TF-MoDISco results

Summary of motifs

TOMTOM matches to motifs

In [1]:
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

In [2]:
# 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_fold1/CEBPB_multitask_profile_fold1_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold1/CEBPB_multitask_profile_fold1_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_fold1/CEBPB_multitask_profile_fold1_profile
In [3]:
# Define paths and constants
input_length = 2114
shap_score_center_size = 400
In [4]:
if tfm_motifs_cache_dir:
    os.makedirs(tfm_motifs_cache_dir, exist_ok=True)

Import SHAP scores and TF-MoDISco results

In [5]:
# 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 [06:40<00:00,  1.47s/it]
In [6]:
# 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

In [7]:
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

In [8]:
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 1/13

10553 seqlets

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

Pattern 2/13

611 seqlets

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

Pattern 3/13

562 seqlets

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

Pattern 4/13

349 seqlets

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

Pattern 5/13

336 seqlets

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

Pattern 6/13

233 seqlets

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

Pattern 7/13

175 seqlets

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

Pattern 8/13

143 seqlets

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

Pattern 9/13

142 seqlets

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

Pattern 10/13

94 seqlets

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

Pattern 11/13

56 seqlets

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

Pattern 12/13

55 seqlets

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

Pattern 13/13

51 seqlets

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

Metacluster 2/2

Pattern 1/16

466 seqlets

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

Pattern 2/16

454 seqlets

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

Pattern 3/16

303 seqlets

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

Pattern 4/16

242 seqlets

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

Pattern 5/16

201 seqlets

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

Pattern 6/16

166 seqlets

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

Pattern 7/16

164 seqlets

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

Pattern 8/16

152 seqlets

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

Pattern 9/16

148 seqlets

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

Pattern 10/16

146 seqlets

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

Pattern 11/16

136 seqlets

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

Pattern 12/16

125 seqlets

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

Pattern 13/16

105 seqlets

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

Pattern 14/16

102 seqlets

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

Pattern 15/16

100 seqlets

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

Pattern 16/16

47 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.

In [9]:
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)
#SeqletsForwardReverse
110553
2611
3562
4349
5336
6233
7175
8143
9142
1094
1156
1255
1351

Metacluster 2/2

#SeqletsForwardReverse
1466
2454
3303
4242
5201
6166
7164
8152
9148
10146
11136
12125
13105
14102
15100
1647

Top TOMTOM matches for each motif

Here, the TF-MoDISco motifs are plotted as hCWMs, but the TOMTOM matches are shown as PWMs.

In [10]:
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 IDq-valPWM
CEBPB_HUMAN.H11MO.0.A1.27615e-09
CEBPD_HUMAN.H11MO.0.C1.58385e-08
CEBPA_HUMAN.H11MO.0.A7.10408e-07
MA0836.2_CEBPD2.2752600000000003e-05
MA0102.4_CEBPA0.000234357
MA0837.1_CEBPE0.00036894099999999996Not shown
MA0466.2_CEBPB0.000449578Not shown
MA0838.1_CEBPG0.000729669Not shown
MA0025.2_NFIL30.00144195Not shown
DBP_HUMAN.H11MO.0.B0.00209578Not shown

Motif 2/13

Motif IDq-valPWM
FOSL1_HUMAN.H11MO.0.A2.05429e-07
FOSB_HUMAN.H11MO.0.A4.5582e-07
JUND_HUMAN.H11MO.0.A2.21362e-06
FOS_HUMAN.H11MO.0.A2.21362e-06
JUN_HUMAN.H11MO.0.A2.86515e-06
FOSL2_HUMAN.H11MO.0.A5.90298e-06Not shown
MA0099.3_FOS::JUN1.87748e-05Not shown
JUNB_HUMAN.H11MO.0.A1.87748e-05Not shown
MA0478.1_FOSL21.87748e-05Not shown
MA1622.1_Smad2::Smad32.2524e-05Not shown

Motif 3/13

Motif IDq-valPWM
MA0139.1_CTCF6.0230200000000006e-15
CTCF_HUMAN.H11MO.0.A1.13865e-14
CTCFL_HUMAN.H11MO.0.A1.14603e-07
MA1102.2_CTCFL4.9180699999999995e-05
MA1568.1_TCF21(var.2)0.1064
MA1638.1_HAND20.12548299999999998Not shown
SNAI1_HUMAN.H11MO.0.C0.195666Not shown
ZIC3_HUMAN.H11MO.0.B0.195666Not shown
ZIC2_HUMAN.H11MO.0.D0.375981Not shown
KLF8_HUMAN.H11MO.0.C0.379083Not shown

