Direct links to results

TF-MoDISco results

Summary of motifs

TOMTOM matches to motifs

import os
import sys
sys.path.append(os.path.abspath("/users/amtseng/tfmodisco/src/"))
from tfmodisco.run_tfmodisco import import_shap_scores, import_tfmodisco_results
from motif.read_motifs import pfm_info_content, pfm_to_pwm, trim_motif_by_ic
from motif.match_motifs import match_motifs_to_database
from util import figure_to_vdom_image
import plot.viz_sequence as viz_sequence
import numpy as np
import h5py
import matplotlib.pyplot as plt
import vdom.helpers as vdomh
from IPython.display import display

Define constants and paths

# Define parameters/fetch arguments
tf_name = os.environ["TFM_TF_NAME"]
shap_scores_path = os.environ["TFM_SHAP_PATH"]
tfm_results_path = os.environ["TFM_TFM_PATH"]
hyp_score_key = os.environ["TFM_HYP_SCORE_KEY"]
if "TFM_MOTIF_CACHE" in os.environ:
    tfm_motifs_cache_dir = os.environ["TFM_MOTIF_CACHE"]
else:
    tfm_motifs_cache_dir = None

print("TF name: %s" % tf_name)
print("DeepSHAP scores path: %s" % shap_scores_path)
print("TF-MoDISco results path: %s" % tfm_results_path)
print("Importance score key: %s" % hyp_score_key)
print("Saved TF-MoDISco-derived motifs cache: %s" % tfm_motifs_cache_dir)

TF name: GABPA
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/GABPA_multitask_profile_fold7/GABPA_multitask_profile_fold7_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/GABPA_multitask_profile_fold7/GABPA_multitask_profile_fold7_profile_tfm.h5
Importance score key: profile_hyp_scores
Saved TF-MoDISco-derived motifs cache: /users/amtseng/tfmodisco/results/reports/tfmodisco_results//cache/multitask_profile/GABPA_multitask_profile_fold7/GABPA_multitask_profile_fold7_profile

# Define paths and constants
input_length = 2114
shap_score_center_size = 400

if tfm_motifs_cache_dir:
    os.makedirs(tfm_motifs_cache_dir, exist_ok=True)

Import SHAP scores and TF-MoDISco results

# Import SHAP coordinates and one-hot sequences
hyp_scores, _, one_hot_seqs, shap_coords = import_shap_scores(shap_scores_path, hyp_score_key, center_cut_size=shap_score_center_size)
# This cuts the sequences/scores off just as how TF-MoDISco saw them, but the coordinates are uncut

Importing SHAP scores: 100%|██████████| 104/104 [02:04<00:00,  1.20s/it]

# Import the TF-MoDISco results object
tfm_obj = import_tfmodisco_results(tfm_results_path, hyp_scores, one_hot_seqs, shap_score_center_size)

Plot some SHAP score tracks

Plot the central region of some randomly selected actual importance scores

plot_slice = slice(int(shap_score_center_size / 4), int(3 * shap_score_center_size / 4))
for index in np.random.choice(hyp_scores.shape[0], size=5, replace=False):
    viz_sequence.plot_weights((hyp_scores[index] * one_hot_seqs[index])[plot_slice], subticks_frequency=100)

Plot TF-MoDISco results

Plot all motifs by metacluster

motif_pfms, motif_hcwms, motif_cwms = [], [], []  # Save the trimmed PFMs, hCWMs, and CWMs
motif_pfms_short = []  # PFMs that are even more trimmed (for TOMTOM)
num_seqlets = []  # Number of seqlets for each motif
motif_seqlets = []  # Save seqlets of each motif
metaclusters = tfm_obj.metacluster_idx_to_submetacluster_results
num_metaclusters = len(metaclusters.keys())
if tfm_motifs_cache_dir:
    motif_hdf5 = h5py.File(os.path.join(tfm_motifs_cache_dir, "all_motifs.h5"), "w")
for metacluster_i, metacluster_key in enumerate(metaclusters.keys()):
    metacluster = metaclusters[metacluster_key]
    display(vdomh.h3("Metacluster %d/%d" % (metacluster_i + 1, num_metaclusters)))
    patterns = metacluster.seqlets_to_patterns_result.patterns
    if not patterns:
        break
    motif_pfms.append([])
    motif_hcwms.append([])
    motif_cwms.append([])
    motif_pfms_short.append([])
    num_seqlets.append([])
    motif_seqlets.append([])
    num_patterns = len(patterns)
    for pattern_i, pattern in enumerate(patterns):
        seqlets = pattern.seqlets
        display(vdomh.h4("Pattern %d/%d" % (pattern_i + 1, num_patterns)))
        display(vdomh.p("%d seqlets" % len(seqlets)))
        
