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_fold9/CEBPB_multitask_profile_fold9_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold9/CEBPB_multitask_profile_fold9_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_fold9/CEBPB_multitask_profile_fold9_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 [05:06<00:00,  1.12s/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

11395 seqlets

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

Pattern 2/12

810 seqlets

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

Pattern 3/12

800 seqlets

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

Pattern 4/12

319 seqlets

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

Pattern 5/12

267 seqlets

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

Pattern 6/12

130 seqlets

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

Pattern 7/12

88 seqlets

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

Pattern 8/12

72 seqlets

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

Pattern 9/12

52 seqlets

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

Pattern 10/12

40 seqlets

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

Pattern 11/12

37 seqlets

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

Pattern 12/12

31 seqlets

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

Metacluster 2/2

Pattern 1/9

698 seqlets

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

Pattern 2/9

417 seqlets

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

Pattern 3/9

201 seqlets

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

Pattern 4/9

188 seqlets

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

Pattern 5/9

182 seqlets

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

Pattern 6/9

162 seqlets

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

Pattern 7/9

149 seqlets

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

Pattern 8/9

137 seqlets

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

Pattern 9/9

88 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	11395
2	810
3	800
4	319
5	267
6	130
7	88
8	72
9	52
10	40
11	37
12	31

Metacluster 2/2

#	Seqlets	Forward	Reverse
1	698
2	417
3	201
4	188
5	182
6	162
7	149
8	137
9	88

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
CEBPB_HUMAN.H11MO.0.A	7.78698e-10
CEBPD_HUMAN.H11MO.0.C	3.7198700000000003e-09
CEBPA_HUMAN.H11MO.0.A	9.78762e-07
MA0836.2_CEBPD	1.80574e-05
MA0102.4_CEBPA	0.00010941399999999999
MA0837.1_CEBPE	0.00033210300000000004	Not shown
MA0466.2_CEBPB	0.000407554	Not shown
MA0838.1_CEBPG	0.000672229	Not shown
MA0025.2_NFIL3	0.00117461	Not shown
DBP_HUMAN.H11MO.0.B	0.00282576	Not shown

Motif 2/12

Motif ID	q-val	PWM
MA0139.1_CTCF	2.1441e-16
CTCF_HUMAN.H11MO.0.A	1.4138499999999999e-14
CTCFL_HUMAN.H11MO.0.A	8.19478e-08
MA1102.2_CTCFL	5.84047e-05
MA1568.1_TCF21(var.2)	0.130365
MA1638.1_HAND2	0.155187	Not shown
ZIC3_HUMAN.H11MO.0.B	0.187029	Not shown
SNAI1_HUMAN.H11MO.0.C	0.187029	Not shown
ZIC2_HUMAN.H11MO.0.D	0.30088000000000004	Not shown
MA1629.1_Zic2	0.301655	Not shown

Motif 3/12

Motif ID	q-val	PWM
JUND_HUMAN.H11MO.0.A	4.5101899999999996e-05
FOSL1_HUMAN.H11MO.0.A	4.5101899999999996e-05
FOSL2_HUMAN.H11MO.0.A	4.5101899999999996e-05
FOSB_HUMAN.H11MO.0.A	6.11144e-05
JUN_HUMAN.H11MO.0.A	9.778309999999999e-05
MA1622.1_Smad2::Smad3	0.000183857	Not shown
FOS_HUMAN.H11MO.0.A	0.000183857	Not shown
NFE2_HUMAN.H11MO.0.A	0.000183857	Not shown
MA1141.1_FOS::JUND	0.000183857	Not shown
MA0099.3_FOS::JUN	0.000183857	Not shown

Motif 4/12

Motif ID	q-val	PWM
HNF4G_HUMAN.H11MO.0.B	1.0699000000000002e-09
HNF4A_HUMAN.H11MO.0.A	1.16957e-09
MA1494.1_HNF4A(var.2)	0.00101815
MA0114.4_HNF4A	0.00101815
MA0484.2_HNF4G	0.00101815
MA0856.1_RXRG	0.00101815	Not shown
MA0677.1_Nr2f6	0.00101815	Not shown
MA1574.1_THRB	0.00101815	Not shown
MA0512.2_Rxra	0.00101815	Not shown
MA1550.1_PPARD	0.00103361	Not shown

Motif 5/12

Motif ID	q-val	PWM
FOXA2_HUMAN.H11MO.0.A	5.15077e-07
FOXA1_HUMAN.H11MO.0.A	5.15077e-07
FOXF2_HUMAN.H11MO.0.D	6.51224e-06
FOXA3_HUMAN.H11MO.0.B	8.59662e-06
FOXM1_HUMAN.H11MO.0.A	8.59662e-06
FOXD3_HUMAN.H11MO.0.D	8.85714e-06	Not shown
MA0846.1_FOXC2	8.85714e-06	Not shown
MA0847.2_FOXD2	3.93865e-05	Not shown
FOXC1_HUMAN.H11MO.0.C	5.2515299999999995e-05	Not shown
MA0032.2_FOXC1	0.000170166	Not shown

Motif 6/12

Motif ID	q-val	PWM
GATA2_HUMAN.H11MO.1.A	1.1509700000000001e-05
GATA4_HUMAN.H11MO.0.A	3.45556e-05
GATA1_HUMAN.H11MO.1.A	3.45556e-05
GATA2_HUMAN.H11MO.0.A	0.00012331200000000001
MA0036.3_GATA2	0.00012331200000000001
MA0037.3_GATA3	0.000174787	Not shown
GATA1_HUMAN.H11MO.0.A	0.000174787	Not shown
GATA6_HUMAN.H11MO.0.A	0.00023035900000000002	Not shown
GATA3_HUMAN.H11MO.0.A	0.000434017	Not shown
MA0482.2_GATA4	0.000988157	Not shown

