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: JUND
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/JUND_multitask_profile_fold4/JUND_multitask_profile_fold4_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/JUND_multitask_profile_fold4/JUND_multitask_profile_fold4_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/JUND_multitask_profile_fold4/JUND_multitask_profile_fold4_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%|██████████| 350/350 [03:34<00:00,  1.63it/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 1/9

7649 seqlets

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

Pattern 2/9

2069 seqlets

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

Pattern 3/9

1018 seqlets

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

Pattern 4/9

255 seqlets

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

Pattern 5/9

252 seqlets

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

Pattern 6/9

213 seqlets

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

Pattern 7/9

136 seqlets

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

Pattern 8/9

89 seqlets

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

Pattern 9/9

41 seqlets

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

Metacluster 2/2

Pattern 1/13

346 seqlets

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

Pattern 2/13

289 seqlets

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

Pattern 3/13

289 seqlets

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

Pattern 4/13

242 seqlets

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

Pattern 5/13

163 seqlets

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

Pattern 6/13

159 seqlets

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

Pattern 7/13

149 seqlets

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

Pattern 8/13

133 seqlets

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

Pattern 9/13

132 seqlets

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

Pattern 10/13

128 seqlets

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

Pattern 11/13

126 seqlets

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

Pattern 12/13

125 seqlets

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

Pattern 13/13

92 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	7649
2	2069
3	1018
4	255
5	252
6	213
7	136
8	89
9	41

Metacluster 2/2

/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	346
2	289
3	289
4	242
5	163
6	159
7	149
8	133
9	132
10	128
11	126
12	125
13	92

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 1/9

Motif ID	q-val	PWM
JUN_HUMAN.H11MO.0.A	2.153e-06
FOSL2_HUMAN.H11MO.0.A	2.153e-06
FOSB_HUMAN.H11MO.0.A	7.61587e-06
JUND_HUMAN.H11MO.0.A	7.61587e-06
MA1130.1_FOSL2::JUN	7.61587e-06
MA0099.3_FOS::JUN	7.61587e-06	Not shown
MA1141.1_FOS::JUND	7.61587e-06	Not shown
MA1144.1_FOSL2::JUND	1.15885e-05	Not shown
FOSL1_HUMAN.H11MO.0.A	1.15885e-05	Not shown
MA1128.1_FOSL1::JUN	1.25378e-05	Not shown

Motif 2/9

Motif ID	q-val	PWM
MA0605.2_ATF3	3.97671e-07
JDP2_HUMAN.H11MO.0.D	1.48988e-06
MA1475.1_CREB3L4(var.2)	2.22613e-06
MA1127.1_FOSB::JUN	2.22613e-06
MA0656.1_JDP2(var.2)	2.22613e-06
MA1145.1_FOSL2::JUND(var.2)	2.22613e-06	Not shown
MA1131.1_FOSL2::JUN(var.2)	2.38053e-06	Not shown
MA1140.2_JUNB(var.2)	2.38053e-06	Not shown
MA1139.1_FOSL2::JUNB(var.2)	4.45174e-06	Not shown
MA1126.1_FOS::JUN(var.2)	5.6541199999999995e-06	Not shown

Motif 3/9

Motif ID	q-val	PWM
ATF4_HUMAN.H11MO.0.A	0.000290127
BATF_HUMAN.H11MO.1.A	0.00032945
CEBPG_HUMAN.H11MO.0.B	0.000395234
MA0833.2_ATF4	0.000592851
MA1636.1_CEBPG(var.2)	0.00196805
DDIT3_HUMAN.H11MO.0.D	0.00351624	Not shown
MA1143.1_FOSL1::JUND(var.2)	0.00351624	Not shown
MA1131.1_FOSL2::JUN(var.2)	0.00351624	Not shown
CEBPD_HUMAN.H11MO.0.C	0.00351624	Not shown
CREB5_HUMAN.H11MO.0.D	0.00424396	Not shown

