%motif file (matrix) svg
44 T C G A C A T G C A T G A C G T A T G C T C G A C T G A A G C T T A C G T G C A G T A C G A T C A G C T A G T C FXR(NR),IR1/Liver-FXR-ChIP-Seq(Chong_et_al.)/Homer1e-22-5.209e+010.0000526.011.45%3286.47.35%motif file (matrix) svg
45 T G C A G C A T C G A T C G T A C A G T A C T G G T A C C G T A C T G A A G C T G T C A A C T G C T A G G T C A C G A T A C T G G T A C T G C A C G T A A G C T CEBP:CEBP(bZIP)/MEF-Chop-ChIP-Seq(GSE35681)/Homer1e-21-5.030e+010.0000225.04.90%1072.82.40%motif file (matrix) svg
46 C T A G C T A G A G T C T C A G A C T G A C G T A C G T C T G A MYB(HTH)/ERMYB-Myb-ChIPSeq(GSE22095)/Homer1e-20-4.812e+010.00001867.040.63%15162.333.91%motif file (matrix) svg
47 G A C T C T A G C T A G A G T C T G C A A C T G A C G T A C G T C T A G T C A G AMYB(HTH)/Testes-AMYB-ChIP-Seq(GSE44588)/Homer1e-20-4.716e+010.00001704.037.08%13677.830.59%motif file (matrix) svg
48 A T G C T C A G T C G A G C A T A C T G C G T A A G T C T C A G G A C T T G A C C G T A A G C T Atf2(bZIP)/3T3L1-Atf2-ChIP-Seq(GSE56872)/Homer1e-20-4.605e+010.0000435.09.47%2667.45.97%motif file (matrix) svg
49 C T A G C T A G G C A T C G A T C T G A G T C A G C T A A T G C C G T A C A G T G A C T C G T A T G C A Hnf1(Homeobox)/Liver-Foxa2-Chip-Seq(GSE25694)/Homer1e-19-4.539e+010.0000203.04.42%968.32.17%motif file (matrix) svg
50 A C G T G A C T T A G C C G T A C T G A C A T G C T A G G A C T G A T C C G T A Nr5a2(NR)/Pancreas-LRH1-ChIP-Seq(GSE34295)/Homer1e-17-4.031e+010.0000845.018.39%6173.713.81%motif file (matrix) svg
51 T G A C C T G A C T A G T C G A C T G A A T G C C G T A A C T G G C A T G T A C G C A T A T C G G C A T A G C T G A T C PR(NR)/T47D-PR-ChIP-Seq(GSE31130)/Homer1e-16-3.890e+010.00002163.047.07%18282.640.88%motif file (matrix) svg
52 C A T G A C T G A G C T A T G C C G T A A T G C G T A C G A C T T A C G C T G A A C T G A C T G G C A T A T G C C T G A THRb(NR)/HepG2-THRb.Flag-ChIP-Seq(Encode)/Homer1e-16-3.808e+010.0000675.014.69%4775.110.68%motif file (matrix) svg
53 C G T A C G T A C G T A A G T C A G C T C T G A A C T G A C T G A G C T A G T C C G T A C T A G C T A G C T A G T G C A RORa(NR)/Liver-Rora-ChIP-Seq(GSE101115)/Homer1e-16-3.762e+010.0000206.04.48%1065.52.38%motif file (matrix) svg
54 G A C T C T A G A T G C A G T C G T C A T A C G A T G C A T C G HIC1(Zf)/Treg-ZBTB29-ChIP-Seq(GSE99889)/Homer1e-15-3.551e+010.00002044.044.48%17277.338.64%motif file (matrix) svg
55 G C T A C G T A A G T C A C G T T C G A T A C G A C T G A G C T A G T C T C G A RORgt(NR)/EL4-RORgt.Flag-ChIP-Seq(GSE56019)/Homer1e-15-3.530e+010.0000189.04.11%970.82.17%motif file (matrix) svg
56 G C T A C G T A A G T C A C G T T C G A T A C G A C T G A G C T A G T C T C G A RORgt(NR)/EL4-RORgt.Flag-ChIP-Seq(GSE56019)/Homer1e-15-3.530e+010.0000189.04.11%970.82.17%motif file (matrix) svg
57 A C G T T G C A A G C T G A T C C T A G C T G A A G C T G T C A T C G A C G T A CUX1(Homeobox)/K562-CUX1-ChIP-Seq(GSE92882)/Homer1e-15-3.526e+010.0000700.015.23%5054.811.30%motif file (matrix) svg
58 C T G A A C G T A C G T A C G T A G T C G A C T C G A T C T G A A C T G C G T A C G T A T C G A STAT5(Stat)/mCD4+-Stat5-ChIP-Seq(GSE12346)/Homer1e-15-3.516e+010.0000448.09.75%2956.46.61%motif file (matrix) svg
59 C T A G T A C G G A T C G T A C G C T A A G C T A G C T G T C A T C G A T A G C Nanog(Homeobox)/mES-Nanog-ChIP-Seq(GSE11724)/Homer1e-14-3.413e+010.00003333.072.54%30023.767.14%motif file (matrix) svg
60 C G A T G A C T C G A T T C A G G A C T A C G T C A G T C T G A G A C T G A C T A G C T C G A T A C T G A T C G G T A C G C T A NF1:FOXA1(CTF,Forkhead)/LNCAP-FOXA1-ChIP-Seq(GSE27824)/Homer1e-14-3.336e+010.0000106.02.31%435.70.97%motif file (matrix) svg
61 T A C G A T G C G C T A A C T G C G T A A C G T C G T A C T G A T A C G T C G A Gata4(Zf)/Heart-Gata4-ChIP-Seq(GSE35151)/Homer1e-14-3.334e+010.00001025.022.31%7946.717.77%motif file (matrix) svg
62 C T G A C T A G T C G A C G T A A T G C C G T A A T C G C G A T T A G C G C A T A T C G G C A T A G C T G A T C G A C T A G C T ARE(NR)/LNCAP-AR-ChIP-Seq(GSE27824)/Homer1e-14-3.315e+010.0000333.07.25%2075.84.64%motif file (matrix) svg
63 A T G C T C G A A G T C A G C T A C G T G T A C A G T C G C T A C T A G C A T G G T C A C T G A T C A G A G T C Stat3+il21(Stat)/CD4-Stat3-ChIP-Seq(GSE19198)/Homer1e-14-3.260e+010.0000798.017.37%5968.813.35%motif file (matrix) svg
64 T G A C C T G A C T A G C T G A C G T A A G T C C T G A A C G T G C A T T A G C G C A T A T C G G A C T G A C T G A T C GRE(NR),IR3/RAW264.7-GRE-ChIP-Seq(Unpublished)/Homer1e-13-3.173e+010.0000317.06.90%1973.64.41%motif file (matrix) svg
65 T C G A G C A T A C T G C T G A A G T C T C A G G A C T G T A C C G T A A G C T A G T C G A T C c-Jun-CRE(bZIP)/K562-cJun-ChIP-Seq(GSE31477)/Homer1e-13-3.156e+010.0000367.07.99%2371.55.30%motif file (matrix) svg
66 A G C T A G T C A T G C A G C T A C G T C G T A A C G T A G T C C G A T A T G C Gata2(Zf)/K562-GATA2-ChIP-Seq(GSE18829)/Homer1e-13-3.098e+010.0000703.015.30%5189.811.61%motif file (matrix) svg
67 C G A T C T G A C G T A C A G T A G T C G A T C G A T C A C T G Pitx1(Homeobox)/Chicken-Pitx1-ChIP-Seq(GSE38910)/Homer1e-13-3.048e+010.00003218.070.03%29003.964.86%motif file (matrix) svg
68 A G T C G C A T C G T A C G T A G T A C A C G T A C T G G A T C G A T C T C G A BMYB(HTH)/Hela-BMYB-ChIP-Seq(GSE27030)/Homer1e-12-2.952e+010.00001584.034.47%13184.029.48%motif file (matrix) svg
69 T A C G C T G A C T A G T C G A C G T A A G T C C T G A A T C G G C A T T A G C G A C T A C T G A C G T A G C T A G T C G A C T GRE(NR),IR3/A549-GR-ChIP-Seq(GSE32465)/Homer1e-12-2.926e+010.0000195.04.24%1082.22.42%motif file (matrix) svg
70 C T A G G C A T G A T C C G T A A G T C T C A G G A C T C T A G CLOCK(bHLH)/Liver-Clock-ChIP-Seq(GSE39860)/Homer1e-12-2.912e+010.0000615.013.38%4482.010.02%motif file (matrix) svg
71 A T G C C T G A G A C T A C G T A C G T G T A C G A T C C G A T C T A G C A T G C G T A C G T A C T G A G A C T STAT1(Stat)/HelaS3-STAT1-ChIP-Seq(GSE12782)/Homer1e-12-2.908e+010.0000370.08.05%2441.65.46%motif file (matrix) svg
72 T A G C G C T A A C T G C G T A A C G T C G T A C G T A T A C G T C A G T C G A Gata1(Zf)/K562-GATA1-ChIP-Seq(GSE18829)/Homer1e-12-2.885e+010.0000629.013.69%4609.710.31%motif file (matrix) svg
73 G C T A A T C G G C T A G A C T G C T A T C G A T A G C T C G A GATA3(Zf)/iTreg-Gata3-ChIP-Seq(GSE20898)/Homer1e-12-2.850e+010.00001424.030.99%11740.526.25%motif file (matrix) svg
74 T C A G A G C T A T G C C G T A A G C T T C A G C A G T A C T G C T G A A G T C MITF(bHLH)/MastCells-MITF-ChIP-Seq(GSE48085)/Homer1e-12-2.806e+010.00001002.021.81%7911.617.69%motif file (matrix) svg
75 A C T G C A G T A C G T C G T A C G T A A C G T A C T G C T G A Nkx6.1(Homeobox)/Islet-Nkx6.1-ChIP-Seq(GSE40975)/Homer1e-11-2.704e+010.00002208.048.05%19204.742.95%motif file (matrix) svg
76 T A G C G T A C C G T A C T A G A C T G T G C A C G T A A T G C C G T A A T C G AR-halfsite(NR)/LNCaP-AR-ChIP-Seq(GSE27824)/Homer1e-11-2.673e+010.00003262.070.99%29611.666.22%motif file (matrix) svg
77 A G C T A T G C G A C T G C A T C G T A A G C T G T A C C G A T A T C G A G T C Gata6(Zf)/HUG1N-GATA6-ChIP-Seq(GSE51936)/Homer1e-11-2.656e+010.0000920.020.02%7228.916.17%motif file (matrix) svg
78 G C A T C T A G G T A C A G T C C G A T A C T G C T A G C T A G G T A C G C T A ZNF416(Zf)/HEK293-ZNF416.GFP-ChIP-Seq(GSE58341)/Homer1e-11-2.593e+010.00001355.029.49%11206.025.06%motif file (matrix) svg
79 A C G T G A C T A T G C G C T A C T G A C T A G A C T G G A C T A G T C C G T A Nr5a2(NR)/mES-Nr5a2-ChIP-Seq(GSE19019)/Homer1e-11-2.564e+010.0000610.013.28%4532.310.14%motif file (matrix) svg
80 T C G A A C T G C A T G A G C T A G T C C G T A C T G A C T A G A C T G C G A T A T G C C T G A RAR:RXR(NR),DR5/ES-RAR-ChIP-Seq(GSE56893)/Homer1e-10-2.530e+010.0000175.03.81%985.92.20%motif file (matrix) svg
81 T C G A C T A G A G T C A G T C C G T A C G T A A C G T T A G C T C A G T A C G NFY(CCAAT)/Promoter/Homer1e-10-2.522e+010.0000850.018.50%6649.914.87%motif file (matrix) svg
82 G C T A T A G C A G C T A T C G G T C A C G T A G C T A A T G C G A T C C T G A IRF4(IRF)/GM12878-IRF4-ChIP-Seq(GSE32465)/Homer1e-10-2.522e+010.0000483.010.51%3459.87.74%motif file (matrix) svg
