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_fold8/CEBPB_multitask_profile_fold8_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold8/CEBPB_multitask_profile_fold8_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_fold8/CEBPB_multitask_profile_fold8_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 [04:17<00:00,  1.06it/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 1/11

11455 seqlets

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

Pattern 2/11

778 seqlets

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

Pattern 3/11

728 seqlets

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

Pattern 4/11

377 seqlets

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

Pattern 5/11

260 seqlets

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

Pattern 6/11

131 seqlets

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

Pattern 7/11

88 seqlets

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

Pattern 8/11

82 seqlets

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

Pattern 9/11

71 seqlets

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

Pattern 10/11

68 seqlets

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

Pattern 11/11

40 seqlets

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

Metacluster 2/2

Pattern 1/6

156 seqlets

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

Pattern 2/6

154 seqlets

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

Pattern 3/6

143 seqlets

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

Pattern 4/6

87 seqlets

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

Pattern 5/6

81 seqlets

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

Pattern 6/6

77 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	11455
2	778
3	728
4	377
5	260
6	131
7	88
8	82
9	71
10	68
11	40

Metacluster 2/2

#	Seqlets	Forward	Reverse
1	156
2	154
3	143
4	87
5	81
6	77

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 1/11

Motif ID	q-val	PWM
CEBPB_HUMAN.H11MO.0.A	2.8002e-10
CEBPD_HUMAN.H11MO.0.C	6.113340000000001e-09
CEBPA_HUMAN.H11MO.0.A	7.450339999999999e-07
MA0836.2_CEBPD	2.2501e-05
MA0102.4_CEBPA	0.000303925
MA0837.1_CEBPE	0.00043995300000000003	Not shown
MA0466.2_CEBPB	0.000617248	Not shown
MA0838.1_CEBPG	0.000838377	Not shown
MA0025.2_NFIL3	0.00166896	Not shown
DBP_HUMAN.H11MO.0.B	0.00298819	Not shown

Motif 2/11

Motif ID	q-val	PWM
JUN_HUMAN.H11MO.0.A	6.374439999999999e-05
MA1141.1_FOS::JUND	6.374439999999999e-05
MA1633.1_BACH1	6.374439999999999e-05
FOSB_HUMAN.H11MO.0.A	6.374439999999999e-05
JUND_HUMAN.H11MO.0.A	6.374439999999999e-05
MA1128.1_FOSL1::JUN	6.374439999999999e-05	Not shown
MA0099.3_FOS::JUN	6.374439999999999e-05	Not shown
MA1622.1_Smad2::Smad3	6.374439999999999e-05	Not shown
MA1130.1_FOSL2::JUN	6.374439999999999e-05	Not shown
FOSL2_HUMAN.H11MO.0.A	6.374439999999999e-05	Not shown

Motif 3/11

Motif ID	q-val	PWM
MA0139.1_CTCF	2.65803e-18
CTCF_HUMAN.H11MO.0.A	2.7378e-15
CTCFL_HUMAN.H11MO.0.A	4.08958e-09
MA1102.2_CTCFL	4.4321400000000006e-05
MA1568.1_TCF21(var.2)	0.11636400000000001
MA1638.1_HAND2	0.14823699999999998	Not shown
SNAI1_HUMAN.H11MO.0.C	0.200543	Not shown
ZIC3_HUMAN.H11MO.0.B	0.244668	Not shown
PLAG1_HUMAN.H11MO.0.D	0.369374	Not shown
MA1629.1_Zic2	0.369374	Not shown

Motif 4/11

Motif ID	q-val	PWM
FOXA1_HUMAN.H11MO.0.A	2.08098e-07
FOXA2_HUMAN.H11MO.0.A	2.52525e-07
FOXM1_HUMAN.H11MO.0.A	5.78784e-06
FOXF2_HUMAN.H11MO.0.D	5.78784e-06
FOXA3_HUMAN.H11MO.0.B	5.78784e-06
MA0846.1_FOXC2	5.78784e-06	Not shown
FOXD3_HUMAN.H11MO.0.D	6.22077e-06	Not shown
FOXC1_HUMAN.H11MO.0.C	1.20578e-05	Not shown
MA0847.2_FOXD2	1.20578e-05	Not shown
FOXD1_HUMAN.H11MO.0.D	0.000451525	Not shown