Motif 4/13

Motif IDq-valPWM
ZN770_HUMAN.H11MO.0.C1.1400999999999999e-08
MA1596.1_ZNF4600.00164187
ZFX_HUMAN.H11MO.1.A0.00836574
ZN770_HUMAN.H11MO.1.C0.00836574
MA1587.1_ZNF1350.029589099999999997
MA0146.2_Zfx0.031478700000000005Not shown
ZSC22_HUMAN.H11MO.0.C0.083849Not shown
SP2_HUMAN.H11MO.0.A0.083849Not shown
WT1_HUMAN.H11MO.0.C0.09807569999999999Not shown
KLF6_HUMAN.H11MO.0.A0.101594Not shown

Motif 5/13

Motif IDq-valPWM
MA0837.1_CEBPE0.001233
MA0466.2_CEBPB0.00191423
MA0838.1_CEBPG0.00191423
CEBPE_HUMAN.H11MO.0.A0.0133683
CEBPD_HUMAN.H11MO.0.C0.026844599999999996
CEBPB_HUMAN.H11MO.0.A0.0312181Not shown
MA0836.2_CEBPD0.0760872Not shown
MA0862.1_GMEB20.07978869999999999Not shown
HLF_HUMAN.H11MO.0.C0.07978869999999999Not shown
MA0102.4_CEBPA0.07978869999999999Not shown

Motif 6/13

Motif IDq-valPWM
FOXM1_HUMAN.H11MO.0.A2.2102499999999998e-07
FOXA1_HUMAN.H11MO.0.A1.6761900000000003e-06
FOXA2_HUMAN.H11MO.0.A1.94211e-06
FOXF2_HUMAN.H11MO.0.D8.78429e-06
FOXA3_HUMAN.H11MO.0.B2.03029e-05
MA0846.1_FOXC28.04188e-05Not shown
FOXD3_HUMAN.H11MO.0.D8.04188e-05Not shown
MA0847.2_FOXD20.000145262Not shown
FOXC1_HUMAN.H11MO.0.C0.000193682Not shown
MA0032.2_FOXC10.00126373Not shown

Motif 7/13

Motif IDq-valPWM
FOXM1_HUMAN.H11MO.0.A4.45185e-06
FOXF2_HUMAN.H11MO.0.D4.45185e-06
FOXA1_HUMAN.H11MO.0.A1.46398e-05
FOXA3_HUMAN.H11MO.0.B4.26407e-05
FOXD3_HUMAN.H11MO.0.D5.1168800000000004e-05
FOXA2_HUMAN.H11MO.0.A7.33766e-05Not shown
MA0846.1_FOXC20.000109293Not shown
FOXC1_HUMAN.H11MO.0.C0.000366357Not shown
FOXD1_HUMAN.H11MO.0.D0.000366357Not shown
MA0847.2_FOXD20.000494155Not shown

Motif 8/13

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D4.5535100000000004e-05
MA1125.1_ZNF3845.79818e-05
MA0679.2_ONECUT10.0126918
HXC10_HUMAN.H11MO.0.D0.0126918
FOXG1_HUMAN.H11MO.0.D0.0126918
FOXL1_HUMAN.H11MO.0.D0.0126918Not shown
LMX1A_HUMAN.H11MO.0.D0.0417287Not shown
FOXJ3_HUMAN.H11MO.0.A0.0644697Not shown
PO3F3_HUMAN.H11MO.0.D0.0644697Not shown
ARI3A_HUMAN.H11MO.0.D0.0644697Not shown

Motif 9/13

Motif IDq-valPWM
GATA2_HUMAN.H11MO.0.A4.9575e-06
GATA2_HUMAN.H11MO.1.A4.9575e-06
GATA1_HUMAN.H11MO.1.A1.13603e-05
TAL1_HUMAN.H11MO.0.A2.55607e-05
GATA1_HUMAN.H11MO.0.A5.85472e-05
MA0037.3_GATA30.000118795Not shown
MA0036.3_GATA20.00060617Not shown
MA0766.2_GATA50.000889229Not shown
MA0482.2_GATA40.000889229Not shown
GATA4_HUMAN.H11MO.0.A0.0013262Not shown

Motif 10/13

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B0.0006655039999999999
HNF4A_HUMAN.H11MO.0.A0.0006655039999999999
MA0114.4_HNF4A0.0006655039999999999
MA0484.2_HNF4G0.000773433
COT2_HUMAN.H11MO.0.A0.00294905
MA1111.1_NR2F20.00304409Not shown
MA0115.1_NR1H2::RXRA0.00376884Not shown
MA1550.1_PPARD0.0040260999999999995Not shown
COT2_HUMAN.H11MO.1.A0.00567678Not shown
MA0677.1_Nr2f60.00567678Not shown