83 C G T A A C T G C G T A A C G T C G T A C T G A T C A G T G C A A G C T T G A C TRPS1(Zf)/MCF7-TRPS1-ChIP-Seq(GSE107013)/Homer1e-10-2.517e+010.00001824.039.70%15624.734.94%motif file (matrix) svg
84 T A C G T C A G A G C T A T G C C G T A A G T C T C A G A C G T A C T G T C G A USF1(bHLH)/GM12878-Usf1-ChIP-Seq(GSE32465)/Homer1e-10-2.517e+010.0000498.010.84%3587.78.02%motif file (matrix) svg
85 C T A G T A C G G A T C G T C A T G C A A C G T T G C A G C T A T C G A T G C A Hoxa9(Homeobox)/ChickenMSG-Hoxa9.Flag-ChIP-Seq(GSE86088)/Homer1e-10-2.490e+010.00002411.052.47%21268.747.56%motif file (matrix) svg
86 C T A G C A T G A C G T A G T C G C T A A G C T A G T C A G C T T C A G C T G A A C T G C A T G G C A T A T G C C G T A THRa(NR)/C17.2-THRa-ChIP-Seq(GSE38347)/Homer1e-10-2.466e+010.0000488.010.62%3515.47.86%motif file (matrix) svg
87 A G T C G A C T C A G T G T A C A G T C A T C G T C A G A C T G G T C A C G T A Stat3(Stat)/mES-Stat3-ChIP-Seq(GSE11431)/Homer1e-10-2.463e+010.0000569.012.38%4207.29.41%motif file (matrix) svg
88 T G A C C G T A C T G A A C T G A C T G G A C T G A T C T G C A G T A C T A C G SF1(NR)/H295R-Nr5a1-ChIP-Seq(GSE44220)/Homer1e-10-2.395e+010.0000553.012.03%4088.49.14%motif file (matrix) svg
89 T C A G A G C T A C G T A C G T G T A C G A T C C G T A C T A G C A T G G T C A C G T A T C G A STAT4(Stat)/CD4-Stat4-ChIP-Seq(GSE22104)/Homer1e-10-2.373e+010.00001020.022.20%8228.918.40%motif file (matrix) svg
90 A C T G T G A C G T A C C G T A A G T C T A C G A C G T A C T G G T C A A G T C NPAS2(bHLH)/Liver-NPAS2-ChIP-Seq(GSE39860)/Homer1e-10-2.356e+010.00001117.024.31%9116.320.39%motif file (matrix) svg
91 C G T A C G T A C G T A A G T C A G C T C T G A A C T G C T A G A G C T A G T C C G T A T C A G RORg(NR)/Liver-Rorc-ChIP-Seq(GSE101115)/Homer1e-10-2.348e+010.0000137.02.98%731.61.64%motif file (matrix) svg
92 A G T C C G A T C T G A C G T A A C G T C A G T T C A G T G A C Isl1(Homeobox)/Neuron-Isl1-ChIP-Seq(GSE31456)/Homer1e-10-2.318e+010.00001824.039.70%15717.435.15%motif file (matrix) svg
93 T C G A A C G T C A T G G C T A T A G C C G A T G T A C G C T A A C G T A T G C AP-1(bZIP)/ThioMac-PU.1-ChIP-Seq(GSE21512)/Homer1e-9-2.297e+010.0000730.015.89%5660.412.66%motif file (matrix) svg
94 T A G C G A T C G T A C C T G A G C A T C T G A C G T A T G C A C G T A G A T C Hoxa13(Homeobox)/ChickenMSG-Hoxa13.Flag-ChIP-Seq(GSE86088)/Homer1e-9-2.260e+010.00002190.047.66%19244.543.04%motif file (matrix) svg
95 C T A G C A T G C A T G A G C T G A C T C G T A G A T C A G C T G T C A A T G C T C G A C T A G C A T G A C G T A G T C C T G A LXRE(NR),DR4/RAW-LXRb.biotin-ChIP-Seq(GSE21512)/Homer1e-9-2.234e+010.000087.01.89%398.70.89%motif file (matrix) svg
96 C T A G T C G A G C A T C A T G G C T A T A G C C G A T G T A C C T G A A G C T JunB(bZIP)/DendriticCells-Junb-ChIP-Seq(GSE36099)/Homer1e-9-2.189e+010.0000545.011.86%4077.19.12%motif file (matrix) svg
97 A C T G C T A G T C G A C G A T C A T G G C T A A T C G C G A T G T A C G C T A A G C T G T A C Fra1(bZIP)/BT549-Fra1-ChIP-Seq(GSE46166)/Homer1e-9-2.148e+010.0000542.011.80%4063.69.09%motif file (matrix) svg
98 A G C T C A T G G C A T G A T C T G C A C T A G G A T C A C G T Tgif2(Homeobox)/mES-Tgif2-ChIP-Seq(GSE55404)/Homer1e-9-2.103e+010.00002919.063.53%26453.959.16%motif file (matrix) svg
99 C T A G T C G A C G A T A C T G C G T A T A C G A G C T T G A C G C T A A C G T G A T C T A G C Fosl2(bZIP)/3T3L1-Fosl2-ChIP-Seq(GSE56872)/Homer1e-9-2.096e+010.0000348.07.57%2430.25.43%motif file (matrix) svg
100 C G T A C T G A C T A G C G T A C G T A A G T C C G T A C A G T G C A T G T C A C G A T A C T G A C G T G C A T G A T C PGR(NR)/EndoStromal-PGR-ChIP-Seq(GSE69539)/Homer1e-9-2.083e+010.0000291.06.33%1964.14.39%motif file (matrix) svg
101 C T A G T C G A A C G T A C T G C G T A A T G C A C G T G T A C C G T A A G C T G A T C G T A C Atf3(bZIP)/GBM-ATF3-ChIP-Seq(GSE33912)/Homer1e-9-2.073e+010.0000637.013.86%4914.710.99%motif file (matrix) svg
102 G A C T A G T C C G A T A C T G C T G A T G A C G T A C C G T A A T C G G C A T C T G A C T A G Bcl11a(Zf)/HSPC-BCL11A-ChIP-Seq(GSE104676)/Homer1e-8-2.016e+010.0000801.017.43%6389.914.29%motif file (matrix) svg
103 T A C G C T A G A T G C G A T C G T A C A G T C C T A G A G T C A G T C A G T C G T A C A G T C Sp1(Zf)/Promoter/Homer1e-8-2.013e+010.0000280.06.09%1889.54.23%motif file (matrix) svg
104 G A C T T C A G C T A G A G T C A G T C G T A C A G T C C T G A A G T C A G T C A G T C G A C T A G T C A C T G A T G C KLF3(Zf)/MEF-Klf3-ChIP-Seq(GSE44748)/Homer1e-8-1.998e+010.0000534.011.62%4038.19.03%motif file (matrix) svg
105 A T G C T A C G A G T C G A C T T G C A C T G A G A C T A C G T C T G A T C A G LXH9(Homeobox)/Hct116-LXH9.V5-ChIP-Seq(GSE116822)/Homer1e-8-1.959e+010.00001325.028.84%11203.225.05%motif file (matrix) svg
106 C A T G G A C T G C A T A C T G A G C T A C T G A C T G C G T A G C A T A G C T A T C G T A C G Foxh1(Forkhead)/hESC-FOXH1-ChIP-Seq(GSE29422)/Homer1e-8-1.932e+010.0000640.013.93%4987.411.15%motif file (matrix) svg
107 C A G T T G C A A C G T A C T G C G T A A T C G C G A T T G A C C G T A A C G T BATF(bZIP)/Th17-BATF-ChIP-Seq(GSE39756)/Homer1e-8-1.927e+010.0000633.013.78%4927.211.02%motif file (matrix) svg
108 C G A T T G C A G T A C C G T A A G T C C T A G G A C T C A T G NPAS(bHLH)/Liver-NPAS-ChIP-Seq(GSE39860)/Homer1e-8-1.910e+010.00001502.032.69%12880.228.80%motif file (matrix) svg
109 A G C T G C A T A C T G A C G T A G T C A C G T C T A G T A C G Smad3(MAD)/NPC-Smad3-ChIP-Seq(GSE36673)/Homer1e-8-1.885e+010.00002622.057.06%23643.152.87%motif file (matrix) svg
110 T C A G A C G T A G T C T C G A A G T C T C A G G C A T C T A G C T A G A G C T Usf2(bHLH)/C2C12-Usf2-ChIP-Seq(GSE36030)/Homer1e-8-1.870e+010.0000354.07.70%2534.25.67%motif file (matrix) svg
111 C G T A A C G T A C G T A C G T A C G T A G T C A G T C C T G A A G C T A G C T NFAT(RHD)/Jurkat-NFATC1-ChIP-Seq(Jolma_et_al.)/Homer1e-8-1.847e+010.0000871.018.96%7085.415.84%motif file (matrix) svg
112 T C G A T A G C T G A C C T G A A G T C A C T G G A C T C A T G c-Myc(bHLH)/LNCAP-cMyc-ChIP-Seq(Unpublished)/Homer1e-7-1.796e+010.0000449.09.77%3363.87.52%motif file (matrix) svg
113 C A T G T G A C C G T A A G T C T A C G G C A T A C T G G T C A A T G C A G T C bHLHE41(bHLH)/proB-Bhlhe41-ChIP-Seq(GSE93764)/Homer1e-7-1.778e+010.00001041.022.66%8664.119.38%motif file (matrix) svg
114 T C G A T G A C A G T C C G T A A G T C C T A G A C G T A C T G A C T G A G C T A G T C G C A T Max(bHLH)/K562-Max-ChIP-Seq(GSE31477)/Homer1e-7-1.726e+010.0000670.014.58%5326.211.91%motif file (matrix) svg
115 C G T A C G T A A G C T G A C T T G C A G T C A A C G T A G C T C T G A T C A G Lhx3(Homeobox)/Neuron-Lhx3-ChIP-Seq(GSE31456)/Homer1e-7-1.691e+010.00001494.032.51%12918.528.89%motif file (matrix) svg
116 C A T G C T A G T C G A A C G T A C T G C G T A T A G C C G A T T G A C C G T A A G C T G A T C Fra2(bZIP)/Striatum-Fra2-ChIP-Seq(GSE43429)/Homer1e-7-1.679e+010.0000458.09.97%3476.77.77%motif file (matrix) svg
117 C T A G C A G T T G A C C G T A G A T C T C A G G A C T C A T G BMAL1(bHLH)/Liver-Bmal1-ChIP-Seq(GSE39860)/Homer1e-7-1.674e+010.00001680.036.56%14684.732.84%motif file (matrix) svg
118 C T G A T C A G G T A C G C T A A C T G T G A C G C A T C A T G SCL(bHLH)/HPC7-Scl-ChIP-Seq(GSE13511)/Homer1e-7-1.673e+010.00003445.074.97%31958.271.47%motif file (matrix) svg
119 C T A G C A T G T G C A A C G T A G T C C G T A C A T G T C A G A C G T A C G T G C T A A G T C Six1(Homeobox)/Myoblast-Six1-ChIP-Chip(GSE20150)/Homer1e-7-1.656e+010.0000279.06.07%1959.84.38%motif file (matrix) svg
120 T C G A A C G T A C T G C T G A A G T C T C A G A G C T G T A C C G T A A G C T G A T C T C G A JunD(bZIP)/K562-JunD-ChIP-Seq/Homer1e-7-1.636e+010.0000125.02.72%733.31.64%motif file (matrix) svg
121 T G C A C G T A G T C A A G C T A G T C G C T A T A G C C G A T C T A G G A T C Gfi1b(Zf)/HPC7-Gfi1b-ChIP-Seq(GSE22178)/Homer1e-7-1.621e+010.0000666.014.49%5330.111.92%motif file (matrix) svg
122 A T C G G A T C G C A T C G T A G T C A A C G T A T G C A G T C CRX(Homeobox)/Retina-Crx-ChIP-Seq(GSE20012)/Homer1e-7-1.612e+010.00002000.043.53%17772.639.74%motif file (matrix) svg