Motif 5/11

Motif ID	q-val	PWM
HNF4G_HUMAN.H11MO.0.B	2.41861e-11
HNF4A_HUMAN.H11MO.0.A	5.27071e-10
MA0856.1_RXRG	0.000454555
MA1550.1_PPARD	0.000454555
MA0677.1_Nr2f6	0.000454555
MA1574.1_THRB	0.000454555	Not shown
MA0512.2_Rxra	0.000454555	Not shown
MA1494.1_HNF4A(var.2)	0.000454555	Not shown
MA1148.1_PPARA::RXRA	0.000454555	Not shown
PPARA_HUMAN.H11MO.0.B	0.000454555	Not shown

Motif 6/11

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	4.47144e-14
SP3_HUMAN.H11MO.0.B	1.20757e-13
KLF3_HUMAN.H11MO.0.B	4.618520000000001e-09
SP4_HUMAN.H11MO.1.A	1.72456e-08
SP4_HUMAN.H11MO.0.A	1.48979e-07
SP1_HUMAN.H11MO.1.A	2.8005600000000003e-07	Not shown
MA1513.1_KLF15	2.84312e-07	Not shown
PATZ1_HUMAN.H11MO.0.C	2.84312e-07	Not shown
KLF6_HUMAN.H11MO.0.A	2.84312e-07	Not shown
SP1_HUMAN.H11MO.0.A	2.84312e-07	Not shown

Motif 7/11

Motif ID	q-val	PWM
FOXB1_HUMAN.H11MO.0.D	0.001336
FOXD2_HUMAN.H11MO.0.D	0.0255111
MA0032.2_FOXC1	0.144714
MA0845.1_FOXB1	0.144714
MA0047.3_FOXA2	0.39414
MA0033.2_FOXL1	0.39414	Not shown
MA0659.2_MAFG	0.39414	Not shown
MA1683.1_FOXA3	0.39414	Not shown
GBX1_HUMAN.H11MO.0.D	0.39414	Not shown
MA0849.1_FOXO6	0.39414	Not shown

Motif 8/11

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	8.18651e-08
SP1_HUMAN.H11MO.0.A	8.18651e-08
SP3_HUMAN.H11MO.0.B	1.13191e-06
SP2_HUMAN.H11MO.1.B	1.2748900000000001e-05
KLF3_HUMAN.H11MO.0.B	1.94909e-05
SP4_HUMAN.H11MO.1.A	2.42363e-05	Not shown
KLF6_HUMAN.H11MO.0.A	2.42363e-05	Not shown
MA1513.1_KLF15	2.42363e-05	Not shown
SP1_HUMAN.H11MO.1.A	4.35068e-05	Not shown
SP4_HUMAN.H11MO.0.A	8.35905e-05	Not shown

Motif 9/11

Motif ID	q-val	PWM
FOXB1_HUMAN.H11MO.0.D	0.000475545
FOXD2_HUMAN.H11MO.0.D	0.000475545
MA0845.1_FOXB1	0.023671400000000002
MA0032.2_FOXC1	0.0240888
FOXA2_HUMAN.H11MO.0.A	0.110385
MA0846.1_FOXC2	0.110385	Not shown
MA0148.4_FOXA1	0.110385	Not shown
MA0847.2_FOXD2	0.110385	Not shown
FOXA1_HUMAN.H11MO.0.A	0.113666	Not shown
MA1683.1_FOXA3	0.137232	Not shown

Motif 10/11

Motif ID	q-val	PWM
GATA4_HUMAN.H11MO.0.A	1.5900999999999998e-06
GATA1_HUMAN.H11MO.1.A	1.49451e-05
GATA2_HUMAN.H11MO.1.A	1.80183e-05
GATA1_HUMAN.H11MO.0.A	7.38001e-05
MA0766.2_GATA5	0.000383653
MA0036.3_GATA2	0.000931313	Not shown
TAL1_HUMAN.H11MO.0.A	0.000931313	Not shown
MA0037.3_GATA3	0.00104671	Not shown
GATA3_HUMAN.H11MO.0.A	0.0030992	Not shown
GATA2_HUMAN.H11MO.0.A	0.0030992	Not shown

