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_fold8/JUND_multitask_profile_fold8_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/JUND_multitask_profile_fold8/JUND_multitask_profile_fold8_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/JUND_multitask_profile_fold8/JUND_multitask_profile_fold8_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%|██████████| 350/350 [04:14<00:00,  1.38it/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 1/7

5831 seqlets

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

Pattern 2/7

1504 seqlets

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

Pattern 3/7

1456 seqlets

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

Pattern 4/7

254 seqlets

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

Pattern 5/7

142 seqlets

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

Pattern 6/7

125 seqlets

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

Pattern 7/7

60 seqlets

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

Metacluster 2/2

Pattern 1/12

97 seqlets

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

Pattern 2/12

90 seqlets

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

Pattern 3/12

80 seqlets

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

Pattern 4/12

77 seqlets

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

Pattern 5/12

71 seqlets

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

Pattern 6/12

70 seqlets

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

Pattern 7/12

69 seqlets

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

Pattern 8/12

66 seqlets

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

Pattern 9/12

53 seqlets

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

Pattern 10/12

53 seqlets

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

Pattern 11/12

43 seqlets

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

Pattern 12/12

32 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	5831
2	1504
3	1456
4	254
5	142
6	125
7	60

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	97
2	90
3	80
4	77
5	71
6	70
7	69
8	66
9	53
10	53
11	43
12	32

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 1/7

Motif ID	q-val	PWM
JUN_HUMAN.H11MO.0.A	1.78232e-07
FOSL2_HUMAN.H11MO.0.A	1.78232e-07
FOSB_HUMAN.H11MO.0.A	6.4783e-07
FOSL1_HUMAN.H11MO.0.A	6.4783e-07
JUND_HUMAN.H11MO.0.A	1.4218200000000002e-06
MA0099.3_FOS::JUN	2.1314e-06	Not shown
MA1130.1_FOSL2::JUN	3.5523400000000005e-06	Not shown
MA1141.1_FOS::JUND	3.5523400000000005e-06	Not shown
MA1128.1_FOSL1::JUN	3.5523400000000005e-06	Not shown
MA1622.1_Smad2::Smad3	8.662539999999998e-06	Not shown

Motif 2/7

Motif ID	q-val	PWM
MA0605.2_ATF3	2.65757e-06
MA1475.1_CREB3L4(var.2)	2.65757e-06
MA1136.1_FOSB::JUNB(var.2)	2.85594e-06
MA1129.1_FOSL1::JUN(var.2)	6.14592e-06
MA1126.1_FOS::JUN(var.2)	6.14592e-06
MA1145.1_FOSL2::JUND(var.2)	6.169249999999999e-06	Not shown
ATF2_HUMAN.H11MO.0.B	6.169249999999999e-06	Not shown
JDP2_HUMAN.H11MO.0.D	6.169249999999999e-06	Not shown
MA1131.1_FOSL2::JUN(var.2)	6.65388e-06	Not shown
MA1139.1_FOSL2::JUNB(var.2)	6.65388e-06	Not shown

Motif 3/7

Motif ID	q-val	PWM
CEBPD_HUMAN.H11MO.0.C	0.00022495299999999999
CEBPB_HUMAN.H11MO.0.A	0.00022495299999999999
MA0466.2_CEBPB	0.00022495299999999999
MA0837.1_CEBPE	0.00022495299999999999
MA1636.1_CEBPG(var.2)	0.000499585
ATF4_HUMAN.H11MO.0.A	0.000499585	Not shown
MA0838.1_CEBPG	0.000499585	Not shown
MA0025.2_NFIL3	0.000499585	Not shown
MA0833.2_ATF4	0.000499585	Not shown
CEBPG_HUMAN.H11MO.0.B	0.000510555	Not shown

Motif 4/7

Motif ID	q-val	PWM
FOXA2_HUMAN.H11MO.0.A	8.41194e-07
FOXA1_HUMAN.H11MO.0.A	0.00043556
MA0849.1_FOXO6	0.0007883839999999999
FOXA3_HUMAN.H11MO.0.B	0.0007883839999999999
MA0846.1_FOXC2	0.00104911
MA0042.2_FOXI1	0.00128264	Not shown
MA0032.2_FOXC1	0.00128264	Not shown
FOXD3_HUMAN.H11MO.0.D	0.00128264	Not shown
FOXF2_HUMAN.H11MO.0.D	0.00128264	Not shown
MA0847.2_FOXD2	0.00140679	Not shown

Motif 5/7

Motif ID	q-val	PWM
FOXA2_HUMAN.H11MO.0.A	4.88516e-06
FOXA3_HUMAN.H11MO.0.B	2.15948e-05
FOXA1_HUMAN.H11MO.0.A	2.68068e-05
MA0846.1_FOXC2	2.68068e-05
MA0032.2_FOXC1	5.4593500000000005e-05
MA1607.1_Foxl2	0.000153175	Not shown
FOXD3_HUMAN.H11MO.0.D	0.000185375	Not shown
MA0852.2_FOXK1	0.000185375	Not shown
FOXF2_HUMAN.H11MO.0.D	0.000185375	Not shown
FOXC1_HUMAN.H11MO.0.C	0.000247981	Not shown