Motif 4/9

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	1.69928e-06
SP3_HUMAN.H11MO.0.B	5.1474e-06
SP1_HUMAN.H11MO.0.A	5.1474e-06
TBX15_HUMAN.H11MO.0.D	0.000421334
KLF16_HUMAN.H11MO.0.D	0.000597833
WT1_HUMAN.H11MO.0.C	0.0006573039999999999	Not shown
MA1513.1_KLF15	0.00149941	Not shown
KLF3_HUMAN.H11MO.0.B	0.0022098	Not shown
SP1_HUMAN.H11MO.1.A	0.00271378	Not shown
THAP1_HUMAN.H11MO.0.C	0.00271378	Not shown

Motif 5/9

Motif ID	q-val	PWM
USF2_HUMAN.H11MO.0.A	1.7957300000000002e-05
SP2_HUMAN.H11MO.0.A	0.0010084
MA1475.1_CREB3L4(var.2)	0.0010084
ATF3_HUMAN.H11MO.0.A	0.0034753999999999996
ATF2_HUMAN.H11MO.2.C	0.0034753999999999996
MA0609.2_CREM	0.00374946	Not shown
ATF1_HUMAN.H11MO.0.B	0.00386352	Not shown
SP1_HUMAN.H11MO.0.A	0.00682311	Not shown
SP3_HUMAN.H11MO.0.B	0.010115899999999999	Not shown
MA0605.2_ATF3	0.010115899999999999	Not shown

Motif 6/9

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	1.8198e-05
SP3_HUMAN.H11MO.0.B	0.00024932700000000003
SP1_HUMAN.H11MO.0.A	0.00024932700000000003
KLF16_HUMAN.H11MO.0.D	0.000579942
CTCFL_HUMAN.H11MO.0.A	0.00090987
THAP1_HUMAN.H11MO.0.C	0.00155464	Not shown
MA1513.1_KLF15	0.00663897	Not shown
TBX15_HUMAN.H11MO.0.D	0.00887427	Not shown
KLF3_HUMAN.H11MO.0.B	0.00887427	Not shown
PATZ1_HUMAN.H11MO.0.C	0.00942697	Not shown

Motif 7/9

Motif ID	q-val	PWM
MA0466.2_CEBPB	0.00076981
MA0837.1_CEBPE	0.00076981
MA0838.1_CEBPG	0.00102258
CEBPD_HUMAN.H11MO.0.C	0.00102258
HLF_HUMAN.H11MO.0.C	0.00102258
CEBPB_HUMAN.H11MO.0.A	0.00130518	Not shown
MA0639.1_DBP	0.00130518	Not shown
MA0843.1_TEF	0.00156407	Not shown
NFIL3_HUMAN.H11MO.0.D	0.00251499	Not shown
MA0043.3_HLF	0.00413731	Not shown

Motif 8/9

Motif ID	q-val	PWM
CTCFL_HUMAN.H11MO.0.A	8.32468e-06
SP2_HUMAN.H11MO.0.A	8.32468e-06
SP1_HUMAN.H11MO.0.A	0.00014751399999999998
CTCF_HUMAN.H11MO.0.A	0.00022187299999999998
SP3_HUMAN.H11MO.0.B	0.00022187299999999998
MA0139.1_CTCF	0.00239302	Not shown
USF2_HUMAN.H11MO.0.A	0.00239302	Not shown
MA1102.2_CTCFL	0.0038887	Not shown
MA1513.1_KLF15	0.0038887	Not shown
MA1650.1_ZBTB14	0.00428355	Not shown

Motif 9/9

Motif ID	q-val	PWM
DDIT3_HUMAN.H11MO.0.D	0.134948

Metacluster 2/2

Motif 1/13

Motif ID	q-val	PWM
MA1142.1_FOSL1::JUND	2.4502399999999998e-05
MA1130.1_FOSL2::JUN	2.4502399999999998e-05
MA1622.1_Smad2::Smad3	2.4502399999999998e-05
MA0099.3_FOS::JUN	3.42666e-05
FOSL2_HUMAN.H11MO.0.A	3.91618e-05
MA1128.1_FOSL1::JUN	3.91618e-05	Not shown
MA1141.1_FOS::JUND	3.91618e-05	Not shown
JUN_HUMAN.H11MO.0.A	6.63372e-05	Not shown
MA1137.1_FOSL1::JUNB	7.96047e-05	Not shown
NFE2_HUMAN.H11MO.0.A	7.96047e-05	Not shown

