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_fold9/GABPA_multitask_profile_fold9_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/GABPA_multitask_profile_fold9/GABPA_multitask_profile_fold9_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_fold9/GABPA_multitask_profile_fold9_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 [01:37<00:00,  1.06it/s]

# 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 1/10

5445 seqlets

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

Pattern 2/10

481 seqlets

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

Pattern 3/10

392 seqlets

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

Pattern 4/10

347 seqlets

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

Pattern 5/10

310 seqlets

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

Pattern 6/10

287 seqlets

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

Pattern 7/10

133 seqlets

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

Pattern 8/10

116 seqlets

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

Pattern 9/10

102 seqlets

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

Pattern 10/10

69 seqlets

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

Metacluster 2/2

Pattern 1/8

411 seqlets

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

Pattern 2/8

234 seqlets

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

Pattern 3/8

173 seqlets

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

Pattern 4/8

135 seqlets

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

Pattern 5/8

90 seqlets

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

Pattern 6/8

50 seqlets

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

Pattern 7/8

47 seqlets

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

Pattern 8/8

41 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

#	Seqlets	Forward	Reverse
1	5445
2	481
3	392
4	347
5	310
6	287
7	133
8	116
9	102
10	69

Metacluster 2/2

#	Seqlets	Forward	Reverse
1	411
2	234
3	173
4	135
5	90
6	50
7	47
8	41

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 1/10

Motif ID	q-val	PWM
ETV1_HUMAN.H11MO.0.A	8.640560000000001e-07
MA0750.2_ZBTB7A	8.640560000000001e-07
ELK1_HUMAN.H11MO.0.B	1.55745e-06
MA0076.2_ELK4	1.55745e-06
ELK4_HUMAN.H11MO.0.A	1.55745e-06
ELF2_HUMAN.H11MO.0.C	1.55745e-06	Not shown
ELF1_HUMAN.H11MO.0.A	1.55745e-06	Not shown
GABPA_HUMAN.H11MO.0.A	1.55745e-06	Not shown
MA0765.2_ETV5	8.90893e-06	Not shown
MA0763.1_ETV3	2.8174200000000002e-05	Not shown

Motif 2/10

Motif ID	q-val	PWM
ZNF76_HUMAN.H11MO.0.C	2.70023e-21
ZN143_HUMAN.H11MO.0.A	7.389760000000001e-20
THA11_HUMAN.H11MO.0.B	2.65974e-17
MA1573.1_THAP11	2.24248e-09
MA0088.2_ZNF143	0.014499799999999998
STAT3_HUMAN.H11MO.0.A	0.0564617	Not shown
P63_HUMAN.H11MO.0.A	0.07686799999999999	Not shown
MA1625.1_Stat5b	0.107287	Not shown
MA0519.1_Stat5a::Stat5b	0.12906199999999998	Not shown
MA0525.2_TP63	0.16322	Not shown

Motif 3/10

Motif ID	q-val	PWM
GABPA_HUMAN.H11MO.0.A	0.00317498
MA0076.2_ELK4	0.00942936
MA0645.1_ETV6	0.0111744
MA0474.2_ERG	0.0111744
MA0475.2_FLI1	0.0111744
ELF2_HUMAN.H11MO.0.C	0.0111744	Not shown
MA0098.3_ETS1	0.0111744	Not shown
MA0763.1_ETV3	0.0111744	Not shown
MA0028.2_ELK1	0.0116	Not shown
ELK4_HUMAN.H11MO.0.A	0.0116	Not shown

Motif 4/10

No TOMTOM matches passing threshold

Motif 5/10

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	0.0536102
CTCFL_HUMAN.H11MO.0.A	0.0536102
USF2_HUMAN.H11MO.0.A	0.0536102
SP1_HUMAN.H11MO.1.A	0.0536102
SP3_HUMAN.H11MO.0.B	0.0536102
CTCF_HUMAN.H11MO.0.A	0.059375199999999996	Not shown
SP1_HUMAN.H11MO.0.A	0.0809607	Not shown
EGR4_HUMAN.H11MO.0.D	0.0809607	Not shown
MA1513.1_KLF15	0.0809607	Not shown
MA1102.2_CTCFL	0.0809607	Not shown