        pfm = pattern["sequence"].fwd
        hcwm = pattern["task0_hypothetical_contribs"].fwd
        cwm = pattern["task0_contrib_scores"].fwd
        
        pfm_fig = viz_sequence.plot_weights(pfm, subticks_frequency=10, return_fig=True)
        hcwm_fig = viz_sequence.plot_weights(hcwm, subticks_frequency=10, return_fig=True)
        cwm_fig = viz_sequence.plot_weights(cwm, subticks_frequency=10, return_fig=True)
        pfm_fig.tight_layout()
        hcwm_fig.tight_layout()
        cwm_fig.tight_layout()
        
        motif_table = vdomh.table(
            vdomh.tr(
                vdomh.td("Sequence (PFM)"),
                vdomh.td(figure_to_vdom_image(pfm_fig))
            ),
            vdomh.tr(
                vdomh.td("Hypothetical contributions (hCWM)"),
                vdomh.td(figure_to_vdom_image(hcwm_fig))
            ),
            vdomh.tr(
                vdomh.td("Actual contributions (CWM)"),
                vdomh.td(figure_to_vdom_image(cwm_fig))
            )
        )
        display(motif_table)
        plt.close("all")  # Remove all standing figures
        
        # Trim motif based on information content
        short_trimmed_pfm = trim_motif_by_ic(pfm, pfm)
        motif_pfms_short[-1].append(short_trimmed_pfm)
        
        # Expand trimming to +/- 4bp on either side
        trimmed_pfm = trim_motif_by_ic(pfm, pfm, pad=4)
        trimmed_hcwm = trim_motif_by_ic(pfm, hcwm, pad=4)
        trimmed_cwm = trim_motif_by_ic(pfm, cwm, pad=4)
        
        motif_pfms[-1].append(trimmed_pfm)
        motif_hcwms[-1].append(trimmed_hcwm)
        motif_cwms[-1].append(trimmed_cwm)
        
        num_seqlets[-1].append(len(seqlets))
        
        if tfm_motifs_cache_dir:
            # Save results and figures
            motif_id = "%d_%d" % (metacluster_i, pattern_i)
            pfm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_pfm_full.png"))
            hcwm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_full.png"))
            cwm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_cwm_full.png"))
            motif_dset = motif_hdf5.create_group(motif_id)
            motif_dset.create_dataset("pfm_full", data=pfm, compression="gzip")
            motif_dset.create_dataset("hcwm_full", data=hcwm, compression="gzip")
            motif_dset.create_dataset("cwm_full", data=cwm, compression="gzip")
            motif_dset.create_dataset("pfm_trimmed", data=trimmed_pfm, compression="gzip")
            motif_dset.create_dataset("hcwm_trimmed", data=trimmed_hcwm, compression="gzip")
            motif_dset.create_dataset("cwm_trimmed", data=trimmed_cwm, compression="gzip")
            motif_dset.create_dataset("pfm_short_trimmed", data=short_trimmed_pfm, compression="gzip")
if tfm_motifs_cache_dir:
    motif_hdf5.close()

Metacluster 1/2

Pattern 1/12

5655 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 2/12

637 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 3/12

508 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 4/12

239 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 5/12

182 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 6/12

148 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 7/12

145 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 8/12

62 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 9/12

51 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 10/12

43 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 11/12

33 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 12/12

30 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Metacluster 2/2

Pattern 1/5

552 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 2/5

307 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 3/5

99 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 4/5

76 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 5/5

54 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Summary of motifs

Motifs are trimmed based on information content, and presented in descending order by number of supporting seqlets. The motifs are separated by metacluster. The motifs are presented as hCWMs. The forward orientation is defined as the orientation that is richer in purines.