123 G T C A C T G A T C A G G A T C T G C A T G C A A C G T T C A G C G T A C G T A C G T A G C T A Hoxd12(Homeobox)/ChickenMSG-Hoxd12.Flag-ChIP-Seq(GSE86088)/Homer1e-6-1.562e+010.00001611.035.06%14094.831.52%motif file (matrix) svg
124 T A G C T A G C G C A T C A T G A C T G G C T A C G T A A C G T A C T G G A T C TEAD4(TEA)/Tropoblast-Tead4-ChIP-Seq(GSE37350)/Homer1e-6-1.555e+010.0000814.017.71%6691.714.96%motif file (matrix) svg
125 A T G C A G T C G C A T A G C T A C G T T C A G C G A T A G C T G A T C A T C G Sox10(HMG)/SciaticNerve-Sox3-ChIP-Seq(GSE35132)/Homer1e-6-1.547e+010.00001638.035.65%14358.132.11%motif file (matrix) svg
126 C T G A T C A G C A G T C T A G A C T G C T A G G A T C A T C G A C T G C T G A T C A G G A T C Sp5(Zf)/mES-Sp5.Flag-ChIP-Seq(GSE72989)/Homer1e-6-1.541e+010.0000924.020.11%7700.717.22%motif file (matrix) svg
127 A T G C A T C G T A C G A G C T A T C G C T G A A G T C C T A G A G C T A T G C C T G A A T G C CRE(bZIP)/Promoter/Homer1e-6-1.520e+010.0000256.05.57%1800.64.03%motif file (matrix) svg
128 C A T G G T A C C G T A A G T C C T A G A C G T A C T G G T A C A G T C A G C T bHLHE40(bHLH)/HepG2-BHLHE40-ChIP-Seq(GSE31477)/Homer1e-6-1.517e+010.0000324.07.05%2372.75.31%motif file (matrix) svg
129 T A C G T C G A T A G C A G T C C G T A A G T C C T A G G C A T A C T G A T C G n-Myc(bHLH)/mES-nMyc-ChIP-Seq(GSE11431)/Homer1e-6-1.479e+010.0000671.014.60%5431.612.15%motif file (matrix) svg
130 G A C T G C A T C T A G C G A T G A T C T C G A C A T G G A T C Tgif1(Homeobox)/mES-Tgif1-ChIP-Seq(GSE55404)/Homer1e-6-1.438e+010.00002738.059.59%25052.956.03%motif file (matrix) svg
131 G T C A T C G A C T A G C T A G A G T C G T C A C G A T C T A G G A C T G A T C G A T C T C A G C T A G C T G A A G T C G C T A C A G T T C A G G A T C G A T C p63(p53)/Keratinocyte-p63-ChIP-Seq(GSE17611)/Homer1e-6-1.437e+010.0000401.08.73%3057.46.84%motif file (matrix) svg
132 C T G A T G A C T G A C C G T A A C G T T G A C A G C T C T A G A C G T G A C T Olig2(bHLH)/Neuron-Olig2-ChIP-Seq(GSE30882)/Homer1e-6-1.430e+010.00001863.040.54%16571.237.06%motif file (matrix) svg
133 G T A C C A G T A C T G A C T G A C T G G A T C A C T G A C G T A C T G A C T G A G T C G A T C KLF6(Zf)/PDAC-KLF6-ChIP-Seq(GSE64557)/Homer1e-6-1.402e+010.0000976.021.24%8245.618.44%motif file (matrix) svg
134 G A T C G T A C C G A T A C T G A C T G C G T A C G T A A C G T A C T G G A T C TEAD(TEA)/Fibroblast-PU.1-ChIP-Seq(Unpublished)/Homer1e-6-1.400e+010.0000650.014.15%5275.311.80%motif file (matrix) svg
135 A T G C G A T C C G A T C T A G A C T G G C T A C G T A A G C T A C T G A G C T TEAD2(TEA)/Py2T-Tead2-ChIP-Seq(GSE55709)/Homer1e-6-1.399e+010.0000515.011.21%4071.09.10%motif file (matrix) svg
136 G A C T T C G A C G A T T A C G C T A G T A C G A C T G A T G C G T A C G T A C Zac1(Zf)/Neuro2A-Plagl1-ChIP-Seq(GSE75942)/Homer1e-6-1.394e+010.00002229.048.51%20116.544.99%motif file (matrix) svg
137 C T A G T C A G C A G T T C A G A C T G A C T G G A T C C T A G A C T G C T A G T C A G A T G C KLF14(Zf)/HEK293-KLF14.GFP-ChIP-Seq(GSE58341)/Homer1e-5-1.378e+010.00001492.032.47%13077.629.25%motif file (matrix) svg
138 C T G A C T G A C T G A A T G C G A T C C A T G A C T G G A C T G A C T G C A T C G T A C G T A A G T C G T A C C T G A A T C G G C A T G A C T G A C T A G C T GRHL2(CP2)/HBE-GRHL2-ChIP-Seq(GSE46194)/Homer1e-5-1.374e+010.0000379.08.25%2886.36.45%motif file (matrix) svg
139 C T G A C T A G A T C G A G C T A C T G G A C T A G T C C T G A Tbx5(T-box)/HL1-Tbx5.biotin-ChIP-Seq(GSE21529)/Homer1e-5-1.367e+010.00002751.059.87%25227.656.42%motif file (matrix) svg
140 A G T C C T A G C T A G A G T C G A T C G T A C A G T C C T A G A G T C A G T C A G T C G T A C Sp2(Zf)/HEK293-Sp2.eGFP-ChIP-Seq(Encode)/Homer1e-5-1.341e+010.00001325.028.84%11526.725.78%motif file (matrix) svg
141 G C A T C G A T C T A G G C T A G C A T C G T A G A T C G C T A C G A T C G A T C T A G C G A T C G T A C G A T G A T C DMRT1(DM)/Testis-DMRT1-ChIP-Seq(GSE64892)/Homer1e-5-1.337e+010.0000270.05.88%1963.94.39%motif file (matrix) svg
142 A T C G G A C T C G A T C G T A C G T A C A G T G A T C G A T C G A T C A G C T Otx2(Homeobox)/EpiLC-Otx2-ChIP-Seq(GSE56098)/Homer1e-5-1.331e+010.0000687.014.95%5638.712.61%motif file (matrix) svg
143 G A C T C G A T T C A G G A T C G A C T A G C T A G C T A G T C G A T C C G T A C T A G C T A G T C G A T C G A C T G A Bcl6(Zf)/Liver-Bcl6-ChIP-Seq(GSE31578)/Homer1e-5-1.319e+010.00001320.028.73%11492.725.70%motif file (matrix) svg
144 C T A G C G T A G T C A C G T A A G T C G A T C A G C T C T A G C G T A A C G T G T C A G A T C Six2(Homeobox)/NephronProgenitor-Six2-ChIP-Seq(GSE39837)/Homer1e-5-1.297e+010.0000947.020.61%8031.717.96%motif file (matrix) svg
145 C T A G T C G A A C G T A C T G C G T A T A G C C G A T G T A C C G T A A G C T G A T C G T A C Jun-AP1(bZIP)/K562-cJun-ChIP-Seq(GSE31477)/Homer1e-5-1.290e+010.0000236.05.14%1689.63.78%motif file (matrix) svg
146 T C A G C G T A A G T C A G C T C G T A A G T C C T G A C G T A A G T C G C A T A G T C A G T C A G T C C T G A A C T G T G C A T C G A C A T G A T C G G A T C Ronin(THAP)/ES-Thap11-ChIP-Seq(GSE51522)/Homer1e-5-1.289e+010.000043.00.94%189.80.42%motif file (matrix) svg
147 C A G T C G A T G C A T G C A T G T C A A G C T C A T G C T A G A T G C G T A C Hoxa11(Homeobox)/ChickenMSG-Hoxa11.Flag-ChIP-Seq(GSE86088)/Homer1e-5-1.259e+010.00002008.043.70%18074.640.42%motif file (matrix) svg
148 T G C A A C T G T A C G A T G C A G T C G A C T T C G A A T C G ZNF711(Zf)/SHSY5Y-ZNF711-ChIP-Seq(GSE20673)/Homer1e-5-1.243e+010.00001567.034.10%13871.131.02%motif file (matrix) svg
149 C G T A C G T A C G T A G C A T G C A T A C T G G T A C G A C T C T A G G C T A T A C G G A C T T G A C C G T A A G C T NFE2L2(bZIP)/HepG2-NFE2L2-ChIP-Seq(Encode)/Homer1e-5-1.206e+010.000067.01.46%361.80.81%motif file (matrix) svg
150 T C G A T G A C G T A C C G T A C A G T T G A C A C G T A C T G A G C T A G C T NeuroG2(bHLH)/Fibroblast-NeuroG2-ChIP-Seq(GSE75910)/Homer1e-5-1.200e+010.00001526.033.21%13510.730.21%motif file (matrix) svg
151 G A T C T C G A A G T C C G A T C G A T A G T C A T G C A C T G A T C G G A C T Elk1(ETS)/Hela-Elk1-ChIP-Seq(GSE31477)/Homer1e-5-1.200e+010.0000582.012.67%4748.410.62%motif file (matrix) svg
152 A G C T C T A G T G A C C G T A A C G T C G A T A G T C A G T C C T G A C A T G TEAD3(TEA)/HepG2-TEAD3-ChIP-Seq(Encode)/Homer1e-5-1.195e+010.00001060.023.07%9134.220.43%motif file (matrix) svg
153 C A T G A G T C G A C T C G T A C G A T G C A T G A C T G C A T C G A T C T A G C A T G T G A C Mef2b(MADS)/HEK293-Mef2b.V5-ChIP-Seq(GSE67450)/Homer1e-5-1.188e+010.0000730.015.89%6095.813.63%motif file (matrix) svg
154 A T G C A T G C A T C G T A C G A G C T A G T C G C T A A G T C T C A G G A C T A C T G T C G A E-box(bHLH)/Promoter/Homer1e-5-1.183e+010.000094.02.05%564.71.26%motif file (matrix) svg
155 T C A G C T A G C T A G T G C A C G A T C G A T C G T A C T A G GSC(Homeobox)/FrogEmbryos-GSC-ChIP-Seq(DRA000576)/Homer1e-5-1.182e+010.0000987.021.48%8463.018.93%motif file (matrix) svg
156 G A C T G T A C T G C A A C G T G A T C G C T A T C G A A C G T A G T C C G T A Pdx1(Homeobox)/Islet-Pdx1-ChIP-Seq(SRA008281)/Homer1e-4-1.142e+010.0000883.019.22%7523.416.82%motif file (matrix) svg
157 G A C T C A G T A G C T C G A T A G T C G A T C A G T C C G T A A T G C T C A G Rbpj1(?)/Panc1-Rbpj1-ChIP-Seq(GSE47459)/Homer1e-4-1.130e+010.00001426.031.03%12609.328.20%motif file (matrix) svg
158 T G A C A G T C C T G A T G A C C G T A A C G T A C G T A G T C A G T C C G T A TEAD1(TEAD)/HepG2-TEAD1-ChIP-Seq(Encode)/Homer1e-4-1.125e+010.0000906.019.72%7745.417.32%motif file (matrix) svg
159 G A C T C T A G G A T C C A G T A C T G C T G A A T G C G C A T A T G C C T G A MafA(bZIP)/Islet-MafA-ChIP-Seq(GSE30298)/Homer1e-4-1.117e+010.0000848.018.45%7214.716.13%motif file (matrix) svg
160 T C A G T C A G T A G C A G T C C T G A A G T C C T A G A C G T A C T G A T C G c-Myc(bHLH)/mES-cMyc-ChIP-Seq(GSE11431)/Homer1e-4-1.110e+010.0000480.010.45%3871.88.66%motif file (matrix) svg