Motif 2/13

No TOMTOM matches passing threshold

Motif 3/13

Motif ID	q-val	PWM
ZN563_HUMAN.H11MO.1.C	0.46552299999999996
ZN260_HUMAN.H11MO.0.C	0.46552299999999996
ZN563_HUMAN.H11MO.0.C	0.46552299999999996
MYOD1_HUMAN.H11MO.0.A	0.46552299999999996
MA1529.1_NHLH2	0.46552299999999996
HEN1_HUMAN.H11MO.0.C	0.46552299999999996	Not shown
MA1635.1_BHLHE22(var.2)	0.46552299999999996	Not shown
MA0521.1_Tcf12	0.46552299999999996	Not shown
ZSC31_HUMAN.H11MO.0.C	0.46552299999999996	Not shown
MA1100.2_ASCL1	0.46552299999999996	Not shown

Motif 4/13

Motif ID	q-val	PWM
COE1_HUMAN.H11MO.0.A	0.288522
MA0154.4_EBF1	0.288522
AP2C_HUMAN.H11MO.0.A	0.291954
MA1615.1_Plagl1	0.291954
AP2A_HUMAN.H11MO.0.A	0.291954

Motif 5/13

No TOMTOM matches passing threshold

Motif 6/13

Motif ID	q-val	PWM
MA0631.1_Six3	0.34383400000000003
MA0475.2_FLI1	0.34383400000000003
MA0760.1_ERF	0.34383400000000003
MA0763.1_ETV3	0.34383400000000003

Motif 7/13

Motif ID	q-val	PWM
PTF1A_HUMAN.H11MO.0.B	0.40169

Motif 8/13

Motif ID	q-val	PWM
STAT6_HUMAN.H11MO.0.B	0.304417
AP2C_HUMAN.H11MO.0.A	0.304417
MA0520.1_Stat6	0.304417
COE1_HUMAN.H11MO.0.A	0.304417
MA0154.4_EBF1	0.304417
AP2A_HUMAN.H11MO.0.A	0.304417	Not shown
MA0812.1_TFAP2B(var.2)	0.41914700000000005	Not shown

Motif 9/13

Motif ID	q-val	PWM
PATZ1_HUMAN.H11MO.1.C	0.26215900000000003
ZN436_HUMAN.H11MO.0.C	0.26215900000000003
FLI1_HUMAN.H11MO.0.A	0.42282299999999995
OLIG2_HUMAN.H11MO.0.B	0.42282299999999995
VEZF1_HUMAN.H11MO.1.C	0.42282299999999995

Motif 10/13

No TOMTOM matches passing threshold

Motif 11/13

Motif ID	q-val	PWM
P53_HUMAN.H11MO.1.A	0.245232

Motif 12/13

Motif ID	q-val	PWM
MA0041.1_Foxd3	0.22725599999999999
FOXL1_HUMAN.H11MO.0.D	0.41751000000000005
FOXJ2_HUMAN.H11MO.0.C	0.41751000000000005
MA1487.1_FOXE1	0.41751000000000005
FOXF1_HUMAN.H11MO.0.D	0.41751000000000005
MA0483.1_Gfi1b	0.41751000000000005	Not shown
FOXD2_HUMAN.H11MO.0.D	0.41751000000000005	Not shown
FOXQ1_HUMAN.H11MO.0.C	0.41751000000000005	Not shown
GFI1_HUMAN.H11MO.0.C	0.41751000000000005	Not shown
MGAP_HUMAN.H11MO.0.D	0.41751000000000005	Not shown

Motif 13/13

No TOMTOM matches passing threshold