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_fold4/CEBPB_multitask_profile_fold4_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold4/CEBPB_multitask_profile_fold4_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_fold4/CEBPB_multitask_profile_fold4_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 [06:23<00:00,  1.41s/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 1/15

10016 seqlets

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

Pattern 2/15

844 seqlets

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

Pattern 3/15

483 seqlets

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

Pattern 4/15

413 seqlets

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

Pattern 5/15

302 seqlets

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

Pattern 6/15

202 seqlets

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

Pattern 7/15

178 seqlets

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

Pattern 8/15

162 seqlets

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

Pattern 9/15

120 seqlets

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

Pattern 10/15

115 seqlets

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

Pattern 11/15

81 seqlets

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

Pattern 12/15

78 seqlets

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

Pattern 13/15

46 seqlets

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

Pattern 14/15

41 seqlets

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

Pattern 15/15

31 seqlets

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

Metacluster 2/2

Pattern 1/18

411 seqlets

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

Pattern 2/18

359 seqlets

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

Pattern 3/18

214 seqlets

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

Pattern 4/18

185 seqlets

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

Pattern 5/18

184 seqlets

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

Pattern 6/18

184 seqlets

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

Pattern 7/18

156 seqlets

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

Pattern 8/18

154 seqlets

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

Pattern 9/18

153 seqlets

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

Pattern 10/18

148 seqlets

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

Pattern 11/18

143 seqlets

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

Pattern 12/18

142 seqlets

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

Pattern 13/18

128 seqlets

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

Pattern 14/18

121 seqlets

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

Pattern 15/18

106 seqlets

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

Pattern 16/18

106 seqlets

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

Pattern 17/18

92 seqlets

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

Pattern 18/18

41 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
110016
2844
3483
4413
5302
6202
7178
8162
9120
10115
1181
1278
1346
1441
1531

Metacluster 2/2

#SeqletsForwardReverse
1411
2359
3214
4185
5184
6184
7156
8154
9153
10148
11143
12142
13128
14121
15106
16106
1792
1841

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 1/15

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A1.0264100000000002e-09
CEBPD_HUMAN.H11MO.0.C4.59559e-09
CEBPA_HUMAN.H11MO.0.A3.34535e-07
MA0836.2_CEBPD3.18221e-05
MA0102.4_CEBPA0.000171557
MA0837.1_CEBPE0.00043193800000000004Not shown
MA0466.2_CEBPB0.000605321Not shown
MA0838.1_CEBPG0.0008389480000000001Not shown
MA0025.2_NFIL30.00163585Not shown
DBP_HUMAN.H11MO.0.B0.00281444Not shown

Motif 2/15

Motif IDq-valPWM
JUN_HUMAN.H11MO.0.A2.0732399999999998e-05
FOSL2_HUMAN.H11MO.0.A2.0732399999999998e-05
JUND_HUMAN.H11MO.0.A5.8492399999999996e-05
NFE2_HUMAN.H11MO.0.A6.1417e-05
MA1622.1_Smad2::Smad36.1417e-05
FOSB_HUMAN.H11MO.0.A8.319129999999999e-05Not shown
FOSL1_HUMAN.H11MO.0.A8.319129999999999e-05Not shown
MA1130.1_FOSL2::JUN8.319129999999999e-05Not shown
MA0099.3_FOS::JUN8.458620000000001e-05Not shown
MA1141.1_FOS::JUND8.458620000000001e-05Not shown

Motif 3/15

Motif IDq-valPWM
MA0139.1_CTCF4.7376199999999996e-18
CTCF_HUMAN.H11MO.0.A1.81164e-14
CTCFL_HUMAN.H11MO.0.A7.30482e-09
MA1102.2_CTCFL5.57092e-05
MA1568.1_TCF21(var.2)0.157502
MA1638.1_HAND20.194972Not shown
ZIC3_HUMAN.H11MO.0.B0.22755100000000003Not shown
SNAI1_HUMAN.H11MO.0.C0.22755100000000003Not shown
ZIC2_HUMAN.H11MO.0.D0.366552Not shown
MA0155.1_INSM10.366552Not shown