161 C T G A T C A G G T A C T G C A A G T C C G T A A G T C A C T G A C G T A C T G MNT(bHLH)/HepG2-MNT-ChIP-Seq(Encode)/Homer1e-4-1.103e+010.0000989.021.52%8527.819.07%motif file (matrix) svg
162 C G A T T A C G T G A C G A C T C A T G C G T A T A C G A C G T G T A C C T G A Bach2(bZIP)/OCILy7-Bach2-ChIP-Seq(GSE44420)/Homer1e-4-1.100e+010.0000202.04.40%1452.83.25%motif file (matrix) svg
163 C T G A C A T G A C T G A C G T A T G C C G T A C A T G T A C G A T G C G C T A T A C G C T G A C T A G A C T G A C G T A T G C C G T A T A G C RAR:RXR(NR),DR5/ES-RAR-ChIP-Seq(GSE56893)/Homer1e-4-1.100e+010.000046.01.00%225.80.50%motif file (matrix) svg
164 C G T A C T A G A C T G A C T G G A C T C T A G C A G T C T A G C A T G G A T C KLF5(Zf)/LoVo-KLF5-ChIP-Seq(GSE49402)/Homer1e-4-1.096e+010.00001155.025.14%10080.322.54%motif file (matrix) svg
165 T G C A A G C T C T G A A T C G G A C T C T A G G T A C G A T C G T C A A G T C G T A C G A C T C T A G A T C G G C A T C A T G C A T G G A T C G T A C C T G A CTCF(Zf)/CD4+-CTCF-ChIP-Seq(Barski_et_al.)/Homer1e-4-1.089e+010.0000142.03.09%959.52.15%motif file (matrix) svg
166 C A G T T C A G A G C T G A C T A C G T A G T C G A T C G A C T C T G A A C T G G A T C C G T A C T G A A G T C G T A C Rfx6(HTH)/Min6b1-Rfx6.HA-ChIP-Seq(GSE62844)/Homer1e-4-1.082e+010.00011097.023.87%9546.921.35%motif file (matrix) svg
167 T C A G T G A C G T A C C G T A A C G T T G A C A C G T T C A G A G C T G A C T NeuroD1(bHLH)/Islet-NeuroD1-ChIP-Seq(GSE30298)/Homer1e-4-1.080e+010.0001839.018.26%7151.215.99%motif file (matrix) svg
168 A G C T G T C A C G T A A C G T A C G T C T G A T C A G A T G C Lhx2(Homeobox)/HFSC-Lhx2-ChIP-Seq(GSE48068)/Homer1e-4-1.074e+010.0001962.020.94%8293.718.55%motif file (matrix) svg
169 C T G A A C T G A C T G A G T C A G T C A G C T C T A G T A C G ZFX(Zf)/mES-Zfx-ChIP-Seq(GSE11431)/Homer1e-4-1.071e+010.00011189.025.88%10415.623.29%motif file (matrix) svg
170 T C G A C G T A A G T C A G C T C G T A A G T C T C G A G C T A G A C T C G A T A G T C A G T C A G T C C T G A T C A G T G C A T C G A C A G T A T C G A G T C GFY-Staf(?,Zf)/Promoter/Homer1e-4-1.042e+010.000161.01.33%337.30.75%motif file (matrix) svg
171 T C A G T A G C G A C T C A T G C T G A A T C G G C A T G T A C C G T A A C T G T A G C T G C A MafK(bZIP)/C2C12-MafK-ChIP-Seq(GSE36030)/Homer1e-4-1.032e+010.0001242.05.27%1810.34.05%motif file (matrix) svg
172 G T A C C G T A C G T A T A C G G C A T G T A C C G T A C A T G A G T C C G T A C G T A C G A T G C A T G C A T G A C T MafF(bZIP)/HepG2-MafF-ChIP-Seq(GSE31477)/Homer1e-4-1.030e+010.0001260.05.66%1965.04.39%motif file (matrix) svg
173 T C A G A T C G G A C T A C T G G A C T C A G T C T A G C G T A G T A C C G T A C T A G A T C G Tbx20(T-box)/Heart-Tbx20-ChIP-Seq(GSE29636)/Homer1e-4-1.024e+010.0001260.05.66%1967.24.40%motif file (matrix) svg
174 C A T G G T A C G A C T G C T A C G T A C G T A C G T A G C T A G A C T C T G A T C A G G T A C Mef2c(MADS)/GM12878-Mef2c-ChIP-Seq(GSE32465)/Homer1e-4-1.017e+010.0001383.08.34%3045.16.81%motif file (matrix) svg
175 A T G C T A G C A G C T A G C T T G A C G A C T T C A G T A C G G T C A C T G A A T C G T A G C G A C T C A G T A G T C A G C T T C G A A T C G T G C A T G C A HRE(HSF)/HepG2-HSF1-ChIP-Seq(GSE31477)/Homer1e-4-1.000e+010.0001185.04.03%1335.12.99%motif file (matrix) svg
176 C T G A A T G C G C T A C G A T A T G C C G T A C G T A C G T A C T A G T A C G Tcf3(HMG)/mES-Tcf3-ChIP-Seq(GSE11724)/Homer1e-4-9.844e+000.0001289.06.29%2231.64.99%motif file (matrix) svg
177 C T G A C A G T C T G A A G T C C T A G G A C T A T C G G T A C HIF-1b(HLH)/T47D-HIF1b-ChIP-Seq(GSE59937)/Homer1e-4-9.825e+000.0001981.021.35%8526.019.07%motif file (matrix) svg
178 A T G C G A T C C G T A A G C T C A G T A T C G G C A T A G C T G A C T A C T G Sox17(HMG)/Endoderm-Sox17-ChIP-Seq(GSE61475)/Homer1e-4-9.734e+000.0001727.015.82%6178.913.82%motif file (matrix) svg
179 C T A G T A C G G A T C G T C A T C G A A C G T T C A G C G T A C G T A C G T A Hoxd10(Homeobox)/ChickenMSG-Hoxd10.Flag-ChIP-Seq(GSE86088)/Homer1e-4-9.669e+000.00021066.023.20%9329.720.86%motif file (matrix) svg
180 C G T A G A C T C G A T A T C G G T A C G C A T C A T G C G T A T A C G G C A T G T A C C G T A C A T G A T G C G C T A C T A G G C A T G C A T G C A T G A C T MafB(bZIP)/BMM-Mafb-ChIP-Seq(GSE75722)/Homer1e-4-9.638e+000.0002415.09.03%3351.17.49%motif file (matrix) svg
181 A T G C A G T C A G C T A G C T A C G T A T C G C G T A C G A T T A G C G A C T LEF1(HMG)/H1-LEF1-ChIP-Seq(GSE64758)/Homer1e-4-9.625e+000.0002706.015.36%5991.213.40%motif file (matrix) svg
182 C T A G T A G C A G T C G T C A C T G A A C G T C G T A C G T A C G T A G C T A Hoxd13(Homeobox)/ChickenMSG-Hoxd13.Flag-ChIP-Seq(GSE86088)/Homer1e-4-9.580e+000.00021414.030.77%12615.028.21%motif file (matrix) svg
183 T C G A C A T G C A T G T A C G G T A C T C G A A G T C C A G T C T G A C G T A A G T C G A C T ZNF264(Zf)/HEK293-ZNF264.GFP-ChIP-Seq(GSE58341)/Homer1e-4-9.488e+000.0002644.014.02%5431.012.15%motif file (matrix) svg
184 C T A G C T A G C G T A C G T A T A C G C G A T C T A G C T G A C T G A C G T A T A C G G A C T PU.1:IRF8(ETS:IRF)/pDC-Irf8-ChIP-Seq(GSE66899)/Homer1e-4-9.329e+000.0002204.04.44%1514.63.39%motif file (matrix) svg
185 A G T C G A C T A C T G G A T C G T A C C G T A T G A C A G T C C G A T A G C T A C G T A C G T C T A G G A C T C T G A ZNF7(Zf)/HepG2-ZNF7.Flag-ChIP-Seq(Encode)/Homer1e-3-9.060e+000.0003565.012.30%4731.710.58%motif file (matrix) svg
186 C T A G T C G A C T G A C G T A T A C G G A C T T C A G T C G A G T C A T G C A T A C G A G C T IRF2(IRF)/Erythroblas-IRF2-ChIP-Seq(GSE36985)/Homer1e-3-8.990e+000.0003111.02.42%747.51.67%motif file (matrix) svg
187 C T G A T C A G C T G A C T A G C A T G A C G T A T G C C G T A A T G C G C A T T C A G C T G A A C T G A C G T C A G T A G T C C G T A C A G T C T A G C A T G VDR(NR),DR3/GM10855-VDR+vitD-ChIP-Seq(GSE22484)/Homer1e-3-8.981e+000.0003226.04.92%1713.03.83%motif file (matrix) svg
188 C T A G A C T G T G C A G T C A A T G C C G T A A T C G A T G C A G T C C T A G ZNF341(Zf)/EBV-ZNF341-ChIP-Seq(GSE113194)/Homer1e-3-8.842e+000.0003717.015.60%6136.613.72%motif file (matrix) svg
189 C T G A T C G A C G T A A T G C C G T A C G T A C G A T C T A G T C A G G A T C Sox15(HMG)/CPA-Sox15-ChIP-Seq(GSE62909)/Homer1e-3-8.491e+000.00051062.023.11%9371.420.96%motif file (matrix) svg
190 G A C T C G A T C A T G G C T A G C A T C G T A A G T C G C T A G C A T C G A T T A C G G C A T C G T A C A G T G A T C DMRT6(DM)/Testis-DMRT6-ChIP-Seq(GSE60440)/Homer1e-3-8.281e+000.0006224.04.87%1718.43.84%motif file (matrix) svg
191 C T G A C T A G A C T G G C A T A T G C C G T A C T G A C T A G A C T G A C G T A G T C C T G A RARg(NR)/ES-RARg-ChIP-Seq(GSE30538)/Homer1e-3-8.132e+000.000750.01.09%285.30.64%motif file (matrix) svg
192 T A G C G T A C A G T C G T A C C G A T A G T C A G T C A G T C A G T C A G T C C G T A G A T C Zfp281(Zf)/ES-Zfp281-ChIP-Seq(GSE81042)/Homer1e-3-8.015e+000.0007266.05.79%2094.64.68%motif file (matrix) svg
193 A G T C G A C T A G C T C G A T A T C G G C T A C G A T A T C G C G A T A C T G T A C G A C G T Tcf7(HMG)/GM12878-TCF7-ChIP-Seq(Encode)/Homer1e-3-7.987e+000.0008371.08.07%3028.96.77%motif file (matrix) svg
194 C T G A T G C A T A G C T G A C T A C G T C A G C T G A G C T A T C A G G A C T ELF1(ETS)/Jurkat-ELF1-ChIP-Seq(SRA014231)/Homer1e-3-7.983e+000.0008525.011.43%4422.09.89%motif file (matrix) svg
195 T C A G T G A C G T A C T G C A G T A C C T A G G T A C A T G C A G T C G T C A A G T C G A C T Klf9(Zf)/GBM-Klf9-ChIP-Seq(GSE62211)/Homer1e-3-7.977e+000.0008393.08.55%3226.17.21%motif file (matrix) svg
196 C T G A C G A T C T A G C G T A A G C T C G A T C A G T C T G A G A C T C T A G C T A G A T G C PBX2(Homeobox)/K562-PBX2-ChIP-Seq(Encode)/Homer1e-3-7.865e+000.0008764.016.63%6630.314.83%motif file (matrix) svg
197 G A T C C T G A A G T C C G A T C G A T G A T C A G T C A C T G A T C G A G C T Elk4(ETS)/Hela-Elk4-ChIP-Seq(GSE31477)/Homer1e-3-7.806e+000.0009550.011.97%4660.310.42%motif file (matrix) svg
198 A T C G T C G A G A C T A T C G T G A C A C G T C T A G A C T G C G T A A C T G A G T C G T A C ZNF415(Zf)/HEK293-ZNF415.GFP-ChIP-Seq(GSE58341)/Homer1e-3-7.789e+000.0009515.011.21%4342.09.71%motif file (matrix) svg