colgroup = vdomh.colgroup(
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "45%"}),
    vdomh.col(style={"width": "45%"})
)
header = vdomh.thead(
    vdomh.tr(
        vdomh.th("#", style={"text-align": "center"}),
        vdomh.th("Seqlets", style={"text-align": "center"}),
        vdomh.th("Forward", style={"text-align": "center"}),
        vdomh.th("Reverse", style={"text-align": "center"})
    )
)

for i in range(len(motif_hcwms)):
    display(vdomh.h3("Metacluster %d/%d" % (i + 1, num_metaclusters)))
    body = []
    for j in range(len(motif_hcwms[i])):
        motif = motif_hcwms[i][j]
        if np.sum(motif[:, [0, 2]]) > 0.5 * np.sum(motif):
            # Forward is purine-rich, reverse-complement is pyrimidine-rich
            f, rc = motif, np.flip(motif, axis=(0, 1))
        else:
            f, rc = np.flip(motif, axis=(0, 1)), motif
            
        f_fig = viz_sequence.plot_weights(f, figsize=(20, 4), return_fig=True)
        f_fig.tight_layout()
        rc_fig = viz_sequence.plot_weights(rc, figsize=(20, 4), return_fig=True)
        rc_fig.tight_layout()
        
        if tfm_motifs_cache_dir:
            # Save results and figures
            motif_id = "%d_%d" % (i, j)
            f_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_trimmed_fwd.png"))
            rc_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_trimmed_rev.png"))

        body.append(
            vdomh.tr(
                vdomh.td(str(j + 1)),
                vdomh.td(str(num_seqlets[i][j])),
                vdomh.td(figure_to_vdom_image(f_fig)),
                vdomh.td(figure_to_vdom_image(rc_fig))
            )
        )
    display(vdomh.table(colgroup, header, vdomh.tbody(*body)))
    plt.close("all")

Metacluster 1/2

/users/amtseng/tfmodisco/src/plot/viz_sequence.py:152: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig = plt.figure(figsize=figsize)

#	Seqlets	Forward	Reverse
1	5655
2	637
3	508
4	239
5	182
6	148
7	145
8	62
9	51
10	43
11	33
12	30

Metacluster 2/2

#	Seqlets	Forward	Reverse
1	552
2	307
3	99
4	76
5	54

Top TOMTOM matches for each motif

Here, the TF-MoDISco motifs are plotted as hCWMs, but the TOMTOM matches are shown as PWMs.

num_matches_to_keep = 10
num_matches_to_show = 5

header = vdomh.thead(
    vdomh.tr(
        vdomh.th("Motif ID", style={"text-align": "center"}),
        vdomh.th("q-val", style={"text-align": "center"}),
        vdomh.th("PWM", style={"text-align": "center"})
    )
)

for i in range(len(motif_pfms)):
    display(vdomh.h3("Metacluster %d/%d" % (i + 1, num_metaclusters)))
    
    # Compute TOMTOM matches for all motifs in the metacluster at once
    out_dir = os.path.join(tfm_motifs_cache_dir, "tomtom", "metacluster_%d" % i) if tfm_motifs_cache_dir else None
    tomtom_matches = match_motifs_to_database(motif_pfms_short[i], top_k=num_matches_to_keep, temp_dir=out_dir)
    
    for j in range(len(motif_pfms[i])):
        display(vdomh.h4("Motif %d/%d" % (j + 1, len(motif_pfms[i]))))
        viz_sequence.plot_weights(motif_hcwms[i][j])
    
        body = []
        for k, (match_name, match_pfm, match_qval) in enumerate(tomtom_matches[j]):
            fig = viz_sequence.plot_weights(pfm_to_pwm(match_pfm), return_fig=True)
            fig.tight_layout()
            if k < num_matches_to_show:
                body.append(
                    vdomh.tr(
                        vdomh.td(match_name),
                        vdomh.td(str(match_qval)),
                        vdomh.td(figure_to_vdom_image(fig))
                    )
                )
                if tfm_motifs_cache_dir:
                    # Save results and figures
                    motif_id = "%d_%d" % (i, j)
                    fig.savefig(os.path.join(out_dir, motif_id + ("_hit-%d.png" % (k + 1))))
            else:
                body.append(
                    vdomh.tr(
                        vdomh.td(match_name),
                        vdomh.td(str(match_qval)),
                        vdomh.td("Not shown")
                    )
                )
        if not body:
            display(vdomh.p("No TOMTOM matches passing threshold"))
        else:
            display(vdomh.table(header, vdomh.tbody(*body)))
        plt.close("all")

