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: CEBPB
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/CEBPB_multitask_profile_fold2/CEBPB_multitask_profile_fold2_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold2/CEBPB_multitask_profile_fold2_count_tfm.h5
Importance score key: count_hyp_scores
Saved TF-MoDISco-derived motifs cache: /users/amtseng/tfmodisco/results/reports/tfmodisco_results//cache/multitask_profile/CEBPB_multitask_profile_fold2/CEBPB_multitask_profile_fold2_count

# 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%|██████████| 273/273 [05:57<00:00,  1.31s/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 1/11

11119 seqlets

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

Pattern 2/11

813 seqlets

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

Pattern 3/11

777 seqlets

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

Pattern 4/11

321 seqlets

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

Pattern 5/11

268 seqlets

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

Pattern 6/11

180 seqlets

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

Pattern 7/11

102 seqlets

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

Pattern 8/11

61 seqlets

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

Pattern 9/11

51 seqlets

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

Pattern 10/11

41 seqlets

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

Pattern 11/11

33 seqlets

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

Metacluster 2/2

Pattern 1/8

317 seqlets

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

Pattern 2/8

290 seqlets

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

Pattern 3/8

200 seqlets

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

Pattern 4/8

168 seqlets

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

Pattern 5/8

123 seqlets

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

Pattern 6/8

91 seqlets

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

Pattern 7/8

89 seqlets

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

Pattern 8/8

86 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	11119
2	813
3	777
4	321
5	268
6	180
7	102
8	61
9	51
10	41
11	33

Metacluster 2/2

#	Seqlets	Forward	Reverse
1	317
2	290
3	200
4	168
5	123
6	91
7	89
8	86

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 1/11

Motif ID	q-val	PWM
CEBPB_HUMAN.H11MO.0.A	1.8356900000000004e-10
CEBPD_HUMAN.H11MO.0.C	4.11378e-09
CEBPA_HUMAN.H11MO.0.A	9.767529999999999e-07
MA0836.2_CEBPD	1.76334e-05
MA0102.4_CEBPA	0.000193904
MA0837.1_CEBPE	0.000326344	Not shown
MA0466.2_CEBPB	0.00040234	Not shown
MA0838.1_CEBPG	0.00066675	Not shown
MA0025.2_NFIL3	0.00176952	Not shown
DBP_HUMAN.H11MO.0.B	0.00280567	Not shown

Motif 2/11

Motif ID	q-val	PWM
MA0139.1_CTCF	2.76986e-16
CTCF_HUMAN.H11MO.0.A	1.6852300000000001e-13
CTCFL_HUMAN.H11MO.0.A	1.00118e-07
MA1102.2_CTCFL	5.68396e-05
MA1568.1_TCF21(var.2)	0.11782000000000001
MA1638.1_HAND2	0.129864	Not shown
ZIC3_HUMAN.H11MO.0.B	0.151221	Not shown
SNAI1_HUMAN.H11MO.0.C	0.181543	Not shown
ZIC2_HUMAN.H11MO.0.D	0.232823	Not shown
MA1629.1_Zic2	0.30912399999999995	Not shown

Motif 3/11

Motif ID	q-val	PWM
FOSL1_HUMAN.H11MO.0.A	4.90334e-07
FOSB_HUMAN.H11MO.0.A	5.98862e-07
JUND_HUMAN.H11MO.0.A	2.02925e-06
JUN_HUMAN.H11MO.0.A	2.02925e-06
FOS_HUMAN.H11MO.0.A	3.20687e-06
FOSL2_HUMAN.H11MO.0.A	3.5630300000000004e-06	Not shown
JUNB_HUMAN.H11MO.0.A	1.48922e-05	Not shown
MA0099.3_FOS::JUN	1.48922e-05	Not shown
MA1130.1_FOSL2::JUN	1.7461300000000002e-05	Not shown
MA0478.1_FOSL2	1.7870599999999998e-05	Not shown