199 G C T A G A C T G A C T T G C A C G T A A G T C C G T A T A G C G A T C G A C T Eomes(T-box)/H9-Eomes-ChIP-Seq(GSE26097)/Homer1e-3-7.772e+000.00091892.041.18%17331.138.76%motif file (matrix) svg
200 T C A G T A C G G A T C G T A C T C G A C G A T C T G A C G T A G C T A C G T A Hoxd11(Homeobox)/ChickenMSG-Hoxd11.Flag-ChIP-Seq(GSE86088)/Homer1e-3-7.760e+000.00092033.044.24%18692.641.80%motif file (matrix) svg
201 T G C A T C G A T A G C G T A C T C A G C T A G G T C A G C T A T C A G G A C T ETS(ETS)/Promoter/Homer1e-3-7.730e+000.0009353.07.68%2878.76.44%motif file (matrix) svg
202 G T A C C T A G T C A G A G C T T A G C C G T A A T G C T A C G A G T C G T A C G T C A A G T C Srebp2(bHLH)/HepG2-Srebp2-ChIP-Seq(GSE31477)/Homer1e-3-7.695e+000.0010148.03.22%1084.32.42%motif file (matrix) svg
203 T A C G C T G A C A T G G A T C G T A C G C A T T C A G T A C G A G C T G T C A G A T C G C A T T A C G C G T A C T A G G A T C G A T C C G A T A C T G T C A G ZNF322(Zf)/HEK293-ZNF322.GFP-ChIP-Seq(GSE58341)/Homer1e-3-7.661e+000.0010301.06.55%2417.65.41%motif file (matrix) svg
204 T A C G T C A G G A T C G T A C T C G A G A C T G C T A G C T A G C T A C G T A G A T C G T C A CDX4(Homeobox)/ZebrafishEmbryos-Cdx4.Myc-ChIP-Seq(GSE48254)/Homer1e-3-7.646e+000.0010784.017.06%6830.215.27%motif file (matrix) svg
205 C G T A C T A G C A T G A G C T A C T G C G A T A T C G C G T A G T C A G T C A Tbet(T-box)/CD8-Tbet-ChIP-Seq(GSE33802)/Homer1e-3-7.618e+000.00101047.022.79%9293.920.78%motif file (matrix) svg
206 G T C A G C A T A C T G G T A C G A C T A C T G G C T A A T C G C A G T G T A C C G T A A G C T Nrf2(bZIP)/Lymphoblast-Nrf2-ChIP-Seq(GSE37589)/Homer1e-3-7.562e+000.001156.01.22%338.40.76%motif file (matrix) svg
207 T G C A C T G A C A T G C T A G C A G T A G T C C G T A A T G C A T G C T A C G G C A T T C A G G T C A G A T C G T A C ERE(NR),IR3/MCF7-ERa-ChIP-Seq(Unpublished)/Homer1e-3-7.537e+000.0011285.06.20%2280.45.10%motif file (matrix) svg
208 C A G T A C T G T C A G T G C A G C T A A T G C T C G A A T C G G T C A T G C A ZNF189(Zf)/HEK293-ZNF189.GFP-ChIP-Seq(GSE58341)/Homer1e-3-7.515e+000.0011811.017.65%7090.515.86%motif file (matrix) svg
209 C G T A C T G A C T A G C T G A A G T C G C T A C G A T A T C G G A C T G A T C A G T C C T G A C T A G C T A G A G T C G C T A C G A T C T A G G A T C G A T C p73(p53)/Trachea-p73-ChIP-Seq(PRJNA310161)/Homer1e-3-7.506e+000.001162.01.35%385.80.86%motif file (matrix) svg
210 A T G C G T A C C G T A A G C T G C A T T A C G A G C T A G C T A G T C A G C T Sox6(HMG)/Myotubes-Sox6-ChIP-Seq(GSE32627)/Homer1e-3-7.496e+000.00111529.033.28%13874.231.03%motif file (matrix) svg
211 T C G A C A G T A C T G A G C T C G T A C G T A A C G T A C G T C T G A T A G C Dlx3(Homeobox)/Kerainocytes-Dlx3-ChIP-Seq(GSE89884)/Homer1e-3-7.447e+000.0012608.013.23%5213.911.66%motif file (matrix) svg
212 A G T C A G T C C G A T A C G T A C G T A C T G A C G T A G C T A G T C A G T C Sox4(HMG)/proB-Sox4-ChIP-Seq(GSE50066)/Homer1e-3-7.446e+000.0012866.018.85%7608.417.01%motif file (matrix) svg
213 T C A G T A C G T A G C A C G T A C T G C G A T A G T C C G T A T A C G A G T C Meis1(Homeobox)/MastCells-Meis1-ChIP-Seq(GSE48085)/Homer1e-3-7.439e+000.00121699.036.97%15506.034.68%motif file (matrix) svg
214 T A C G T C G A C A G T A C T G G C T A A T G C C G A T G T A C C G T A A C T G T A G C C G T A NF-E2(bZIP)/K562-NFE2-ChIP-Seq(GSE31477)/Homer1e-3-7.334e+000.001366.01.44%419.10.94%motif file (matrix) svg
215 C G T A C T G A A C T G A G C T A G T C G A T C G A T C G C A T C T G A C T A G C T A G T A C G T C G A T G C A G C A T EBF2(EBF)/BrownAdipose-EBF2-ChIP-Seq(GSE97114)/Homer1e-3-7.319e+000.0013900.019.59%7935.317.75%motif file (matrix) svg
216 C G A T C T A G T C G A A G C T C G A T C T G A C G T A A G C T A C T G C T A G A T G C G A T C Hoxb4(Homeobox)/ES-Hoxb4-ChIP-Seq(GSE34014)/Homer1e-3-7.311e+000.0013196.04.27%1507.73.37%motif file (matrix) svg
217 C G T A C G T A C G T A G C A T G C A T T A C G G T A C G A C T A C T G C G T A A T C G A C G T G T A C C G T A A G C T Bach1(bZIP)/K562-Bach1-ChIP-Seq(GSE31477)/Homer1e-3-7.284e+000.001465.01.41%412.50.92%motif file (matrix) svg
218 C T G A A T C G A G C T A G C T A C G T T A G C C T G A T A C G C G A T A C G T G A C T A G T C ISRE(IRF)/ThioMac-LPS-Expression(GSE23622)/Homer1e-3-7.252e+000.001469.01.50%444.20.99%motif file (matrix) svg
219 A C T G G A C T A G T C C T G A G A T C T C A G A T G C G A C T A G T C A T G C T A G C A G C T A T C G T G C A PAX5(Paired,Homeobox),condensed/GM12878-PAX5-ChIP-Seq(GSE32465)/Homer1e-3-7.185e+000.0015126.02.74%912.92.04%motif file (matrix) svg
220 C T A G T A C G G A C T T G C A T G C A C G A T T A C G C T G A T C G A C T G A Hoxa10(Homeobox)/ChickenMSG-Hoxa10.Flag-ChIP-Seq(GSE86088)/Homer1e-3-7.068e+000.0017552.012.01%4720.510.56%motif file (matrix) svg
221 T C G A T C G A T A G C G T A C T C A G T A C G C G T A C G T A T C A G A G C T GABPA(ETS)/Jurkat-GABPa-ChIP-Seq(GSE17954)/Homer1e-3-7.056e+000.0017908.019.76%8029.917.96%motif file (matrix) svg
222 A T C G A G C T C T G A C T A G A C T G A C G T G T A C G C T A A T G C A C G T C T A G C A T G T A C G C G A T A T G C C G T A Reverb(NR),DR2/RAW-Reverba.biotin-ChIP-Seq(GSE45914)/Homer1e-3-7.013e+000.0017148.03.22%1103.62.47%motif file (matrix) svg
223 A T G C A G C T T C A G T G A C T C A G A T G C T G C A A C G T A T C G G A T C A C T G A G T C NRF1(NRF)/MCF7-NRF1-ChIP-Seq(Unpublished)/Homer1e-2-6.900e+000.0019125.02.72%912.02.04%motif file (matrix) svg
224 C G A T A C G T A C G T A C G T C G T A A G C T C A G T C T A G A T C G A C T G HOXB13(Homeobox)/ProstateTumor-HOXB13-ChIP-Seq(GSE56288)/Homer1e-2-6.844e+000.0020937.020.39%8317.218.60%motif file (matrix) svg
225 T C A G A G C T A T G C C G T A A G T C T C A G A C G T A T C G T C G A A G T C G A T C T G A C TFE3(bHLH)/MEF-TFE3-ChIP-Seq(GSE75757)/Homer1e-2-6.841e+000.002098.02.13%687.91.54%motif file (matrix) svg
226 T A C G G A C T T G A C C G T A A C G T G A T C G T C A C G T A A C G T A T G C C G T A G A C T HOXA2(Homeobox)/mES-Hoxa2-ChIP-Seq(Donaldson_et_al.)/Homer1e-2-6.826e+000.0021107.02.33%762.81.71%motif file (matrix) svg
227 C A T G A G T C A G C T C G T A C G A T C G A T G C A T G C A T C G A T C T G A C A T G T G A C Mef2d(MADS)/Retina-Mef2d-ChIP-Seq(GSE61391)/Homer1e-2-6.751e+000.0022170.03.70%1300.02.91%motif file (matrix) svg
228 C T G A A G T C C G A T A G C T A T G C G T A C A C G T A T C G C A G T G C A T Elf4(ETS)/BMDM-Elf4-ChIP-Seq(GSE88699)/Homer1e-2-6.733e+000.00221009.021.96%9003.120.13%motif file (matrix) svg
229 G A C T A G C T G T A C G A C T C T G A A C T G G T C A C T G A A T G C T A C G G A C T A C G T A G T C G A C T C T G A HRE(HSF)/Striatum-HSF1-ChIP-Seq(GSE38000)/Homer1e-2-6.468e+000.0029238.05.18%1906.54.26%motif file (matrix) svg
230 T C G A C T G A T A G C T G A C T C A G T C A G C G T A C G T A T C A G A G C T ETV1(ETS)/GIST48-ETV1-ChIP-Seq(GSE22441)/Homer1e-2-6.467e+000.00291317.028.66%11943.326.71%motif file (matrix) svg
231 T C G A T A G C T G C A A C T G A C T G C G T A C G T A C T A G G A C T T A C G ETS1(ETS)/Jurkat-ETS1-ChIP-Seq(GSE17954)/Homer1e-2-6.407e+000.00311018.022.15%9114.220.38%motif file (matrix) svg
232 C G T A C A T G A G T C G A C T T G C A C G T A A C G T A C G T C T G A T C A G Lhx1(Homeobox)/EmbryoCarcinoma-Lhx1-ChIP-Seq(GSE70957)/Homer1e-2-6.319e+000.0033976.021.24%8725.219.51%motif file (matrix) svg
233 T C A G A G C T G T A C C G T A A C G T C G T A C G T A C G T A G C T A G A C T Cdx2(Homeobox)/mES-Cdx2-ChIP-Seq(GSE14586)/Homer1e-2-6.311e+000.0033602.013.10%5228.311.69%motif file (matrix) svg
234 T A G C A G T C T G A C A G T C C T A G A T C G A G T C C A T G T G A C A G T C G T A C A G T C A G T C G C A T C T A G A T C G G C A T A C T G A T C G G A T C BORIS(Zf)/K562-CTCFL-ChIP-Seq(GSE32465)/Homer1e-2-6.242e+000.0036176.03.83%1369.93.06%motif file (matrix) svg
235 T G C A C T A G C T A G C T G A C A T G A C T G T G C A G A T C G T C A T G C A G T C A G T C A A G C T C T A G G C A T ZNF675(Zf)/HEK293-ZNF675.GFP-ChIP-Seq(GSE58341)/Homer1e-2-6.186e+000.0037145.03.16%1102.32.46%motif file (matrix) svg
