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: FOXA2
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/FOXA2_multitask_profile_fold9/FOXA2_multitask_profile_fold9_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/FOXA2_multitask_profile_fold9/FOXA2_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/FOXA2_multitask_profile_fold9/FOXA2_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%|██████████| 174/174 [01:25<00:00,  2.04it/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")

Metacluster 1/2

Pattern 1/13

4874 seqlets

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

Pattern 2/13

2177 seqlets

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

Pattern 3/13

1910 seqlets

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

Pattern 4/13

1804 seqlets

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

Pattern 5/13

773 seqlets

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

Pattern 6/13

619 seqlets

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

Pattern 7/13

349 seqlets

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

Pattern 8/13

187 seqlets

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

Pattern 9/13

169 seqlets

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

Pattern 10/13

132 seqlets

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

Pattern 11/13

71 seqlets

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

Pattern 12/13

47 seqlets

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

Pattern 13/13

43 seqlets

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

Metacluster 2/2

Pattern 1/5

174 seqlets

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

Pattern 2/5

112 seqlets

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

Pattern 3/5

78 seqlets

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

Pattern 4/5

66 seqlets

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

Pattern 5/5

65 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	4874
2	2177
3	1910
4	1804
5	773
6	619
7	349
8	187
9	169
10	132
11	71
12	47
13	43

Metacluster 2/2

#	Seqlets	Forward	Reverse
1	174
2	112
3	78
4	66
5	65

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 1/13

Motif ID	q-val	PWM
FOXA2_HUMAN.H11MO.0.A	8.16819e-07
FOXA3_HUMAN.H11MO.0.B	8.16819e-07
FOXA1_HUMAN.H11MO.0.A	1.91473e-06
MA0846.1_FOXC2	4.46166e-06
FOXD3_HUMAN.H11MO.0.D	1.55887e-05
FOXF2_HUMAN.H11MO.0.D	1.55887e-05	Not shown
MA1607.1_Foxl2	1.55887e-05	Not shown
FOXC1_HUMAN.H11MO.0.C	3.52943e-05	Not shown
MA0852.2_FOXK1	3.7800500000000005e-05	Not shown
MA0032.2_FOXC1	4.8306e-05	Not shown

Motif 2/13

Motif ID	q-val	PWM
FOXM1_HUMAN.H11MO.0.A	0.000190184
FOXJ2_HUMAN.H11MO.0.C	0.00442274
MA1487.1_FOXE1	0.00442274
FOXA1_HUMAN.H11MO.0.A	0.00672123
FOXF2_HUMAN.H11MO.0.D	0.00691389
FOXA2_HUMAN.H11MO.0.A	0.00724774	Not shown
MA0846.1_FOXC2	0.00724774	Not shown
MA0847.2_FOXD2	0.00724774	Not shown
FOXD3_HUMAN.H11MO.0.D	0.00948053	Not shown
MA0041.1_Foxd3	0.010178399999999999	Not shown

Motif 3/13

Motif ID	q-val	PWM
MA0845.1_FOXB1	1.15342e-05
MA0032.2_FOXC1	8.05454e-05
FOXD2_HUMAN.H11MO.0.D	0.00018234299999999999
FOXB1_HUMAN.H11MO.0.D	0.000231785
MA0846.1_FOXC2	0.00330034
FOXA3_HUMAN.H11MO.0.B	0.00330034	Not shown
FOXA1_HUMAN.H11MO.0.A	0.00330034	Not shown
FOXA2_HUMAN.H11MO.0.A	0.00330034	Not shown
MA0847.2_FOXD2	0.00799383	Not shown
FOXF2_HUMAN.H11MO.0.D	0.00799383	Not shown

