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_fold6/CEBPB_multitask_profile_fold6_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold6/CEBPB_multitask_profile_fold6_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/CEBPB_multitask_profile_fold6/CEBPB_multitask_profile_fold6_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%|██████████| 273/273 [06:41<00:00,  1.47s/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 1/15

9853 seqlets

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

Pattern 2/15

879 seqlets

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

Pattern 3/15

672 seqlets

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

Pattern 4/15

565 seqlets

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

Pattern 5/15

219 seqlets

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

Pattern 6/15

176 seqlets

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

Pattern 7/15

115 seqlets

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

Pattern 8/15

86 seqlets

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

Pattern 9/15

85 seqlets

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

Pattern 10/15

84 seqlets

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

Pattern 11/15

64 seqlets

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

Pattern 12/15

53 seqlets

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

Pattern 13/15

51 seqlets

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

Pattern 14/15

48 seqlets

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

Pattern 15/15

40 seqlets

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

Metacluster 2/2

Pattern 1/5

414 seqlets

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

Pattern 2/5

373 seqlets

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

Pattern 3/5

294 seqlets

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

Pattern 4/5

182 seqlets

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

Pattern 5/5

99 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	9853
2	879
3	672
4	565
5	219
6	176
7	115
8	86
9	85
10	84
11	64
12	53
13	51
14	48
15	40

Metacluster 2/2

#	Seqlets	Forward	Reverse
1	414
2	373
3	294
4	182
5	99

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 1/15

Motif ID	q-val	PWM
CEBPB_HUMAN.H11MO.0.A	9.09828e-11
CEBPD_HUMAN.H11MO.0.C	3.1532e-09
CEBPA_HUMAN.H11MO.0.A	2.96964e-07
MA0836.2_CEBPD	1.42208e-05
MA0102.4_CEBPA	0.000131652
MA0837.1_CEBPE	0.000515358	Not shown
MA0466.2_CEBPB	0.00069912	Not shown
MA0838.1_CEBPG	0.0009277160000000001	Not shown
MA0025.2_NFIL3	0.00140549	Not shown
DBP_HUMAN.H11MO.0.B	0.0030775	Not shown

Motif 2/15

Motif ID	q-val	PWM
FOSL2_HUMAN.H11MO.0.A	1.29336e-05
JUND_HUMAN.H11MO.0.A	2.5061599999999997e-05
JUN_HUMAN.H11MO.0.A	2.5061599999999997e-05
MA1128.1_FOSL1::JUN	4.5441999999999996e-05
MA1141.1_FOS::JUND	4.5441999999999996e-05
NFE2_HUMAN.H11MO.0.A	4.5441999999999996e-05	Not shown
FOSB_HUMAN.H11MO.0.A	4.5441999999999996e-05	Not shown
BACH2_HUMAN.H11MO.0.A	4.5441999999999996e-05	Not shown
FOSL1_HUMAN.H11MO.0.A	4.5441999999999996e-05	Not shown
MA1622.1_Smad2::Smad3	4.62441e-05	Not shown

Motif 3/15

Motif ID	q-val	PWM
MA0139.1_CTCF	1.88923e-13
CTCF_HUMAN.H11MO.0.A	1.09328e-11
CTCFL_HUMAN.H11MO.0.A	5.8113e-06
MA1102.2_CTCFL	0.00736156
MA1568.1_TCF21(var.2)	0.042113599999999994
MA1638.1_HAND2	0.0471792	Not shown
ZIC3_HUMAN.H11MO.0.B	0.17940899999999999	Not shown
SNAI1_HUMAN.H11MO.0.C	0.21061799999999997	Not shown
ZIC2_HUMAN.H11MO.0.D	0.275435	Not shown
MA1629.1_Zic2	0.359033	Not shown

Motif 4/15

Motif ID	q-val	PWM
FOXA1_HUMAN.H11MO.0.A	5.90379e-08
FOXA2_HUMAN.H11MO.0.A	7.5677e-08
FOXF2_HUMAN.H11MO.0.D	9.24665e-07
FOXA3_HUMAN.H11MO.0.B	1.3869999999999998e-06
MA0846.1_FOXC2	2.3547299999999996e-06
FOXM1_HUMAN.H11MO.0.A	3.1302099999999995e-06	Not shown
FOXD3_HUMAN.H11MO.0.D	3.3638999999999997e-06	Not shown
MA0847.2_FOXD2	1.0894100000000001e-05	Not shown
FOXC1_HUMAN.H11MO.0.C	1.45254e-05	Not shown
FOXD1_HUMAN.H11MO.0.D	0.000216176	Not shown