Motif 4/11

Motif ID	q-val	PWM
HNF4G_HUMAN.H11MO.0.B	4.5432399999999997e-10
HNF4A_HUMAN.H11MO.0.A	5.744580000000001e-10
MA1494.1_HNF4A(var.2)	0.000987179
MA0114.4_HNF4A	0.0012319000000000002
MA0484.2_HNF4G	0.0012319000000000002
MA0856.1_RXRG	0.00182263	Not shown
MA0677.1_Nr2f6	0.00182263	Not shown
MA0512.2_Rxra	0.00182263	Not shown
MA1574.1_THRB	0.00182263	Not shown
MA1550.1_PPARD	0.00182263	Not shown

Motif 5/11

Motif ID	q-val	PWM
FOXA1_HUMAN.H11MO.0.A	4.844380000000001e-07
FOXF2_HUMAN.H11MO.0.D	4.844380000000001e-07
FOXM1_HUMAN.H11MO.0.A	9.71411e-07
FOXA3_HUMAN.H11MO.0.B	1.82408e-06
FOXA2_HUMAN.H11MO.0.A	3.29949e-06
FOXD3_HUMAN.H11MO.0.D	8.92592e-06	Not shown
MA0846.1_FOXC2	8.976419999999999e-06	Not shown
MA0847.2_FOXD2	2.92079e-05	Not shown
FOXC1_HUMAN.H11MO.0.C	2.92079e-05	Not shown
FOXD1_HUMAN.H11MO.0.D	0.000365282	Not shown

Motif 6/11

Motif ID	q-val	PWM
GATA1_HUMAN.H11MO.1.A	5.477459999999999e-06
GATA2_HUMAN.H11MO.1.A	5.477459999999999e-06
GATA4_HUMAN.H11MO.0.A	1.11359e-05
GATA1_HUMAN.H11MO.0.A	1.78531e-05
GATA2_HUMAN.H11MO.0.A	2.3530900000000002e-05
TAL1_HUMAN.H11MO.0.A	4.6070100000000005e-05	Not shown
GATA6_HUMAN.H11MO.0.A	0.000114409	Not shown
MA0036.3_GATA2	0.00049434	Not shown
MA0766.2_GATA5	0.000750633	Not shown
MA0037.3_GATA3	0.0033868	Not shown

Motif 7/11

Motif ID	q-val	PWM
ATF2_HUMAN.H11MO.0.B	5.77565e-06
MA0018.4_CREB1	5.36194e-05
MA1136.1_FOSB::JUNB(var.2)	7.94334e-05
MA0605.2_ATF3	7.94334e-05
MA1145.1_FOSL2::JUND(var.2)	9.535549999999999e-05
MA1129.1_FOSL1::JUN(var.2)	9.535549999999999e-05	Not shown
MA1131.1_FOSL2::JUN(var.2)	0.00010605799999999999	Not shown
MA1133.1_JUN::JUNB(var.2)	0.00010605799999999999	Not shown
MA1139.1_FOSL2::JUNB(var.2)	0.00010605799999999999	Not shown
MA1127.1_FOSB::JUN	0.00010605799999999999	Not shown

Motif 8/11

Motif ID	q-val	PWM
SP1_HUMAN.H11MO.0.A	7.366979999999999e-13
SP2_HUMAN.H11MO.0.A	7.963739999999999e-10
SP3_HUMAN.H11MO.0.B	2.50259e-09
KLF3_HUMAN.H11MO.0.B	9.858160000000001e-09
SP1_HUMAN.H11MO.1.A	1.6007900000000001e-06
KLF6_HUMAN.H11MO.0.A	1.69984e-06	Not shown
SP4_HUMAN.H11MO.0.A	4.07319e-06	Not shown
SP2_HUMAN.H11MO.1.B	4.07319e-06	Not shown
SP4_HUMAN.H11MO.1.A	4.07319e-06	Not shown
KLF16_HUMAN.H11MO.0.D	6.0765999999999996e-06	Not shown

Motif 9/11

Motif ID	q-val	PWM
SP3_HUMAN.H11MO.0.B	6.76219e-07
SP2_HUMAN.H11MO.0.A	6.80983e-07
SP1_HUMAN.H11MO.1.A	1.8897799999999998e-06
KLF3_HUMAN.H11MO.0.B	2.72166e-06
SP4_HUMAN.H11MO.1.A	4.27701e-06
MA1513.1_KLF15	1.1152699999999999e-05	Not shown
TBX15_HUMAN.H11MO.0.D	1.33963e-05	Not shown
KLF16_HUMAN.H11MO.0.D	1.33963e-05	Not shown
SP1_HUMAN.H11MO.0.A	1.39155e-05	Not shown
SP4_HUMAN.H11MO.0.A	2.6426500000000002e-05	Not shown