Motif 6/10

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	5.75151e-06
SP1_HUMAN.H11MO.0.A	1.3186500000000002e-05
SP3_HUMAN.H11MO.0.B	6.25006e-05
KLF16_HUMAN.H11MO.0.D	6.37028e-05
TBX15_HUMAN.H11MO.0.D	0.000135048
SP1_HUMAN.H11MO.1.A	0.000135048	Not shown
KLF3_HUMAN.H11MO.0.B	0.0009614360000000001	Not shown
MA1513.1_KLF15	0.00154848	Not shown
MA1650.1_ZBTB14	0.00245929	Not shown
PATZ1_HUMAN.H11MO.0.C	0.00308759	Not shown

Motif 7/10

No TOMTOM matches passing threshold

Motif 8/10

Motif ID	q-val	PWM
SP1_HUMAN.H11MO.0.A	0.045951599999999995
ATF2_HUMAN.H11MO.2.C	0.045951599999999995
SP2_HUMAN.H11MO.0.A	0.045951599999999995
USF2_HUMAN.H11MO.0.A	0.0679066
RFX1_HUMAN.H11MO.0.B	0.0718509
SP3_HUMAN.H11MO.0.B	0.0718509	Not shown
CREM_HUMAN.H11MO.0.C	0.0718509	Not shown
KLF16_HUMAN.H11MO.0.D	0.11839100000000001	Not shown
MA0765.2_ETV5	0.134444	Not shown
MA1475.1_CREB3L4(var.2)	0.134444	Not shown

Motif 9/10

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	0.035374199999999995
USF2_HUMAN.H11MO.0.A	0.035374199999999995
MXI1_HUMAN.H11MO.0.A	0.0709697
SP1_HUMAN.H11MO.0.A	0.100678
MA1527.1_NFIC(var.2)	0.100678
MA1513.1_KLF15	0.14777300000000002	Not shown
MA0146.2_Zfx	0.176448	Not shown
MA1650.1_ZBTB14	0.176448	Not shown
SP3_HUMAN.H11MO.0.B	0.176448	Not shown
MA1528.1_NFIX(var.2)	0.176448	Not shown

Motif 10/10

Motif ID	q-val	PWM
SP1_HUMAN.H11MO.0.A	7.4009699999999995e-06
SP2_HUMAN.H11MO.0.A	7.4009699999999995e-06
SP3_HUMAN.H11MO.0.B	0.00021490099999999998
PATZ1_HUMAN.H11MO.0.C	0.0009736069999999999
MA1513.1_KLF15	0.0009736069999999999
MA1650.1_ZBTB14	0.0009736069999999999	Not shown
SP1_HUMAN.H11MO.1.A	0.00104494	Not shown
KLF16_HUMAN.H11MO.0.D	0.00141096	Not shown
ZFX_HUMAN.H11MO.1.A	0.00361323	Not shown
USF2_HUMAN.H11MO.0.A	0.00361323	Not shown

Metacluster 2/2

Motif 1/8

Motif ID	q-val	PWM
MA0076.2_ELK4	4.20439e-06
MA0750.2_ZBTB7A	4.20439e-06
ELK4_HUMAN.H11MO.0.A	1.17928e-05
MA0765.2_ETV5	5.4437399999999995e-05
ELF1_HUMAN.H11MO.0.A	5.97171e-05
GABPA_HUMAN.H11MO.0.A	7.47474e-05	Not shown
ELF2_HUMAN.H11MO.0.C	7.47474e-05	Not shown
ELK1_HUMAN.H11MO.0.B	7.47474e-05	Not shown
ETV1_HUMAN.H11MO.0.A	0.00110926	Not shown
ETS1_HUMAN.H11MO.0.A	0.00114018	Not shown

Motif 2/8

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	1.25861e-08
SP3_HUMAN.H11MO.0.B	5.9789099999999995e-06
SP1_HUMAN.H11MO.0.A	0.000139354
KLF3_HUMAN.H11MO.0.B	0.00048526900000000003
SP1_HUMAN.H11MO.1.A	0.00048526900000000003
MA0146.2_Zfx	0.00110697	Not shown
THAP1_HUMAN.H11MO.0.C	0.0016928000000000002	Not shown
MA1513.1_KLF15	0.00330208	Not shown
ZFX_HUMAN.H11MO.1.A	0.00467669	Not shown
RFX1_HUMAN.H11MO.0.B	0.00705724	Not shown