Motif 6/7

Motif ID	q-val	PWM
CPEB1_HUMAN.H11MO.0.D	1.22874e-05
FOXL1_HUMAN.H11MO.0.D	0.00833936
MA1125.1_ZNF384	0.00833936
PRDM6_HUMAN.H11MO.0.C	0.00833936
FOXG1_HUMAN.H11MO.0.D	0.00833936
FOXJ3_HUMAN.H11MO.0.A	0.016161500000000002	Not shown
FUBP1_HUMAN.H11MO.0.D	0.044620099999999996	Not shown
FOXJ3_HUMAN.H11MO.1.B	0.06105359999999999	Not shown
MA0679.2_ONECUT1	0.061275300000000005	Not shown
ANDR_HUMAN.H11MO.0.A	0.07447089999999999	Not shown

Motif 7/7

Motif ID	q-val	PWM
USF2_HUMAN.H11MO.0.A	0.021659
SP2_HUMAN.H11MO.0.A	0.0358103
MA0814.2_TFAP2C(var.2)	0.037198400000000006
MA1583.1_ZFP57	0.037198400000000006
ATF3_HUMAN.H11MO.0.A	0.0933573
MA0003.4_TFAP2A	0.11378599999999998	Not shown
MA1615.1_Plagl1	0.11378599999999998	Not shown
THB_HUMAN.H11MO.1.D	0.11378599999999998	Not shown
MBD2_HUMAN.H11MO.0.B	0.131886	Not shown
SP3_HUMAN.H11MO.0.B	0.135766	Not shown

Metacluster 2/2

Motif 1/12

Motif ID	q-val	PWM
KLF5_HUMAN.H11MO.0.A	0.0111383
MA0154.4_EBF1	0.40158299999999997
ZN331_HUMAN.H11MO.0.C	0.40158299999999997
THA11_HUMAN.H11MO.0.B	0.40158299999999997
MA1615.1_Plagl1	0.40158299999999997
COE1_HUMAN.H11MO.0.A	0.40158299999999997	Not shown
MA1625.1_Stat5b	0.40158299999999997	Not shown
MA1597.1_ZNF528	0.415238	Not shown
ZN121_HUMAN.H11MO.0.C	0.45421999999999996	Not shown

Motif 2/12

No TOMTOM matches passing threshold

Motif 3/12

Motif ID	q-val	PWM
ZN563_HUMAN.H11MO.0.C	0.156315

Motif 4/12

Motif ID	q-val	PWM
MA0655.1_JDP2	0.000140145
MA1135.1_FOSB::JUNB	0.000140145
MA1138.1_FOSL2::JUNB	0.000140145
MA1144.1_FOSL2::JUND	0.000140145
MA0478.1_FOSL2	0.000140145
MA1134.1_FOS::JUNB	0.000140145	Not shown
MA0841.1_NFE2	0.00035558800000000005	Not shown
MA0491.2_JUND	0.000435595	Not shown
MA0489.1_JUN(var.2)	0.00044251	Not shown
MA0099.3_FOS::JUN	0.00046532699999999997	Not shown

Motif 5/12

Motif ID	q-val	PWM
PLAL1_HUMAN.H11MO.0.D	0.055915999999999993
AP2B_HUMAN.H11MO.0.B	0.191152
SP1_HUMAN.H11MO.0.A	0.25714499999999996
MA1615.1_Plagl1	0.25714499999999996
MA1596.1_ZNF460	0.25714499999999996
NR1H4_HUMAN.H11MO.0.B	0.25714499999999996	Not shown
MA1604.1_Ebf2	0.348433	Not shown
USF2_HUMAN.H11MO.0.A	0.348433	Not shown
MA0778.1_NFKB2	0.348433	Not shown
SP2_HUMAN.H11MO.0.A	0.348433	Not shown

Motif 6/12

Motif ID	q-val	PWM
TBX15_HUMAN.H11MO.0.D	0.000167712
SP2_HUMAN.H11MO.0.A	0.000191075
SP3_HUMAN.H11MO.0.B	0.000584933
MA0073.1_RREB1	0.00125325
RREB1_HUMAN.H11MO.0.D	0.00181713
SP1_HUMAN.H11MO.0.A	0.00181713	Not shown
KLF3_HUMAN.H11MO.0.B	0.00486916	Not shown
EGR1_HUMAN.H11MO.0.A	0.00486916	Not shown
KLF6_HUMAN.H11MO.0.A	0.00486916	Not shown
EGR2_HUMAN.H11MO.0.A	0.00728066	Not shown

Motif 7/12

No TOMTOM matches passing threshold

Motif 8/12

No TOMTOM matches passing threshold

Motif 9/12

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	0.0795245
KLF3_HUMAN.H11MO.0.B	0.228542
SP3_HUMAN.H11MO.0.B	0.228542
ZFX_HUMAN.H11MO.1.A	0.41806099999999996
USF2_HUMAN.H11MO.0.A	0.451212
SP1_HUMAN.H11MO.1.A	0.451212	Not shown
SP1_HUMAN.H11MO.0.A	0.48579799999999995	Not shown

Motif 10/12

Motif ID	q-val	PWM
FOSL1_HUMAN.H11MO.0.A	0.0127022
MA0478.1_FOSL2	0.0127022
FOSL2_HUMAN.H11MO.0.A	0.0127022
FOS_HUMAN.H11MO.0.A	0.0221322
JUND_HUMAN.H11MO.0.A	0.0221322
MA0476.1_FOS	0.026692900000000002	Not shown
MA1141.1_FOS::JUND	0.0416241	Not shown
MA1130.1_FOSL2::JUN	0.0416241	Not shown
FOSB_HUMAN.H11MO.0.A	0.0416241	Not shown
JUN_HUMAN.H11MO.0.A	0.04221880000000001	Not shown

Motif 11/12

No TOMTOM matches passing threshold

Motif 12/12

No TOMTOM matches passing threshold