Motif 10/11

Motif ID	q-val	PWM
ELK1_HUMAN.H11MO.0.B	0.00838607
MA0763.1_ETV3	0.00838607
MA0028.2_ELK1	0.00838607
MA0474.2_ERG	0.00838607
MA0760.1_ERF	0.00838607
MA0156.2_FEV	0.00838607	Not shown
MA0475.2_FLI1	0.00838607	Not shown
MA0765.2_ETV5	0.00992291	Not shown
GABPA_HUMAN.H11MO.0.A	0.00992291	Not shown
MA0098.3_ETS1	0.0109817	Not shown

Motif 11/11

Motif ID	q-val	PWM
MA0837.1_CEBPE	0.0917125
CEBPE_HUMAN.H11MO.0.A	0.0917125
CEBPD_HUMAN.H11MO.0.C	0.0917125
MA0512.2_Rxra	0.0917125
MA0838.1_CEBPG	0.0917125
CEBPB_HUMAN.H11MO.0.A	0.0917125	Not shown
MA0855.1_RXRB	0.0917125	Not shown
MA0843.1_TEF	0.0917125	Not shown
MA0639.1_DBP	0.0917125	Not shown
DBP_HUMAN.H11MO.0.B	0.0917125	Not shown

Metacluster 2/2

Motif 1/8

Motif ID	q-val	PWM
CEBPD_HUMAN.H11MO.0.C	8.1528e-06
CEBPB_HUMAN.H11MO.0.A	1.29018e-05
MA0466.2_CEBPB	3.63805e-05
MA0837.1_CEBPE	3.63805e-05
MA0838.1_CEBPG	0.000384148
NFIL3_HUMAN.H11MO.0.D	0.000623504	Not shown
MA0043.3_HLF	0.000623504	Not shown
MA0025.2_NFIL3	0.000623504	Not shown
DBP_HUMAN.H11MO.0.B	0.000623504	Not shown
CEBPG_HUMAN.H11MO.0.B	0.000623504	Not shown

Motif 2/8

Motif ID	q-val	PWM
ZN770_HUMAN.H11MO.0.C	0.45613400000000004
ETV2_HUMAN.H11MO.0.B	0.45613400000000004
ZN257_HUMAN.H11MO.0.C	0.45613400000000004
ZSC22_HUMAN.H11MO.0.C	0.45613400000000004
FLI1_HUMAN.H11MO.0.A	0.45613400000000004

Motif 3/8

Motif ID	q-val	PWM
WT1_HUMAN.H11MO.0.C	0.012779299999999999
SP3_HUMAN.H11MO.0.B	0.015156799999999998
MA0753.2_ZNF740	0.015156799999999998
ZN341_HUMAN.H11MO.0.C	0.015156799999999998
PATZ1_HUMAN.H11MO.0.C	0.0172218
KLF15_HUMAN.H11MO.0.A	0.0172218	Not shown
SP1_HUMAN.H11MO.0.A	0.0172218	Not shown
ZN740_HUMAN.H11MO.0.D	0.018603900000000003	Not shown
SP4_HUMAN.H11MO.1.A	0.018603900000000003	Not shown
KLF16_HUMAN.H11MO.0.D	0.018603900000000003	Not shown

Motif 4/8

Motif ID	q-val	PWM
MA1529.1_NHLH2	0.06620830000000001
TAF1_HUMAN.H11MO.0.A	0.42916499999999996
MA1583.1_ZFP57	0.42916499999999996
MA1596.1_ZNF460	0.42916499999999996
HEN1_HUMAN.H11MO.0.C	0.495696

Motif 5/8

No TOMTOM matches passing threshold

Motif 6/8

Motif ID	q-val	PWM
NFIC_HUMAN.H11MO.0.A	0.41601499999999997

Motif 7/8

No TOMTOM matches passing threshold

Motif 8/8

No TOMTOM matches passing threshold