Motif 4/13

Motif ID	q-val	PWM
HNF4A_HUMAN.H11MO.0.A	0.000124464
HNF4G_HUMAN.H11MO.0.B	0.000124464
RXRG_HUMAN.H11MO.0.B	0.000426104
MA0856.1_RXRG	0.000426104
MA0855.1_RXRB	0.000426104
MA0512.2_Rxra	0.000426104	Not shown
MA0677.1_Nr2f6	0.000426104	Not shown
MA1550.1_PPARD	0.000426104	Not shown
MA1574.1_THRB	0.000426104	Not shown
MA1537.1_NR2F1(var.2)	0.000578905	Not shown

Motif 5/13

Motif ID	q-val	PWM
MA0466.2_CEBPB	8.17975e-06
MA0837.1_CEBPE	8.17975e-06
MA0838.1_CEBPG	4.4412799999999996e-05
HLF_HUMAN.H11MO.0.C	0.00046509599999999997
CEBPD_HUMAN.H11MO.0.C	0.0007779089999999999
CEBPB_HUMAN.H11MO.0.A	0.000876087	Not shown
MA0836.2_CEBPD	0.00329063	Not shown
MA0639.1_DBP	0.00424295	Not shown
CEBPA_HUMAN.H11MO.0.A	0.00514674	Not shown
MA0843.1_TEF	0.00514674	Not shown

Motif 6/13

Motif ID	q-val	PWM
FOXD2_HUMAN.H11MO.0.D	0.000476166
FOXB1_HUMAN.H11MO.0.D	0.018296200000000002
MA0845.1_FOXB1	0.0615355
MA0032.2_FOXC1	0.0615355
MA0846.1_FOXC2	0.27260300000000004
MA0148.4_FOXA1	0.318642	Not shown
FOXJ2_HUMAN.H11MO.0.C	0.382791	Not shown
PO4F3_HUMAN.H11MO.0.D	0.382791	Not shown
LMX1A_HUMAN.H11MO.0.D	0.46964399999999995	Not shown
MA0481.3_FOXP1	0.46964399999999995	Not shown

Motif 7/13

Motif ID	q-val	PWM
MA0489.1_JUN(var.2)	9.892299999999999e-05
MA1135.1_FOSB::JUNB	9.892299999999999e-05
MA1138.1_FOSL2::JUNB	9.892299999999999e-05
MA1144.1_FOSL2::JUND	9.892299999999999e-05
MA0099.3_FOS::JUN	9.892299999999999e-05
JUN_HUMAN.H11MO.0.A	9.892299999999999e-05	Not shown
FOSB_HUMAN.H11MO.0.A	9.892299999999999e-05	Not shown
FOSL1_HUMAN.H11MO.0.A	0.00010931299999999999	Not shown
MA0476.1_FOS	0.000136479	Not shown
FOSL2_HUMAN.H11MO.0.A	0.000148886	Not shown

Motif 8/13

Motif ID	q-val	PWM
FOXM1_HUMAN.H11MO.0.A	0.0332965
MA1115.1_POU5F1	0.0517695
MA0627.2_POU2F3	0.0517695
MA0845.1_FOXB1	0.0616422
MA0032.2_FOXC1	0.0616422
FOXA1_HUMAN.H11MO.0.A	0.0638824	Not shown
FOXJ2_HUMAN.H11MO.0.C	0.06500059999999999	Not shown
MA0847.2_FOXD2	0.06500059999999999	Not shown
MA0792.1_POU5F1B	0.06500059999999999	Not shown
FOXD2_HUMAN.H11MO.0.D	0.06500059999999999	Not shown

Motif 9/13

Motif ID	q-val	PWM
NR4A3_HUMAN.H11MO.0.D	0.00295347
RXRA_HUMAN.H11MO.1.A	0.00295347
PPARA_HUMAN.H11MO.1.B	0.00295347
PPARG_HUMAN.H11MO.1.A	0.00295347
MA0856.1_RXRG	0.00295347
MA0512.2_Rxra	0.00295347	Not shown
MA0855.1_RXRB	0.00295347	Not shown
MA0677.1_Nr2f6	0.00295347	Not shown
HNF4G_HUMAN.H11MO.0.B	0.00407398	Not shown
RXRG_HUMAN.H11MO.0.B	0.00460365	Not shown