Motif 4/15

Motif IDq-valPWM
CTCF_HUMAN.H11MO.0.A0.00157353
CTCFL_HUMAN.H11MO.0.A0.0030839
MA0139.1_CTCF0.0030839
MA1102.2_CTCFL0.0232609
MA1568.1_TCF21(var.2)0.06739450000000001
SP4_HUMAN.H11MO.0.A0.06739450000000001Not shown
MA0830.2_TCF40.06739450000000001Not shown
MA1648.1_TCF12(var.2)0.06739450000000001Not shown
NDF1_HUMAN.H11MO.0.A0.159299Not shown
ATOH1_HUMAN.H11MO.0.B0.159299Not shown

Motif 5/15

Motif IDq-valPWM
FOXM1_HUMAN.H11MO.0.A1.4668999999999999e-06
FOXA1_HUMAN.H11MO.0.A3.99445e-06
FOXF2_HUMAN.H11MO.0.D6.92653e-06
FOXA2_HUMAN.H11MO.0.A7.79234e-06
FOXA3_HUMAN.H11MO.0.B2.63435e-05
FOXD3_HUMAN.H11MO.0.D4.64534e-05Not shown
MA0846.1_FOXC26.82178e-05Not shown
MA0847.2_FOXD20.000180066Not shown
FOXC1_HUMAN.H11MO.0.C0.000184413Not shown
FOXD1_HUMAN.H11MO.0.D0.000700112Not shown

Motif 6/15

Motif IDq-valPWM
CEBPA_HUMAN.H11MO.0.A0.12110599999999999
MA0043.3_HLF0.12110599999999999
CEBPB_HUMAN.H11MO.0.A0.12110599999999999
MA0025.2_NFIL30.12110599999999999
DBP_HUMAN.H11MO.0.B0.12110599999999999
CEBPD_HUMAN.H11MO.0.C0.12110599999999999Not shown
NFIL3_HUMAN.H11MO.0.D0.12110599999999999Not shown
MA0836.2_CEBPD0.134989Not shown
MA1636.1_CEBPG(var.2)0.134989Not shown
PO3F4_HUMAN.H11MO.0.D0.134989Not shown

Motif 7/15

Motif IDq-valPWM
MA1596.1_ZNF4600.00300759
MA1587.1_ZNF1350.00300759
ZN770_HUMAN.H11MO.0.C0.00300759
ZN341_HUMAN.H11MO.0.C0.0559915
ZFX_HUMAN.H11MO.1.A0.0620361
MA1102.2_CTCFL0.14377Not shown
KLF15_HUMAN.H11MO.0.A0.14377Not shown
SP2_HUMAN.H11MO.0.A0.14377Not shown
WT1_HUMAN.H11MO.0.C0.14377Not shown
MA1630.1_Znf2810.14377Not shown

Motif 8/15

Motif IDq-valPWM
ZN770_HUMAN.H11MO.0.C1.64823e-07
MA1596.1_ZNF4600.000274403
ZN770_HUMAN.H11MO.1.C0.0009686139999999999
MA1587.1_ZNF1350.0284351
PITX2_HUMAN.H11MO.0.D0.0508674
ZSC22_HUMAN.H11MO.0.C0.197847Not shown
IKZF1_HUMAN.H11MO.0.C0.349892Not shown

Motif 9/15

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B3.818740000000001e-06
HNF4A_HUMAN.H11MO.0.A2.5860900000000002e-05
MA0065.2_Pparg::Rxra0.00335791
PPARA_HUMAN.H11MO.0.B0.00586463
MA1494.1_HNF4A(var.2)0.00750019
MA0856.1_RXRG0.00750019Not shown
PPARG_HUMAN.H11MO.0.A0.00750019Not shown
MA1574.1_THRB0.00750019Not shown
MA0512.2_Rxra0.00750019Not shown
MA0855.1_RXRB0.00750019Not shown