Metacluster 1/2

Motif 1/12

Motif ID	q-val	PWM
MA0076.2_ELK4	2.86856e-06
ETV1_HUMAN.H11MO.0.A	2.86856e-06
ELF2_HUMAN.H11MO.0.C	1.51099e-05
MA0750.2_ZBTB7A	1.51099e-05
MA0759.1_ELK3	2.6889699999999998e-05
ELK1_HUMAN.H11MO.0.B	2.80101e-05	Not shown
ELK4_HUMAN.H11MO.0.A	2.80101e-05	Not shown
MA1483.1_ELF2	2.80101e-05	Not shown
ELF1_HUMAN.H11MO.0.A	3.13713e-05	Not shown
GABPA_HUMAN.H11MO.0.A	3.13713e-05	Not shown

Motif 2/12

Motif ID	q-val	PWM
GABPA_HUMAN.H11MO.0.A	4.62388e-05
ELF2_HUMAN.H11MO.0.C	0.00128641
ELF1_HUMAN.H11MO.0.A	0.00221173
MA0076.2_ELK4	0.002559
ELK1_HUMAN.H11MO.0.B	0.002559
MA0645.1_ETV6	0.00314363	Not shown
MA0763.1_ETV3	0.00328048	Not shown
ETS1_HUMAN.H11MO.0.A	0.00328048	Not shown
ERG_HUMAN.H11MO.0.A	0.00328048	Not shown
MA0098.3_ETS1	0.00328048	Not shown

Motif 3/12

Motif ID	q-val	PWM
ZN143_HUMAN.H11MO.0.A	2.71714e-23
ZNF76_HUMAN.H11MO.0.C	2.71714e-23
THA11_HUMAN.H11MO.0.B	1.0128500000000001e-19
MA1573.1_THAP11	4.6756800000000005e-09
MA0088.2_ZNF143	0.00669159
STAT3_HUMAN.H11MO.0.A	0.056435900000000004	Not shown
P63_HUMAN.H11MO.0.A	0.0921184	Not shown
MA1625.1_Stat5b	0.0965254	Not shown
MA0519.1_Stat5a::Stat5b	0.130545	Not shown
HSF1_HUMAN.H11MO.1.A	0.221111	Not shown

Motif 4/12

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	0.0616073
EGR4_HUMAN.H11MO.0.D	0.0616073
SP3_HUMAN.H11MO.0.B	0.0616073
SP1_HUMAN.H11MO.0.A	0.11714100000000001
KLF3_HUMAN.H11MO.0.B	0.11714100000000001
MA1513.1_KLF15	0.11714100000000001	Not shown
TAF1_HUMAN.H11MO.0.A	0.129778	Not shown
KLF1_HUMAN.H11MO.0.A	0.129778	Not shown
MA0146.2_Zfx	0.129778	Not shown
SP1_HUMAN.H11MO.1.A	0.131073	Not shown

Motif 5/12

Motif ID	q-val	PWM
MA0765.2_ETV5	0.0895911
MA0645.1_ETV6	0.0895911
ETV1_HUMAN.H11MO.0.A	0.0895911
MA0076.2_ELK4	0.0895911
ELF2_HUMAN.H11MO.0.C	0.0895911
MA0750.2_ZBTB7A	0.0895911	Not shown
ELF1_HUMAN.H11MO.0.A	0.0895911	Not shown
GABPA_HUMAN.H11MO.0.A	0.0895911	Not shown
ELK1_HUMAN.H11MO.0.B	0.13198800000000002	Not shown
MA0641.1_ELF4	0.13198800000000002	Not shown