Motif 11/11

Motif ID	q-val	PWM
MA0139.1_CTCF	9.614130000000002e-08
CTCF_HUMAN.H11MO.0.A	3.47567e-07
CTCFL_HUMAN.H11MO.0.A	3.4055199999999995e-06
MA1102.2_CTCFL	1.52681e-05
NR1H3_HUMAN.H11MO.0.B	0.140439
MA1513.1_KLF15	0.161189	Not shown
TAF1_HUMAN.H11MO.0.A	0.161189	Not shown
MA0155.1_INSM1	0.161189	Not shown
SP4_HUMAN.H11MO.0.A	0.161189	Not shown
MA0830.2_TCF4	0.161189	Not shown

Metacluster 2/2

Motif 1/6

No TOMTOM matches passing threshold

Motif 2/6

Motif ID	q-val	PWM
KLF12_HUMAN.H11MO.0.C	0.0740687
MA0471.2_E2F6	0.0740687
MA1522.1_MAZ	0.0740687
SP1_HUMAN.H11MO.0.A	0.0740687
MA1653.1_ZNF148	0.0740687
MAZ_HUMAN.H11MO.1.A	0.0740687	Not shown
E2F7_HUMAN.H11MO.0.B	0.0740687	Not shown
PATZ1_HUMAN.H11MO.0.C	0.0740687	Not shown
SP3_HUMAN.H11MO.0.B	0.0740687	Not shown
TFDP1_HUMAN.H11MO.0.C	0.0740687	Not shown

Motif 3/6

Motif ID	q-val	PWM
CEBPD_HUMAN.H11MO.0.C	2.69149e-06
CEBPB_HUMAN.H11MO.0.A	3.06233e-06
MA0837.1_CEBPE	6.69666e-05
MA0466.2_CEBPB	6.69666e-05
CEBPA_HUMAN.H11MO.0.A	0.000160715
MA0838.1_CEBPG	0.00022700900000000002	Not shown
DBP_HUMAN.H11MO.0.B	0.000850429	Not shown
MA0836.2_CEBPD	0.000850429	Not shown
MA1636.1_CEBPG(var.2)	0.000850429	Not shown
NFIL3_HUMAN.H11MO.0.D	0.000850429	Not shown

Motif 4/6

Motif ID	q-val	PWM
TBX21_HUMAN.H11MO.0.A	0.411656
KLF6_HUMAN.H11MO.0.A	0.411656
EGR2_HUMAN.H11MO.0.A	0.411656
PATZ1_HUMAN.H11MO.1.C	0.411656
BRAC_HUMAN.H11MO.0.A	0.411656
NFIC_HUMAN.H11MO.0.A	0.411656	Not shown
KLF3_HUMAN.H11MO.0.B	0.411656	Not shown
SP4_HUMAN.H11MO.0.A	0.411656	Not shown
TBX15_HUMAN.H11MO.0.D	0.411656	Not shown
ESR2_HUMAN.H11MO.0.A	0.411656	Not shown

Motif 5/6

Motif ID	q-val	PWM
FLI1_HUMAN.H11MO.0.A	0.33401
ASCL1_HUMAN.H11MO.0.A	0.33401
TAL1_HUMAN.H11MO.1.A	0.33401
ETV2_HUMAN.H11MO.0.B	0.33401
ELF5_HUMAN.H11MO.0.A	0.33401
RXRA_HUMAN.H11MO.0.A	0.33401	Not shown
ZN770_HUMAN.H11MO.1.C	0.33401	Not shown
ZN257_HUMAN.H11MO.0.C	0.33401	Not shown
SP4_HUMAN.H11MO.0.A	0.33401	Not shown
SP2_HUMAN.H11MO.1.B	0.33401	Not shown

Motif 6/6

Motif ID	q-val	PWM
MA0812.1_TFAP2B(var.2)	0.0105635
AP2C_HUMAN.H11MO.0.A	0.0105635
AP2A_HUMAN.H11MO.0.A	0.0105635
MA0814.2_TFAP2C(var.2)	0.0105635
MA1569.1_TFAP2E	0.0105635
MA0003.4_TFAP2A	0.0105635	Not shown
AP2D_HUMAN.H11MO.0.D	0.0143041	Not shown
MA0524.2_TFAP2C	0.0455736	Not shown
MA0811.1_TFAP2B	0.0567337	Not shown
MA1615.1_Plagl1	0.111679	Not shown