Motif 5/15

Motif ID	q-val	PWM
CEBPE_HUMAN.H11MO.0.A	0.185117
MA1635.1_BHLHE22(var.2)	0.185117
KLF3_HUMAN.H11MO.0.B	0.185117
SP4_HUMAN.H11MO.0.A	0.185117
MA0838.1_CEBPG	0.185117
ZN770_HUMAN.H11MO.1.C	0.185117	Not shown
MA0466.2_CEBPB	0.185117	Not shown
MA0837.1_CEBPE	0.185117	Not shown
ZN423_HUMAN.H11MO.0.D	0.185117	Not shown
THAP1_HUMAN.H11MO.0.C	0.185117	Not shown

Motif 6/15

Motif ID	q-val	PWM
TBX15_HUMAN.H11MO.0.D	5.82938e-06
SP3_HUMAN.H11MO.0.B	1.4663699999999998e-05
SP2_HUMAN.H11MO.0.A	2.89085e-05
KLF16_HUMAN.H11MO.0.D	3.7296300000000004e-05
ZN467_HUMAN.H11MO.0.C	3.86827e-05
WT1_HUMAN.H11MO.0.C	5.42217e-05	Not shown
ZN341_HUMAN.H11MO.0.C	5.99876e-05	Not shown
SP1_HUMAN.H11MO.0.A	6.75099e-05	Not shown
VEZF1_HUMAN.H11MO.0.C	6.79637e-05	Not shown
MAZ_HUMAN.H11MO.0.A	7.56077e-05	Not shown

Motif 7/15

Motif ID	q-val	PWM
HNF4G_HUMAN.H11MO.0.B	2.49607e-08
HNF4A_HUMAN.H11MO.0.A	3.88255e-08
MA1148.1_PPARA::RXRA	0.00659133
MA1574.1_THRB	0.00659133
MA1550.1_PPARD	0.00659133
MA1494.1_HNF4A(var.2)	0.00659133	Not shown
MA0677.1_Nr2f6	0.00659133	Not shown
MA0855.1_RXRB	0.00659133	Not shown
PPARA_HUMAN.H11MO.0.B	0.00659133	Not shown
MA0856.1_RXRG	0.00659133	Not shown

Motif 8/15

Motif ID	q-val	PWM
CPEB1_HUMAN.H11MO.0.D	7.130319999999999e-06
MA1125.1_ZNF384	0.00656623
FOXL1_HUMAN.H11MO.0.D	0.014518600000000001
PRDM6_HUMAN.H11MO.0.C	0.014518600000000001
FOXG1_HUMAN.H11MO.0.D	0.014518600000000001
MA0679.2_ONECUT1	0.028269599999999995	Not shown
HXC10_HUMAN.H11MO.0.D	0.0341685	Not shown
FOXJ3_HUMAN.H11MO.0.A	0.053549099999999995	Not shown
ONEC2_HUMAN.H11MO.0.D	0.053549099999999995	Not shown
ANDR_HUMAN.H11MO.0.A	0.0550601	Not shown

Motif 9/15

Motif ID	q-val	PWM
GATA4_HUMAN.H11MO.0.A	0.0338139
GATA1_HUMAN.H11MO.0.A	0.0338139
GATA1_HUMAN.H11MO.1.A	0.052435800000000005
GATA2_HUMAN.H11MO.1.A	0.052435800000000005
MA0037.3_GATA3	0.07294289999999999
TAL1_HUMAN.H11MO.0.A	0.07294289999999999	Not shown
MA0036.3_GATA2	0.07294289999999999	Not shown
MA0482.2_GATA4	0.110225	Not shown
GATA3_HUMAN.H11MO.0.A	0.110765	Not shown
MA0766.2_GATA5	0.193096	Not shown

Motif 10/15

Motif ID	q-val	PWM
HNF4A_HUMAN.H11MO.0.A	0.000536276
HNF4G_HUMAN.H11MO.0.B	0.000536276
NR2E3_HUMAN.H11MO.0.C	0.00384659
MA0114.4_HNF4A	0.0151019
MA0484.2_HNF4G	0.0151019
MA0115.1_NR1H2::RXRA	0.0151019	Not shown
MA1494.1_HNF4A(var.2)	0.0151019	Not shown
NR1H2_HUMAN.H11MO.0.D	0.0189729	Not shown
MA0857.1_Rarb	0.0260733	Not shown
MA1111.1_NR2F2	0.0260733	Not shown