Motif 6/12

Motif ID	q-val	PWM
SP1_HUMAN.H11MO.1.A	0.000289808
KLF16_HUMAN.H11MO.0.D	0.000289808
SP3_HUMAN.H11MO.0.B	0.000313593
SP1_HUMAN.H11MO.0.A	0.000313593
SP2_HUMAN.H11MO.0.A	0.000314949
TBX15_HUMAN.H11MO.0.D	0.000977601	Not shown
MA1513.1_KLF15	0.00219738	Not shown
KLF3_HUMAN.H11MO.0.B	0.00507868	Not shown
KLF6_HUMAN.H11MO.0.A	0.0059828	Not shown
MA0162.4_EGR1	0.00601004	Not shown

Motif 7/12

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	4.8968400000000004e-05
SP1_HUMAN.H11MO.0.A	5.7782700000000005e-05
SP3_HUMAN.H11MO.0.B	5.7782700000000005e-05
KLF16_HUMAN.H11MO.0.D	0.000595672
TBX15_HUMAN.H11MO.0.D	0.00104909
KLF6_HUMAN.H11MO.0.A	0.00159185	Not shown
KLF3_HUMAN.H11MO.0.B	0.00159185	Not shown
PATZ1_HUMAN.H11MO.0.C	0.00159185	Not shown
ZFX_HUMAN.H11MO.1.A	0.00244572	Not shown
SP1_HUMAN.H11MO.1.A	0.00311346	Not shown

Motif 8/12

Motif ID	q-val	PWM
SP1_HUMAN.H11MO.0.A	0.00197156
SP2_HUMAN.H11MO.0.A	0.00197156
SP3_HUMAN.H11MO.0.B	0.00225356
MA1513.1_KLF15	0.00264989
KLF3_HUMAN.H11MO.0.B	0.00341705
CTCFL_HUMAN.H11MO.0.A	0.00472491	Not shown
KLF12_HUMAN.H11MO.0.C	0.00535772	Not shown
SP1_HUMAN.H11MO.1.A	0.00535772	Not shown
MA1522.1_MAZ	0.00996004	Not shown
MA1650.1_ZBTB14	0.0111435	Not shown

Motif 9/12

Motif ID	q-val	PWM
THA11_HUMAN.H11MO.0.B	1.2725199999999998e-07
ZN143_HUMAN.H11MO.0.A	1.6009699999999998e-07
ZNF76_HUMAN.H11MO.0.C	1.6009699999999998e-07
MA1573.1_THAP11	3.43304e-05
USF2_HUMAN.H11MO.0.A	0.0475472
SP2_HUMAN.H11MO.0.A	0.04801130000000001	Not shown
SP3_HUMAN.H11MO.0.B	0.12133900000000002	Not shown
SP1_HUMAN.H11MO.0.A	0.15099	Not shown
MA1513.1_KLF15	0.15099	Not shown
MA0814.2_TFAP2C(var.2)	0.156944	Not shown

Motif 10/12

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	0.00372899
THAP1_HUMAN.H11MO.0.C	0.00442148
SP1_HUMAN.H11MO.0.A	0.00674917
SP3_HUMAN.H11MO.0.B	0.00847309
KLF3_HUMAN.H11MO.0.B	0.010232600000000001
SP1_HUMAN.H11MO.1.A	0.0337106	Not shown
MA0076.2_ELK4	0.0337106	Not shown
USF2_HUMAN.H11MO.0.A	0.0337106	Not shown
MA1513.1_KLF15	0.045051	Not shown
MA0765.2_ETV5	0.045051	Not shown

Motif 11/12

Motif ID	q-val	PWM
CPEB1_HUMAN.H11MO.0.D	4.26852e-05
PRDM6_HUMAN.H11MO.0.C	0.00703944
MA1125.1_ZNF384	0.014277200000000002
FOXL1_HUMAN.H11MO.0.D	0.017364599999999997
ANDR_HUMAN.H11MO.0.A	0.0469573
FOXG1_HUMAN.H11MO.0.D	0.0469573	Not shown
FOXJ3_HUMAN.H11MO.0.A	0.0611236	Not shown
ONEC2_HUMAN.H11MO.0.D	0.0712365	Not shown
FUBP1_HUMAN.H11MO.0.D	0.08480089999999998	Not shown
FOXJ3_HUMAN.H11MO.1.B	0.08480089999999998	Not shown