Motif 10/15

Motif IDq-valPWM
GATA1_HUMAN.H11MO.1.A2.8674299999999998e-05
GATA1_HUMAN.H11MO.0.A4.3011400000000006e-05
GATA4_HUMAN.H11MO.0.A6.92226e-05
GATA2_HUMAN.H11MO.1.A6.92226e-05
GATA2_HUMAN.H11MO.0.A6.92226e-05
TAL1_HUMAN.H11MO.0.A6.92226e-05Not shown
GATA6_HUMAN.H11MO.0.A0.000275482Not shown
MA0140.2_GATA1::TAL10.00220113Not shown
MA0036.3_GATA20.00761198Not shown
MA0482.2_GATA40.00856345Not shown

Motif 11/15

Motif IDq-valPWM
GATA6_HUMAN.H11MO.0.A0.045723
MA0766.2_GATA50.479167
GATA4_HUMAN.H11MO.0.A0.479167
MA0037.3_GATA30.479167
MA0025.2_NFIL30.479167
MA1104.2_GATA60.479167Not shown

Motif 12/15

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D1.87832e-05
MA1125.1_ZNF3840.0177026
FOXL1_HUMAN.H11MO.0.D0.024110200000000002
PRDM6_HUMAN.H11MO.0.C0.024110200000000002
FOXG1_HUMAN.H11MO.0.D0.026178399999999998
MA0679.2_ONECUT10.0686039Not shown
FOXJ3_HUMAN.H11MO.0.A0.0846582Not shown
ANDR_HUMAN.H11MO.0.A0.0846582Not shown
HXC10_HUMAN.H11MO.0.D0.150631Not shown
FUBP1_HUMAN.H11MO.0.D0.150631Not shown

Motif 13/15

Motif IDq-valPWM
MA1135.1_FOSB::JUNB4.93522e-06
MA1138.1_FOSL2::JUNB4.93522e-06
MA1144.1_FOSL2::JUND4.93522e-06
MA0099.3_FOS::JUN4.93522e-06
MA0478.1_FOSL21.2605e-05
FOSB_HUMAN.H11MO.0.A5.7895699999999995e-05Not shown
FOS_HUMAN.H11MO.0.A0.000158154Not shown
MA1132.1_JUN::JUNB0.000158154Not shown
MA1142.1_FOSL1::JUND0.000158154Not shown
JUND_HUMAN.H11MO.0.A0.000239538Not shown

Motif 14/15

Motif IDq-valPWM
CTCF_HUMAN.H11MO.0.A0.000580871
MA0139.1_CTCF0.000580871
CTCFL_HUMAN.H11MO.0.A0.074789
MA1568.1_TCF21(var.2)0.15275899999999998
MA0116.1_Znf4230.165692
SNAI1_HUMAN.H11MO.0.C0.17436Not shown
MA1638.1_HAND20.188836Not shown
MA1102.2_CTCFL0.188836Not shown
MYC_HUMAN.H11MO.0.A0.30633699999999997Not shown

Motif 15/15

Motif IDq-valPWM
MA0837.1_CEBPE0.056428
CEBPE_HUMAN.H11MO.0.A0.056428
MA0466.2_CEBPB0.056428
MA0838.1_CEBPG0.0822762
CEBPB_HUMAN.H11MO.0.A0.166681
CEBPA_HUMAN.H11MO.0.A0.166681Not shown
CEBPD_HUMAN.H11MO.0.C0.166681Not shown
ZFX_HUMAN.H11MO.1.A0.166681Not shown
ZN121_HUMAN.H11MO.0.C0.223358Not shown
THB_HUMAN.H11MO.0.C0.31327699999999997Not shown