236 T C G A T G A C G C A T A G C T C A G T G A T C G C T A G A T C G A C T A C G T G C A T A G T C PRDM1(Zf)/Hela-PRDM1-ChIP-Seq(GSE31477)/Homer1e-2-6.151e+000.0039509.011.08%4379.39.79%motif file (matrix) svg
237 T A G C G C T A T C G A C T G A A G T C A G T C C T G A A G T C C G T A C T A G RUNX(Runt)/HPC7-Runx1-ChIP-Seq(GSE22178)/Homer1e-2-6.004e+000.0045726.015.80%6403.314.32%motif file (matrix) svg
238 T C A G T C A G G C T A C G T A T A C G G A C T T C A G T C G A C T G A C G T A T A C G G A C T IRF8(IRF)/BMDM-IRF8-ChIP-Seq(GSE77884)/Homer1e-2-5.995e+000.0045308.06.70%2553.95.71%motif file (matrix) svg
239 C T G A C T G A T A G C G A T C G C T A G T A C A C G T G A T C T G C A C G T A Nkx2.5(Homeobox)/HL1-Nkx2.5.biotin-ChIP-Seq(GSE21529)/Homer1e-2-5.967e+000.00461985.043.20%18402.041.15%motif file (matrix) svg
240 C G T A C T A G T C A G T C A G A G T C A T G C A G T C G C A T A G C T A C G T A T C G C G A T Sox9(HMG)/Limb-SOX9-ChIP-Seq(GSE73225)/Homer1e-2-5.931e+000.0047901.019.61%8050.818.00%motif file (matrix) svg
241 A C G T T C G A T C G A A G T C G T C A T A C G A T G C A C G T A C T G A G C T Myf5(bHLH)/GM-Myf5-ChIP-Seq(GSE24852)/Homer1e-2-5.875e+000.0050716.015.58%6319.014.13%motif file (matrix) svg
242 C T A G A C G T A G T C C G T A A C T G A G T C G C A T A C T G G C A T A G T C G A C T G A T C G C A T A G T C A G C T ZNF317(Zf)/HEK293-ZNF317.GFP-ChIP-Seq(GSE58341)/Homer1e-2-5.788e+000.005499.02.15%721.41.61%motif file (matrix) svg
243 C A G T G A C T C G T A G C T A G T A C G A T C G T A C G A C T A G C T A C G T T G A C C G T A C A G T A C G T A T G C ZNF652/HepG2-ZNF652.Flag-ChIP-Seq(Encode)/Homer1e-2-5.701e+000.0059254.05.53%2081.54.65%motif file (matrix) svg
244 C T G A C T G A C T A G T C G A C G T A A T G C C G T A A C T G C G T A A C G T C T G A C G A T A G C T C G T A A C G T A G T C C G A T T A C G G T C A G C A T GATA(Zf),IR3/iTreg-Gata3-ChIP-Seq(GSE20898)/Homer1e-2-5.520e+000.0070140.03.05%1080.82.42%motif file (matrix) svg
245 T G A C C T A G A C T G T A G C C G A T A C T G A T G C C A T G A T C G A T C G A T C G T A G C C T G A T A G C G C T A A C T G C G T A A G C T C G T A C T G A GATA:SCL(Zf,bHLH)/Ter119-SCL-ChIP-Seq(GSE18720)/Homer1e-2-5.515e+000.0070121.02.63%916.12.05%motif file (matrix) svg
246 A C T G A G T C G T C A C G T A A G T C C G T A C T A G C T A G G A C T C A T G SCRT1(Zf)/HEK293-SCRT1.eGFP-ChIP-Seq(Encode)/Homer1e-2-5.506e+000.0071320.06.96%2686.86.01%motif file (matrix) svg
247 T A G C G C A T A G T C G A T C A T G C G A C T C T A G A C T G A C T G C T G A A C T G C T A G A G T C T G A C C G A T GLIS3(Zf)/Thyroid-Glis3.GFP-ChIP-Seq(GSE103297)/Homer1e-2-5.499e+000.00711233.026.83%11233.425.12%motif file (matrix) svg
248 C T G A T C A G A G T C C G T A A T C G A T G C C G A T A C T G A G T C G A C T A T C G A G T C MyoD(bHLH)/Myotube-MyoD-ChIP-Seq(GSE21614)/Homer1e-2-5.429e+000.0076775.016.87%6906.215.44%motif file (matrix) svg
249 G A C T C A G T G A T C G A T C A C G T G A T C C T G A T A C G C G T A G T C A STAT6(Stat)/Macrophage-Stat6-ChIP-Seq(GSE38377)/Homer1e-2-5.323e+000.0084539.011.73%4711.010.54%motif file (matrix) svg
250 C T A G G T A C A G T C T G C A A G T C C T G A A G T C A G T C A G T C G C T A Klf4(Zf)/mES-Klf4-ChIP-Seq(GSE11431)/Homer1e-2-5.310e+000.0085360.07.83%3061.56.85%motif file (matrix) svg
251 T C A G A C G T T C G A T A G C A G T C C G T A A C T G G T A C A C G T A C T G A T C G A G T C Atoh1(bHLH)/Cerebellum-Atoh1-ChIP-Seq(GSE22111)/Homer1e-2-5.298e+000.00851075.023.39%9750.521.80%motif file (matrix) svg
252 T A G C T C A G C A T G G C A T A G C T C G A T A T G C C G T A C G T A G T C A CHR(?)/Hela-CellCycle-Expression/Homer1e-2-5.291e+000.0086614.013.36%5410.912.10%motif file (matrix) svg
253 T A C G T A C G G T A C A T C G T A C G T A C G G T C A C T G A C G T A G A C T E2F4(E2F)/K562-E2F4-ChIP-Seq(GSE31477)/Homer1e-2-5.244e+000.0089312.06.79%2627.85.88%motif file (matrix) svg
254 A G T C C T G A A G T C C G A T C A G T G A T C A T G C A C T G A T C G G A C T Fli1(ETS)/CD8-FLI-ChIP-Seq(GSE20898)/Homer1e-2-5.241e+000.00891064.023.16%9651.021.58%motif file (matrix) svg
255 T C G A A G T C C G T A A T C G A T G C C G A T A C T G A G T C A G C T A C T G Tcf12(bHLH)/GM12878-Tcf12-ChIP-Seq(GSE32465)/Homer1e-2-5.237e+000.0089949.020.65%8562.819.15%motif file (matrix) svg
256 G C T A C T G A T C G A A G T C A G T C C T G A A G T C G T C A C T G A T G C A RUNX1(Runt)/Jurkat-RUNX1-ChIP-Seq(GSE29180)/Homer1e-2-5.198e+000.00921010.021.98%9144.020.45%motif file (matrix) svg
257 T C A G C T G A C G T A C G T A T A C G G C A T C T A G C T G A C G T A C G T A T A C G G A C T IRF1(IRF)/PBMC-IRF1-ChIP-Seq(GSE43036)/Homer1e-2-5.180e+000.0094138.03.00%1074.92.40%motif file (matrix) svg
258 C T G A C T G A C A T G A T C G A G C T A T C G G A C T C A T G C T G A G T C A Tbr1(T-box)/Cortex-Tbr1-ChIP-Seq(GSE71384)/Homer1e-2-5.134e+000.00981267.027.57%11593.125.93%motif file (matrix) svg
259 C G A T C T A G C G T A G A C T C A G T C T A G C G T A A G C T C A T G C T A G HOXA1(Homeobox)/mES-Hoxa1-ChIP-Seq(SRP084292)/Homer1e-2-5.116e+000.0099240.05.22%1982.04.43%motif file (matrix) svg
260 A G T C G A T C A G T C C G T A A T C G C A G T A G T C G T A C C T G A A C T G T C A G A G C T A G C T A G C T A G C T PRDM15(Zf)/ESC-Prdm15-ChIP-Seq(GSE73694)/Homer1e-2-5.075e+000.0103967.021.04%8747.819.56%motif file (matrix) svg
261 A T C G A G C T A C T G A G T C A C T G A G T C C G T A A C G T A C T G A G T C A C T G A G T C NRF(NRF)/Promoter/Homer1e-2-5.030e+000.0107168.03.66%1343.53.00%motif file (matrix) svg
262 A T G C G A T C C G A T A C G T A C G T A C T G C A G T A G C T Sox3(HMG)/NPC-Sox3-ChIP-Seq(GSE33059)/Homer1e-2-5.028e+000.01071644.035.78%15216.434.03%motif file (matrix) svg
263 T G C A T A G C G A C T T G C A T G A C T G C A C G T A A G C T A G C T A G T C A G T C G T A C GFY(?)/Promoter/Homer1e-2-4.958e+000.011474.01.61%532.51.19%motif file (matrix) svg
264 G T C A T G C A G C T A A G T C C G T A A C T G T G A C G C A T T C A G C A G T Ap4(bHLH)/AML-Tfap4-ChIP-Seq(GSE45738)/Homer1e-2-4.951e+000.01151176.025.59%10744.924.03%motif file (matrix) svg
265 G T A C G A C T C G T A C T G A T C G A C G T A G C T A C A G T C T G A T A C G Mef2a(MADS)/HL1-Mef2a.biotin-ChIP-Seq(GSE21529)/Homer1e-2-4.920e+000.0118357.07.77%3056.26.83%motif file (matrix) svg
266 T G A C A T G C C G T A A T C G A T G C C A G T C A T G A C T G A G T C G T A C HEB(bHLH)/mES-Heb-ChIP-Seq(GSE53233)/Homer1e-2-4.911e+000.01181716.037.34%15922.135.61%motif file (matrix) svg
267 T G A C T C G A C T G A C T G A A T G C G A T C C T A G T A C G G A C T G A C T G A T C T C G A C T G A C T G A A T G C G A T C C T A G A T C G G A C T G A C T Tcfcp2l1(CP2)/mES-Tcfcp2l1-ChIP-Seq(GSE11431)/Homer1e-2-4.874e+000.0123162.03.53%1296.02.90%motif file (matrix) svg
268 A C T G C G T A A C T G A T G C T G A C G A T C A T C G T G C A A C T G A G T C ZNF519(Zf)/HEK293-ZNF519.GFP-ChIP-Seq(GSE58341)/Homer1e-2-4.820e+000.0129191.04.16%1556.23.48%motif file (matrix) svg
269 C A T G A C G T A G T C G A T C G A T C G A T C G C T A C T A G C T A G C T A G T C A G T C G A EBF1(EBF)/Near-E2A-ChIP-Seq(GSE21512)/Homer1e-2-4.795e+000.0132954.020.76%8650.919.35%motif file (matrix) svg
270 T G C A C T G A A T G C G T C A A C G T A T G C A C G T A C T G A C T G T G C A ZBTB18(Zf)/HEK293-ZBTB18.GFP-ChIP-Seq(GSE58341)/Homer1e-2-4.760e+000.0136526.011.45%4629.210.35%motif file (matrix) svg
271 G T A C G A T C C A G T A G T C A G T C A G T C T G C A G A T C C T G A A T G C G T C A A C G T WT1(Zf)/Kidney-WT1-ChIP-Seq(GSE90016)/Homer1e-2-4.750e+000.0137530.011.53%4667.710.44%motif file (matrix) svg
272 C G T A A T G C C G A T A C G T A G T C C G T A C G T A C G T A C T A G A T C G TCFL2(HMG)/K562-TCF7L2-ChIP-Seq(GSE29196)/Homer1e-2-4.750e+000.013794.02.05%707.61.58%motif file (matrix) svg
273 T C G A A G C T A C G T A C G T A G T C A G T C A C G T A T C G G A C T A T C G EWS:ERG-fusion(ETS)/CADO_ES1-EWS:ERG-ChIP-Seq(SRA014231)/Homer1e-2-4.662e+000.0148711.015.47%6368.314.24%motif file (matrix) svg
274 T C G A T C G A A G T C C G T A C T A G T A G C A C G T A C T G MyoG(bHLH)/C2C12-MyoG-ChIP-Seq(GSE36024)/Homer1e-2-4.632e+000.01521042.022.68%9501.221.25%motif file (matrix) svg 
[46]:

homer_results(homer_dict, 'Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K', results='denovo')

[46]:
/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K/ - Homer de novo Motif Results

Homer de novo Motif Results (/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K/)

Known Motif Enrichment Results
Gene Ontology Enrichment Results
If Homer is having trouble matching a motif to a known motif, try copy/pasting the matrix file into STAMP
More information on motif finding results: HOMER | Description of Results | Tips
Total target sequences = 4595
Total background sequences = 44741
* - possible false positive
RankMotifP-valuelog P-pvalue% of Targets% of Background STD(Bg STD) Best Match/DetailsMotif File
1 T C G A A G C T C A G T C T A G G A T C T C A G G T A C G T C A C T G A A G C T G T A C T A C G 1e-1721-3.963e+0362.81%9.40%55.1bp (152.9bp)NFIL3(bZIP)/HepG2-NFIL3-ChIP-Seq(Encode)/Homer(0.918)
More Information | Similar Motifs Found		motif file (matrix)
2 A C G T A G C T A C T G A G T C C T G A C G T A A C G T G T A C 1e-196-4.516e+0222.00%7.81%123.0bp (154.6bp)Ddit3::Cebpa/MA0019.1/Jaspar(0.770)
More Information | Similar Motifs Found		motif file (matrix)
3 C T A G C A T G A T G C T G A C G T C A T C G A C T G A C A T G C A T G A G C T A G T C G T C A 1e-133-3.076e+0238.02%21.94%116.4bp (147.3bp)PPARa(NR),DR1/Liver-Ppara-ChIP-Seq(GSE47954)/Homer(0.938)
More Information | Similar Motifs Found		motif file (matrix)
4 A C G T C T A G A C G T A C G T C A G T C T G A A T G C G A T C G C T A C G T A 1e-85-1.964e+0238.48%25.29%129.6bp (152.7bp)FOXM1(Forkhead)/MCF7-FOXM1-ChIP-Seq(GSE72977)/Homer(0.922)
More Information | Similar Motifs Found		motif file (matrix)
5 T A G C C T G A A G C T A C G T C A T G T A C G G T A C G A T C 1e-50-1.152e+0249.84%38.95%131.9bp (150.3bp)NFY(CCAAT)/Promoter/Homer(0.860)
More Information | Similar Motifs Found		motif file (matrix)
6 C T G A C T A G C A T G C G A T T A G C G T C A T C G A C T G A A C T G A G C T A T G C G A C T 1e-48-1.121e+0227.73%18.78%135.3bp (146.2bp)Hnf4a/MA0114.3/Jaspar(0.793)
More Information | Similar Motifs Found		motif file (matrix)
7 A C T G A G T C G T A C G T C A A C T G T A G C A T C G C G A T 1e-46-1.080e+0246.79%36.36%134.5bp (150.6bp)HIC1(Zf)/Treg-ZBTB29-ChIP-Seq(GSE99889)/Homer(0.760)
More Information | Similar Motifs Found		motif file (matrix)
8 C T G A G T C A C G A T T A G C C G T A C A G T A C G T C G T A T C G A A G T C 1e-46-1.061e+0213.56%7.43%128.4bp (152.5bp)HNF1b(Homeobox)/PDAC-HNF1B-ChIP-Seq(GSE64557)/Homer(0.893)
More Information | Similar Motifs Found		motif file (matrix)
9 C T A G C T G A G C A T C G A T C A T G T A G C G T C A C A G T G A T C C G T A C T A G A T G C 1e-36-8.461e+013.29%0.95%128.6bp (150.7bp)Atf4(bZIP)/MEF-Atf4-ChIP-Seq(GSE35681)/Homer(0.838)
More Information | Similar Motifs Found		motif file (matrix)
10 C T G A G T C A A G C T G T C A C G A T A C G T A C G T C T A G 1e-30-7.016e+0114.67%9.33%141.9bp (154.7bp)Arid5a/MA0602.1/Jaspar(0.788)
More Information | Similar Motifs Found		motif file (matrix)
11 T G C A A T C G C T A G G A C T A T G C C G T A A T G C A T C G A G C T A T C G 1e-28-6.528e+019.95%5.75%124.4bp (150.8bp)USF2/MA0526.2/Jaspar(0.906)
More Information | Similar Motifs Found		motif file (matrix)
12 C G T A C G A T A G C T G T A C A G C T C T A G T C A G C T A G G T C A G T C A 1e-24-5.561e+0110.71%6.63%135.6bp (145.7bp)Stat5a::Stat5b/MA0519.1/Jaspar(0.932)
More Information | Similar Motifs Found		motif file (matrix)
13 A T G C G C A T A T C G C T A G A G T C T G A C A G C T A T C G A C G T A C G T 1e-22-5.213e+014.87%2.35%131.7bp (147.1bp)BMYB(HTH)/Hela-BMYB-ChIP-Seq(GSE27030)/Homer(0.629)
More Information | Similar Motifs Found		motif file (matrix)
14 A C T G C G T A A C T G C G T A A C T G A C G T A C G T C G A T C G T A A G T C A C G T A G C T 1e-18-4.252e+0160.41%53.91%146.0bp (147.8bp)FOXA1(Forkhead)/LNCAP-FOXA1-ChIP-Seq(GSE27824)/Homer(0.652)
More Information | Similar Motifs Found		motif file (matrix)
15 C T G A A C G T A C G T C G A T A G T C C T A G A T G C A G T C 1e-16-3.718e+0116.69%12.48%134.3bp (151.3bp)IRF4(IRF)/GM12878-IRF4-ChIP-Seq(GSE32465)/Homer(0.689)
More Information | Similar Motifs Found		motif file (matrix)
16 C G T A A C G T A G C T A C T G C T G A A C T G C G T A T G A C G T C A T A C G G T C A A C T G 1e-15-3.603e+010.50%0.05%115.6bp (123.1bp)Hnf6b(Homeobox)/LNCaP-Hnf6b-ChIP-Seq(GSE106305)/Homer(0.616)
More Information | Similar Motifs Found		motif file (matrix)
17 A G T C A G T C T A C G A G C T G T C A C G T A A G T C C G T A A G T C A C G T A C G T A G T C 1e-14-3.451e+010.28%0.01%141.8bp (77.8bp)Tbet(T-box)/CD8-Tbet-ChIP-Seq(GSE33802)/Homer(0.681)
More Information | Similar Motifs Found		motif file (matrix)
18 A G C T A C G T T G C A A G T C A C T G A C G T A G T C C G T A G T C A T G C A 1e-12-2.929e+011.33%0.44%149.2bp (150.5bp)Atf1/MA0604.1/Jaspar(0.842)
More Information | Similar Motifs Found		motif file (matrix)
19 * A C G T A C T G C G T A G T C A C T G A A G T C A C T G A C G T A T G C A C G T 1e-10-2.488e+010.48%0.08%135.2bp (154.5bp)Creb3l2/MA0608.1/Jaspar(0.689)
More Information | Similar Motifs Found		motif file (matrix)
20 * A C G T C G T A C G T A A C T G C G T A A C T G A C G T A C G T A C G T A G T C G T C A A C T G 1e-10-2.455e+010.15%0.00%149.3bp (10.0bp)Barx1(Homeobox)/Stomach-Barx1.3xFlag-ChIP-Seq(GSE69483)/Homer(0.643)
More Information | Similar Motifs Found		motif file (matrix)
21 * C G T A A C G T A C T G A C T G A G T C A C T G G T A C A C G T A C G T C G T A 1e-9-2.109e+010.35%0.05%130.0bp (168.5bp)YY2/MA0748.1/Jaspar(0.741)
More Information | Similar Motifs Found		motif file (matrix)
22 * A G T C A G T C A C G T A C T G A G T C A G T C C G T A A C T G A G T C A C G T A C T G A C G T 1e-8-1.978e+010.15%0.01%135.1bp (83.5bp)Tcf21(bHLH)/ArterySmoothMuscle-Tcf21-ChIP-Seq(GSE61369)/Homer(0.774)
More Information | Similar Motifs Found		motif file (matrix)
23 * A C G T A C G T C G T A C G T A C G T A A C T G A C G T C G T A A G T C C G T A 1e-6-1.520e+010.39%0.09%145.0bp (161.8bp)PB0134.1_Hnf4a_2/Jaspar(0.701)
More Information | Similar Motifs Found		motif file (matrix)
24 * A C G T A G T C C G T A A C T G A G T C A C G T A G T C A C T G C G T A A G T C 1e-6-1.391e+010.13%0.01%138.0bp (93.0bp)Ap4(bHLH)/AML-Tfap4-ChIP-Seq(GSE45738)/Homer(0.602)
More Information | Similar Motifs Found		motif file (matrix)
25 * A T C G A G T C C G T A A T G C A G T C C G T A C G T A A G C T A G C T C G T A A G T C C G T A 1e-6-1.388e+010.17%0.02%136.8bp (104.8bp)Nobox/MA0125.1/Jaspar(0.705)
More Information | Similar Motifs Found		motif file (matrix)
26 * A C T G C G T A A C T G A C G T A G T C A G T C A G T C A G T C A C T G A C G T 1e-4-1.001e+010.13%0.01%121.9bp (131.2bp)MZF1/MA0056.1/Jaspar(0.700)
More Information | Similar Motifs Found		motif file (matrix)
27 * A C G T C G T A C G T A A G T C A G T C A C T G A G T C A C G T A C T G A G T C A G T C G T A C 1e-4-9.576e+000.11%0.01%152.2bp (71.8bp)RXR(NR),DR1/3T3L1-RXR-ChIP-Seq(GSE13511)/Homer(0.659)
More Information | Similar Motifs Found		motif file (matrix)