Motif 7/12

Motif ID	q-val	PWM
FOXA2_HUMAN.H11MO.0.A	0.00308011
FOXA1_HUMAN.H11MO.0.A	0.00477097
FOXC1_HUMAN.H11MO.0.C	0.00477097
FOXD3_HUMAN.H11MO.0.D	0.00477097
FOXF2_HUMAN.H11MO.0.D	0.00640774
FOXA3_HUMAN.H11MO.0.B	0.00653083	Not shown
MA0846.1_FOXC2	0.00705572	Not shown
MA0847.2_FOXD2	0.00705572	Not shown
FOXD1_HUMAN.H11MO.0.D	0.00738017	Not shown
MA0850.1_FOXP3	0.00927492	Not shown

Motif 8/12

Motif ID	q-val	PWM
ELK1_HUMAN.H11MO.0.B	0.0238544
MA0474.2_ERG	0.0238544
MA0763.1_ETV3	0.0238544
MA0076.2_ELK4	0.0238544
MA0028.2_ELK1	0.0238544
MA0475.2_FLI1	0.0238544	Not shown
CTCFL_HUMAN.H11MO.0.A	0.0238544	Not shown
MA0760.1_ERF	0.0238544	Not shown
MA0765.2_ETV5	0.0274437	Not shown
MA0156.2_FEV	0.028758799999999998	Not shown

Motif 9/12

Motif ID	q-val	PWM
FOXM1_HUMAN.H11MO.0.A	0.196156
FOXF2_HUMAN.H11MO.0.D	0.33722199999999997
MA0029.1_Mecom	0.427317
FOXA2_HUMAN.H11MO.0.A	0.427317
MA0789.1_POU3F4	0.46414399999999995
MA0851.1_Foxj3	0.482278	Not shown
MA0847.2_FOXD2	0.482278	Not shown
FOXD3_HUMAN.H11MO.0.D	0.482278	Not shown
VSX1_HUMAN.H11MO.0.D	0.482278	Not shown
MA0843.1_TEF	0.482278	Not shown

Motif 10/12

Motif ID	q-val	PWM
CEBPB_HUMAN.H11MO.0.A	0.48603100000000005
CEBPA_HUMAN.H11MO.0.A	0.48603100000000005
ARI5B_HUMAN.H11MO.0.C	0.48603100000000005

Motif 11/12

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	2.29105e-06
MA1513.1_KLF15	2.29105e-06
SP1_HUMAN.H11MO.0.A	7.178439999999999e-06
SP1_HUMAN.H11MO.1.A	0.000156449
KLF6_HUMAN.H11MO.0.A	0.000170754
KLF3_HUMAN.H11MO.0.B	0.000170754	Not shown
SP3_HUMAN.H11MO.0.B	0.00020266599999999997	Not shown
MA0599.1_KLF5	0.000275656	Not shown
KLF12_HUMAN.H11MO.0.C	0.000275656	Not shown
SP2_HUMAN.H11MO.1.B	0.000275656	Not shown

Motif 12/12

Motif ID	q-val	PWM
ETV6_HUMAN.H11MO.0.D	0.0234349
TEAD2_HUMAN.H11MO.0.D	0.138802
PURA_HUMAN.H11MO.0.D	0.138802
MA0764.2_ETV4	0.138802
FLI1_HUMAN.H11MO.1.A	0.138802
ETS1_HUMAN.H11MO.0.A	0.138802	Not shown
ETV7_HUMAN.H11MO.0.D	0.138802	Not shown
MA0062.3_GABPA	0.138802	Not shown
ERG_HUMAN.H11MO.0.A	0.138802	Not shown
MA0808.1_TEAD3	0.15951600000000002	Not shown

Metacluster 2/2

Motif 1/9

No TOMTOM matches passing threshold

Motif 2/9

Motif ID	q-val	PWM
CEBPD_HUMAN.H11MO.0.C	0.00157666
CEBPB_HUMAN.H11MO.0.A	0.00157666
MA0466.2_CEBPB	0.00410069
MA0837.1_CEBPE	0.00410069
MA0838.1_CEBPG	0.017011099999999998
MA1636.1_CEBPG(var.2)	0.0248974	Not shown
CEBPG_HUMAN.H11MO.0.B	0.029115499999999996	Not shown
ATF4_HUMAN.H11MO.0.A	0.029115499999999996	Not shown
MA0043.3_HLF	0.029115499999999996	Not shown
BATF_HUMAN.H11MO.1.A	0.029115499999999996	Not shown

Motif 3/9

No TOMTOM matches passing threshold

Motif 4/9

Motif ID	q-val	PWM
EOMES_HUMAN.H11MO.0.D	0.43589399999999995
SCRT1_HUMAN.H11MO.0.D	0.43589399999999995
MA0744.2_SCRT2	0.43589399999999995
MA0743.2_SCRT1	0.43589399999999995

Motif 5/9

No TOMTOM matches passing threshold

Motif 6/9

No TOMTOM matches passing threshold

Motif 7/9

No TOMTOM matches passing threshold

Motif 8/9

No TOMTOM matches passing threshold

Motif 9/9

No TOMTOM matches passing threshold