Motif 3/8

Motif ID	q-val	PWM
GABPA_HUMAN.H11MO.0.A	0.356108
MA0474.2_ERG	0.356108
MA0098.3_ETS1	0.356108
MA0641.1_ELF4	0.356108
ELK4_HUMAN.H11MO.0.A	0.356108
MA0759.1_ELK3	0.356108	Not shown
MA0475.2_FLI1	0.356108	Not shown
ELF2_HUMAN.H11MO.0.C	0.356108	Not shown
MA0762.1_ETV2	0.356108	Not shown
MA0076.2_ELK4	0.356108	Not shown

Motif 4/8

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	2.1146100000000002e-07
SP1_HUMAN.H11MO.0.A	1.0284500000000001e-05
SP3_HUMAN.H11MO.0.B	1.12277e-05
KLF3_HUMAN.H11MO.0.B	0.00110058
SP1_HUMAN.H11MO.1.A	0.00166886
MA0146.2_Zfx	0.0025226	Not shown
THAP1_HUMAN.H11MO.0.C	0.00444476	Not shown
MA1513.1_KLF15	0.00537156	Not shown
ZFX_HUMAN.H11MO.1.A	0.00537156	Not shown
MA1650.1_ZBTB14	0.00636835	Not shown

Motif 5/8

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	1.5955200000000001e-06
SP3_HUMAN.H11MO.0.B	5.4266400000000006e-05
SP1_HUMAN.H11MO.0.A	7.23284e-05
MXI1_HUMAN.H11MO.0.A	0.00213446
MA0750.2_ZBTB7A	0.00311168
MA0076.2_ELK4	0.00311168	Not shown
MA0765.2_ETV5	0.00414841	Not shown
KLF16_HUMAN.H11MO.0.D	0.00414841	Not shown
KLF3_HUMAN.H11MO.0.B	0.00414841	Not shown
ELK1_HUMAN.H11MO.0.B	0.00587283	Not shown

Motif 6/8

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	1.8709099999999998e-06
SP3_HUMAN.H11MO.0.B	7.371489999999999e-06
SP1_HUMAN.H11MO.0.A	6.096060000000001e-05
KLF3_HUMAN.H11MO.0.B	0.0008157389999999999
SP1_HUMAN.H11MO.1.A	0.00131308
MA1513.1_KLF15	0.00323884	Not shown
USF2_HUMAN.H11MO.0.A	0.004529600000000001	Not shown
THAP1_HUMAN.H11MO.0.C	0.00717675	Not shown
PATZ1_HUMAN.H11MO.0.C	0.00717675	Not shown
KLF16_HUMAN.H11MO.0.D	0.0116458	Not shown

Motif 7/8

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	1.31602e-05
SP1_HUMAN.H11MO.0.A	5.41108e-05
SP3_HUMAN.H11MO.0.B	5.41108e-05
KLF3_HUMAN.H11MO.0.B	0.000196205
MA0146.2_Zfx	0.0006666589999999999
SP1_HUMAN.H11MO.1.A	0.0029697	Not shown
KLF16_HUMAN.H11MO.0.D	0.00393194	Not shown
MXI1_HUMAN.H11MO.0.A	0.00393194	Not shown
MA1513.1_KLF15	0.00511767	Not shown
THAP1_HUMAN.H11MO.0.C	0.00868532	Not shown

Motif 8/8

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	2.8564099999999997e-06
SP3_HUMAN.H11MO.0.B	2.2293400000000002e-05
SP1_HUMAN.H11MO.0.A	9.62446e-05
KLF3_HUMAN.H11MO.0.B	0.0028508
KLF16_HUMAN.H11MO.0.D	0.00322931
MA1513.1_KLF15	0.00326969	Not shown
MBD2_HUMAN.H11MO.0.B	0.00682825	Not shown
USF2_HUMAN.H11MO.0.A	0.0080395	Not shown
THAP1_HUMAN.H11MO.0.C	0.0080395	Not shown
MXI1_HUMAN.H11MO.0.A	0.0106155	Not shown