You can also access the regions enriched for each motif (use known_motif_hits for known motifs; and denovo_motif_hits for de novo motifs):

[47]:

homer_dict['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'].known_motif_hits['CEBP(bZIP)/ThioMac-CEBPb-ChIP-Seq(GSE21512)/Homer'][0:10]

[47]:

['chr10:89748570-89749071',
 'chr10:111335980-111336481',
 'chr4:45495781-45496282',
 'chr19:30170213-30170714',
 'chr10:121129224-121129725',
 'chr2:103492434-103492935',
 'chr2:26600492-26600993',
 'chr4:145280844-145281345',
 'chr13:81329746-81330247',
 'chr13:96742830-96743331']

To access cistromes (use known_cistromes for cistromes based on known motifs; and denovo_cistromes for cistromes based on de novo motifs):

[52]:

homer_dict['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'].denovo_cistromes['Cebpa_(2886r)'][0:10]

[52]:

['chr10:89748570-89749071',
 'chr10:111335980-111336481',
 'chr8:70544122-70544623',
 'chr19:30170213-30170714',
 'chr10:121129224-121129725',
 'chr2:103492434-103492935',
 'chr2:26600492-26600993',
 'chr4:145280844-145281345',
 'chr1:193289929-193290430',
 'chr13:81329746-81330247']

You can easily export cistromes to a bed file:

[53]:

from pycistarget.utils import *
cebpa_cistrome_pr = pr.PyRanges(region_names_to_coordinates(homer_dict['Cebpa_ERR235722_summits_order_by_score_extended_250bp_top5K'].denovo_cistromes['Cebpa_(2886r)']))
cebpa_cistrome_pr.to_bed(path='/staging/leuven/stg_00002/lcb/cbravo/Multiomics_pipeline/pycistarget_tutorial/Homer/cebpa_cistrome_example.bed')