Motif 12/12

Motif ID	q-val	PWM
ZNF76_HUMAN.H11MO.0.C	3.02056e-18
ZN143_HUMAN.H11MO.0.A	1.9534e-16
THA11_HUMAN.H11MO.0.B	1.39822e-15
MA1573.1_THAP11	3.51794e-07
STAT3_HUMAN.H11MO.0.A	0.08542050000000001
MA0088.2_ZNF143	0.11521500000000001	Not shown
P63_HUMAN.H11MO.0.A	0.179626	Not shown
MA0525.2_TP63	0.285496	Not shown
THAP1_HUMAN.H11MO.0.C	0.285496	Not shown
MA0519.1_Stat5a::Stat5b	0.28813	Not shown

Metacluster 2/2

Motif 1/5

Motif ID	q-val	PWM
MA0076.2_ELK4	8.34824e-05
ELK4_HUMAN.H11MO.0.A	0.000161558
MA0750.2_ZBTB7A	0.000161558
MA0765.2_ETV5	0.000426061
ELF1_HUMAN.H11MO.0.A	0.000474039
ELK1_HUMAN.H11MO.0.B	0.000474039	Not shown
GABPA_HUMAN.H11MO.0.A	0.000527961	Not shown
ELF2_HUMAN.H11MO.0.C	0.000527961	Not shown
ETV1_HUMAN.H11MO.0.A	0.00183229	Not shown
ETS1_HUMAN.H11MO.0.A	0.00230517	Not shown

Motif 2/5

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	0.0169963
SP3_HUMAN.H11MO.0.B	0.0169963
SP1_HUMAN.H11MO.0.A	0.0169963
MA1650.1_ZBTB14	0.0169963
MA0146.2_Zfx	0.0169963
MBD2_HUMAN.H11MO.0.B	0.0169963	Not shown
MA1513.1_KLF15	0.0207532	Not shown
USF2_HUMAN.H11MO.0.A	0.0207532	Not shown
MA1102.2_CTCFL	0.03389930000000001	Not shown
MA0753.2_ZNF740	0.03389930000000001	Not shown

Motif 3/5

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	2.28857e-09
SP1_HUMAN.H11MO.0.A	5.7732e-07
SP3_HUMAN.H11MO.0.B	6.77178e-07
SP1_HUMAN.H11MO.1.A	0.00020901
MA1513.1_KLF15	0.000376246
KLF3_HUMAN.H11MO.0.B	0.0007776580000000001	Not shown
USF2_HUMAN.H11MO.0.A	0.00184564	Not shown
KLF16_HUMAN.H11MO.0.D	0.0024127	Not shown
MA1650.1_ZBTB14	0.00623894	Not shown
MA0146.2_Zfx	0.00864647	Not shown

Motif 4/5

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	4.19454e-08
SP3_HUMAN.H11MO.0.B	5.33085e-06
SP1_HUMAN.H11MO.0.A	5.33085e-06
ZFX_HUMAN.H11MO.1.A	0.00460525
SP1_HUMAN.H11MO.1.A	0.00460525
KLF16_HUMAN.H11MO.0.D	0.00460525	Not shown
KLF3_HUMAN.H11MO.0.B	0.00460525	Not shown
PATZ1_HUMAN.H11MO.0.C	0.00460593	Not shown
USF2_HUMAN.H11MO.0.A	0.00460593	Not shown
MA1650.1_ZBTB14	0.00460593	Not shown

Motif 5/5

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	6.84947e-09
SP3_HUMAN.H11MO.0.B	2.8054500000000003e-07
SP1_HUMAN.H11MO.0.A	1.5732299999999998e-06
GABPA_HUMAN.H11MO.0.A	9.325409999999999e-05
KLF3_HUMAN.H11MO.0.B	0.000145654
ELK4_HUMAN.H11MO.0.A	0.000145654	Not shown
MA0076.2_ELK4	0.000145654	Not shown
MA0750.2_ZBTB7A	0.000145654	Not shown
MA0765.2_ETV5	0.00018868900000000002	Not shown
SP1_HUMAN.H11MO.1.A	0.000257072	Not shown