Motif 11/15

Motif ID	q-val	PWM
CEBPE_HUMAN.H11MO.0.A	0.0158623
ZN264_HUMAN.H11MO.0.C	0.053132500000000006
MA1104.2_GATA6	0.0812456
CREB1_HUMAN.H11MO.0.A	0.138381
MA0756.1_ONECUT2	0.284376
CPEB1_HUMAN.H11MO.0.D	0.284376	Not shown
ATF1_HUMAN.H11MO.0.B	0.284376	Not shown
LHX9_HUMAN.H11MO.0.D	0.284376	Not shown
MA1131.1_FOSL2::JUN(var.2)	0.305537	Not shown
MA1127.1_FOSB::JUN	0.327573	Not shown

Motif 12/15

Motif ID	q-val	PWM
ZN770_HUMAN.H11MO.0.C	1.53398e-07
MA1596.1_ZNF460	1.7542100000000002e-07
ZN770_HUMAN.H11MO.1.C	0.00101202
MA1587.1_ZNF135	0.00903091
ZFX_HUMAN.H11MO.1.A	0.0816548
ZSC22_HUMAN.H11MO.0.C	0.156682	Not shown
MAF_HUMAN.H11MO.0.A	0.234269	Not shown
PITX2_HUMAN.H11MO.0.D	0.234269	Not shown
ZBT17_HUMAN.H11MO.0.A	0.25809099999999996	Not shown
MA0812.1_TFAP2B(var.2)	0.25809099999999996	Not shown

Motif 13/15

Motif ID	q-val	PWM
MA0481.3_FOXP1	0.024987799999999998
MA0047.3_FOXA2	0.024987799999999998
MA1683.1_FOXA3	0.024987799999999998
MA0148.4_FOXA1	0.03732
MA0031.1_FOXD1	0.156405
SHOX_HUMAN.H11MO.0.D	0.211813	Not shown
MA0846.1_FOXC2	0.211813	Not shown
MA0157.2_FOXO3	0.211813	Not shown
MA1606.1_Foxf1	0.211813	Not shown
MA0032.2_FOXC1	0.211813	Not shown

Motif 14/15

Motif ID	q-val	PWM
ZN770_HUMAN.H11MO.0.C	2.83707e-07
MA1596.1_ZNF460	0.00030289900000000003
ZN770_HUMAN.H11MO.1.C	0.00110587
MA1587.1_ZNF135	0.05273380000000001
PITX2_HUMAN.H11MO.0.D	0.05273380000000001
ZSC22_HUMAN.H11MO.0.C	0.231767	Not shown
IKZF1_HUMAN.H11MO.0.C	0.32571100000000003	Not shown

Motif 15/15

Motif ID	q-val	PWM
FOSL1_HUMAN.H11MO.0.A	3.93864e-06
JUN_HUMAN.H11MO.0.A	7.204960000000001e-06
MA0476.1_FOS	7.204960000000001e-06
JUND_HUMAN.H11MO.0.A	7.204960000000001e-06
JUNB_HUMAN.H11MO.0.A	7.204960000000001e-06
FOSL2_HUMAN.H11MO.0.A	1.05449e-05	Not shown
MA1138.1_FOSL2::JUNB	1.05449e-05	Not shown
MA0478.1_FOSL2	1.0613299999999999e-05	Not shown
MA1135.1_FOSB::JUNB	1.41307e-05	Not shown
MA1144.1_FOSL2::JUND	1.41307e-05	Not shown

Metacluster 2/2

Motif 1/5

No TOMTOM matches passing threshold

Motif 2/5

No TOMTOM matches passing threshold

Motif 3/5

Motif ID	q-val	PWM
MA0466.2_CEBPB	3.30554e-05
MA0837.1_CEBPE	3.30554e-05
MA0838.1_CEBPG	0.000474484
CEBPE_HUMAN.H11MO.0.A	0.000797891
CEBPB_HUMAN.H11MO.0.A	0.018824599999999997
CEBPD_HUMAN.H11MO.0.C	0.018824599999999997	Not shown
MA0862.1_GMEB2	0.03881130000000001	Not shown
HLF_HUMAN.H11MO.0.C	0.0471053	Not shown
CEBPA_HUMAN.H11MO.0.A	0.06803239999999999	Not shown
MA0836.2_CEBPD	0.07710030000000001	Not shown

Motif 4/5

No TOMTOM matches passing threshold

Motif 5/5

No TOMTOM matches passing threshold