Motif 10/13

Motif ID	q-val	PWM
HNF4G_HUMAN.H11MO.0.B	4.26018e-05
HNF4A_HUMAN.H11MO.0.A	5.37033e-05
RXRG_HUMAN.H11MO.0.B	0.00136242
MA0114.4_HNF4A	0.00137727
MA1537.1_NR2F1(var.2)	0.00137727
MA0484.2_HNF4G	0.00201916	Not shown
MA1550.1_PPARD	0.00202786	Not shown
MA0677.1_Nr2f6	0.00237806	Not shown
MA0065.2_Pparg::Rxra	0.00257538	Not shown
MA1111.1_NR2F2	0.00331422	Not shown

Motif 11/13

Motif ID	q-val	PWM
BATF_HUMAN.H11MO.1.A	0.015334200000000001
CEBPE_HUMAN.H11MO.0.A	0.015334200000000001
MA0489.1_JUN(var.2)	0.0319416
MA0466.2_CEBPB	0.0319416
CEBPD_HUMAN.H11MO.0.C	0.0319416
ATF4_HUMAN.H11MO.0.A	0.0319416	Not shown
MA1132.1_JUN::JUNB	0.0319416	Not shown
MA0837.1_CEBPE	0.0319416	Not shown
ATF1_HUMAN.H11MO.0.B	0.0328851	Not shown
MA1142.1_FOSL1::JUND	0.038197699999999994	Not shown

Motif 12/13

Motif ID	q-val	PWM
CPEB1_HUMAN.H11MO.0.D	4.64521e-05
PRDM6_HUMAN.H11MO.0.C	0.0322767
MA1125.1_ZNF384	0.0322767
FOXL1_HUMAN.H11MO.0.D	0.0359834
FOXG1_HUMAN.H11MO.0.D	0.070518
ANDR_HUMAN.H11MO.0.A	0.141237	Not shown
MA0679.2_ONECUT1	0.151193	Not shown
FOXJ3_HUMAN.H11MO.0.A	0.151193	Not shown
FUBP1_HUMAN.H11MO.0.D	0.21971999999999997	Not shown
ONEC2_HUMAN.H11MO.0.D	0.21971999999999997	Not shown

Motif 13/13

Motif ID	q-val	PWM
FOXM1_HUMAN.H11MO.0.A	0.00596077
ANDR_HUMAN.H11MO.0.A	0.00596077
FOXJ2_HUMAN.H11MO.0.C	0.0112227
MA1487.1_FOXE1	0.013716999999999998
FOXA1_HUMAN.H11MO.0.A	0.0251363
CPEB1_HUMAN.H11MO.0.D	0.0251363	Not shown
MA0679.2_ONECUT1	0.027306200000000003	Not shown
MA0041.1_Foxd3	0.028996299999999996	Not shown
FOXF2_HUMAN.H11MO.0.D	0.034201	Not shown
FOXA2_HUMAN.H11MO.0.A	0.038974199999999994	Not shown

Metacluster 2/2

Motif 1/5

No TOMTOM matches passing threshold

Motif 2/5

Motif ID	q-val	PWM
MA0849.1_FOXO6	0.0092981
MA0042.2_FOXI1	0.0092981
MA0850.1_FOXP3	0.0092981
FOXA2_HUMAN.H11MO.0.A	0.0092981
FOXM1_HUMAN.H11MO.0.A	0.0092981
MA0848.1_FOXO4	0.0092981	Not shown
FOXA1_HUMAN.H11MO.0.A	0.0102678	Not shown
FOXF2_HUMAN.H11MO.0.D	0.0102678	Not shown
MA0033.2_FOXL1	0.0102678	Not shown
MA0157.2_FOXO3	0.0102678	Not shown

Motif 3/5

No TOMTOM matches passing threshold

Motif 4/5

No TOMTOM matches passing threshold

Motif 5/5

No TOMTOM matches passing threshold