Metacluster 2/2

Motif 1/18

Motif IDq-valPWM
PATZ1_HUMAN.H11MO.0.C0.22796100000000002
TFDP1_HUMAN.H11MO.0.C0.22796100000000002
E2F1_HUMAN.H11MO.0.A0.22796100000000002
ZFX_HUMAN.H11MO.1.A0.22796100000000002
TAF1_HUMAN.H11MO.0.A0.22796100000000002
SP1_HUMAN.H11MO.1.A0.22796100000000002Not shown
E2F7_HUMAN.H11MO.0.B0.22796100000000002Not shown
MA0471.2_E2F60.22796100000000002Not shown
EGR1_HUMAN.H11MO.0.A0.22796100000000002Not shown
WT1_HUMAN.H11MO.0.C0.22796100000000002Not shown

Motif 2/18

Motif IDq-valPWM
MA0837.1_CEBPE1.0974400000000001e-05
MA0466.2_CEBPB1.0974400000000001e-05
CEBPD_HUMAN.H11MO.0.C1.0974400000000001e-05
MA0838.1_CEBPG0.00010460600000000001
CEBPB_HUMAN.H11MO.0.A0.000151478
CEBPA_HUMAN.H11MO.0.A0.00132775Not shown
DBP_HUMAN.H11MO.0.B0.00397135Not shown
MA0639.1_DBP0.00674061Not shown
NFIL3_HUMAN.H11MO.0.D0.00740148Not shown
MA0043.3_HLF0.00740148Not shown

Motif 3/18

No TOMTOM matches passing threshold

Motif 4/18

No TOMTOM matches passing threshold

Motif 5/18

No TOMTOM matches passing threshold

Motif 6/18

No TOMTOM matches passing threshold

Motif 7/18

No TOMTOM matches passing threshold

Motif 8/18

Motif IDq-valPWM
AP2B_HUMAN.H11MO.0.B0.020241400000000003
MA0810.1_TFAP2A(var.2)0.10496199999999999
ZN320_HUMAN.H11MO.0.C0.113033
MA0872.1_TFAP2A(var.3)0.113033
MA0815.1_TFAP2C(var.3)0.11658699999999998
MA0524.2_TFAP2C0.116868Not shown
MA0811.1_TFAP2B0.116868Not shown
MA0155.1_INSM10.170944Not shown
MA0813.1_TFAP2B(var.3)0.170944Not shown
MECP2_HUMAN.H11MO.0.C0.176455Not shown

Motif 9/18

No TOMTOM matches passing threshold

Motif 10/18

No TOMTOM matches passing threshold

Motif 11/18

No TOMTOM matches passing threshold

Motif 12/18

Motif IDq-valPWM
ATF2_HUMAN.H11MO.1.B0.039108699999999996
MA0032.2_FOXC10.194528

Motif 13/18

No TOMTOM matches passing threshold

Motif 14/18

No TOMTOM matches passing threshold

Motif 15/18

No TOMTOM matches passing threshold

Motif 16/18

No TOMTOM matches passing threshold

Motif 17/18

Motif IDq-valPWM
CEBPE_HUMAN.H11MO.0.A0.00479069
MA0837.1_CEBPE0.00479069
CEBPB_HUMAN.H11MO.0.A0.00479069
MA0466.2_CEBPB0.00479069
MA0838.1_CEBPG0.00479069
CEBPD_HUMAN.H11MO.0.C0.00479069Not shown
MA1636.1_CEBPG(var.2)0.0135017Not shown
CEBPA_HUMAN.H11MO.0.A0.0135017Not shown
ZSC31_HUMAN.H11MO.0.C0.0135017Not shown
BATF_HUMAN.H11MO.1.A0.014412600000000001Not shown

Motif 18/18

Motif IDq-valPWM
GRHL1_HUMAN.H11MO.0.D0.395825
GRHL2_HUMAN.H11MO.0.A0.395825