Motif 11/13

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A1.46584e-07
SP1_HUMAN.H11MO.0.A1.46584e-07
TBX15_HUMAN.H11MO.0.D1.46584e-07
SP3_HUMAN.H11MO.0.B1.46584e-07
MAZ_HUMAN.H11MO.0.A1.72246e-07
ZN467_HUMAN.H11MO.0.C2.50623e-07Not shown
KLF16_HUMAN.H11MO.0.D2.50623e-07Not shown
VEZF1_HUMAN.H11MO.0.C4.36385e-07Not shown
PATZ1_HUMAN.H11MO.0.C6.076809999999999e-07Not shown
WT1_HUMAN.H11MO.0.C1.48138e-06Not shown

Motif 12/13

Motif IDq-valPWM
MA0139.1_CTCF0.028279400000000003
CTCF_HUMAN.H11MO.0.A0.031040699999999997
CTCFL_HUMAN.H11MO.0.A0.184136
MA0473.3_ELF10.219075
MCR_HUMAN.H11MO.0.D0.312623
TAL1_HUMAN.H11MO.1.A0.312623Not shown
MYBB_HUMAN.H11MO.0.D0.361991Not shown
ATOH1_HUMAN.H11MO.0.B0.361991Not shown
EHF_HUMAN.H11MO.0.B0.361991Not shown
MA1568.1_TCF21(var.2)0.432804Not shown

Motif 13/13

Motif IDq-valPWM
FOXJ2_HUMAN.H11MO.0.C0.00857524
FOXF1_HUMAN.H11MO.0.D0.025350099999999997
FOXF2_HUMAN.H11MO.0.D0.0494334
PRDM6_HUMAN.H11MO.0.C0.0785798
FOXC1_HUMAN.H11MO.0.C0.0785798
MA0033.2_FOXL10.0785798Not shown
FOXA2_HUMAN.H11MO.0.A0.0785798Not shown
FOXL1_HUMAN.H11MO.0.D0.0785798Not shown
MA0846.1_FOXC20.0785798Not shown
MA0847.2_FOXD20.0785798Not shown

Metacluster 2/2

Motif 1/16

No TOMTOM matches passing threshold

Motif 2/16

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C0.00026110799999999997
CEBPB_HUMAN.H11MO.0.A0.000346913
MA0837.1_CEBPE0.00291361
MA0466.2_CEBPB0.00478368
MA0838.1_CEBPG0.010939299999999999
CEBPA_HUMAN.H11MO.0.A0.010939299999999999Not shown
MA0836.2_CEBPD0.030062799999999997Not shown
MA0102.4_CEBPA0.030062799999999997Not shown
ATF4_HUMAN.H11MO.0.A0.030062799999999997Not shown
MA0043.3_HLF0.030062799999999997Not shown

Motif 3/16

Motif IDq-valPWM
MGAP_HUMAN.H11MO.0.D0.317279
NFAC2_HUMAN.H11MO.0.B0.317279
TBX4_HUMAN.H11MO.0.D0.317279
NFAC4_HUMAN.H11MO.0.C0.3592
NFAC3_HUMAN.H11MO.0.B0.36041599999999996

Motif 4/16

No TOMTOM matches passing threshold

Motif 5/16

No TOMTOM matches passing threshold

Motif 6/16

No TOMTOM matches passing threshold

Motif 7/16

No TOMTOM matches passing threshold

Motif 8/16

No TOMTOM matches passing threshold

Motif 9/16

No TOMTOM matches passing threshold

Motif 10/16

No TOMTOM matches passing threshold

Motif 11/16

No TOMTOM matches passing threshold

Motif 12/16

Motif IDq-valPWM
KLF12_HUMAN.H11MO.0.C0.47521599999999997
SP2_HUMAN.H11MO.1.B0.47521599999999997
MA1513.1_KLF150.47521599999999997
MA1529.1_NHLH20.47521599999999997
SP1_HUMAN.H11MO.1.A0.482212

Motif 13/16

No TOMTOM matches passing threshold

Motif 14/16

No TOMTOM matches passing threshold

Motif 15/16

Motif IDq-valPWM
CEBPE_HUMAN.H11MO.0.A0.00159066
CEBPB_HUMAN.H11MO.0.A0.00228308
MA0837.1_CEBPE0.00228308
MA0466.2_CEBPB0.00279052
MA0838.1_CEBPG0.00301854
CEBPD_HUMAN.H11MO.0.C0.00417022Not shown
CEBPA_HUMAN.H11MO.0.A0.00417022Not shown
MA0102.4_CEBPA0.020624299999999998Not shown
MA1636.1_CEBPG(var.2)0.020624299999999998Not shown
MA0483.1_Gfi1b0.0331061Not shown

Motif 16/16

No TOMTOM matches passing threshold