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_fold5/CEBPB_multitask_profile_fold5_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold5/CEBPB_multitask_profile_fold5_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_fold5/CEBPB_multitask_profile_fold5_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 [06:50<00:00,  1.50s/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 1/14

10026 seqlets

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

Pattern 2/14

733 seqlets

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

Pattern 3/14

604 seqlets

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

Pattern 4/14

284 seqlets

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

Pattern 5/14

248 seqlets

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

Pattern 6/14

225 seqlets

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

Pattern 7/14

193 seqlets

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

Pattern 8/14

174 seqlets

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

Pattern 9/14

117 seqlets

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

Pattern 10/14

101 seqlets

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

Pattern 11/14

91 seqlets

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

Pattern 12/14

63 seqlets

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

Pattern 13/14

57 seqlets

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

Pattern 14/14

33 seqlets

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

Metacluster 2/2

Pattern 1/11

398 seqlets

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

Pattern 2/11

320 seqlets

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

Pattern 3/11

291 seqlets

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

Pattern 4/11

235 seqlets

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

Pattern 5/11

234 seqlets

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

Pattern 6/11

232 seqlets

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

Pattern 7/11

160 seqlets

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

Pattern 8/11

149 seqlets

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

Pattern 9/11

141 seqlets

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

Pattern 10/11

134 seqlets

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

Pattern 11/11

119 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
110026
2733
3604
4284
5248
6225
7193
8174
9117
10101
1191
1263
1357
1433

Metacluster 2/2

#SeqletsForwardReverse
1398
2320
3291
4235
5234
6232
7160
8149
9141
10134
11119

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 1/14

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A1.4896099999999998e-09
CEBPD_HUMAN.H11MO.0.C5.97935e-09
CEBPA_HUMAN.H11MO.0.A7.31918e-07
MA0836.2_CEBPD2.4101e-05
MA0102.4_CEBPA0.00018810799999999998
MA0837.1_CEBPE0.000458279Not shown
MA0466.2_CEBPB0.000464929Not shown
MA0838.1_CEBPG0.000746786Not shown
MA0025.2_NFIL30.00148296Not shown
DBP_HUMAN.H11MO.0.B0.00291953Not shown

Motif 2/14

Motif IDq-valPWM
BACH2_HUMAN.H11MO.0.A1.1283099999999999e-05
BACH1_HUMAN.H11MO.0.A1.4208299999999999e-05
MA1135.1_FOSB::JUNB1.4208299999999999e-05
MA1138.1_FOSL2::JUNB1.4208299999999999e-05
MA1144.1_FOSL2::JUND1.4208299999999999e-05
MA0099.3_FOS::JUN1.4208299999999999e-05Not shown
MA0489.1_JUN(var.2)2.48645e-05Not shown
MA1622.1_Smad2::Smad32.48645e-05Not shown
MA0591.1_Bach1::Mafk2.52592e-05Not shown
MA0478.1_FOSL22.7927600000000003e-05Not shown

Motif 3/14

Motif IDq-valPWM
MA0139.1_CTCF1.3027099999999999e-17
CTCF_HUMAN.H11MO.0.A1.0843799999999998e-14
CTCFL_HUMAN.H11MO.0.A6.63439e-08
MA1102.2_CTCFL6.0801300000000003e-05
MA1568.1_TCF21(var.2)0.12397899999999999
MA1638.1_HAND20.154838Not shown
ZIC3_HUMAN.H11MO.0.B0.197684Not shown
SNAI1_HUMAN.H11MO.0.C0.254255Not shown
ZIC2_HUMAN.H11MO.0.D0.267656Not shown
MA1628.1_Zic1::Zic20.341731Not shown

Motif 4/14

Motif IDq-valPWM
FOXA1_HUMAN.H11MO.0.A1.74494e-07
FOXA2_HUMAN.H11MO.0.A4.09478e-07
FOXF2_HUMAN.H11MO.0.D2.01607e-06
FOXM1_HUMAN.H11MO.0.A2.26808e-06
FOXA3_HUMAN.H11MO.0.B2.41928e-06
MA0846.1_FOXC29.495689999999999e-06Not shown
FOXD3_HUMAN.H11MO.0.D1.0680599999999999e-05Not shown
MA0847.2_FOXD22.95134e-05Not shown
FOXC1_HUMAN.H11MO.0.C5.52192e-05Not shown
MA0032.2_FOXC10.000515171Not shown

Motif 5/14

No TOMTOM matches passing threshold

Motif 6/14

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D1.3104e-05
MA1125.1_ZNF3840.0165673
FOXL1_HUMAN.H11MO.0.D0.0167833
PRDM6_HUMAN.H11MO.0.C0.0167833
FOXG1_HUMAN.H11MO.0.D0.0473555
ANDR_HUMAN.H11MO.0.A0.0656959Not shown
MA0679.2_ONECUT10.0656959Not shown
FOXJ3_HUMAN.H11MO.0.A0.08431480000000001Not shown
ONEC2_HUMAN.H11MO.0.D0.09244139999999999Not shown
FUBP1_HUMAN.H11MO.0.D0.09244139999999999Not shown

Motif 7/14

Motif IDq-valPWM
ZN770_HUMAN.H11MO.0.C0.000208281
MA1596.1_ZNF4600.000445919
ZN770_HUMAN.H11MO.1.C0.0115201
MA1587.1_ZNF1350.07833939999999999
SP1_HUMAN.H11MO.0.A0.07833939999999999
CTCF_HUMAN.H11MO.0.A0.12133499999999998Not shown
WT1_HUMAN.H11MO.0.C0.12133499999999998Not shown
MA0139.1_CTCF0.12133499999999998Not shown
ZN263_HUMAN.H11MO.0.A0.16609000000000002Not shown
MAZ_HUMAN.H11MO.0.A0.16609000000000002Not shown

Motif 8/14

Motif IDq-valPWM
ZN770_HUMAN.H11MO.0.C0.051510900000000005
MA0116.1_Znf4230.355636
NFIC_HUMAN.H11MO.0.A0.355636

Motif 9/14

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B2.04438e-07
HNF4A_HUMAN.H11MO.0.A2.65315e-07
MA0677.1_Nr2f60.00100695
MA0856.1_RXRG0.00100695
MA0512.2_Rxra0.00100695
MA1550.1_PPARD0.00100695Not shown
MA1574.1_THRB0.00100695Not shown
MA1537.1_NR2F1(var.2)0.00100695Not shown
MA1148.1_PPARA::RXRA0.00106135Not shown
MA0115.1_NR1H2::RXRA0.00106135Not shown

Motif 10/14

Motif IDq-valPWM
MA0043.3_HLF0.028814599999999996
MA0025.2_NFIL30.028814599999999996
DBP_HUMAN.H11MO.0.B0.048468000000000004
NFIL3_HUMAN.H11MO.0.D0.052204999999999994
PO3F4_HUMAN.H11MO.0.D0.057292499999999996
MA0142.1_Pou5f1::Sox20.057292499999999996Not shown
CEBPB_HUMAN.H11MO.0.A0.057292499999999996Not shown
CEBPA_HUMAN.H11MO.0.A0.057292499999999996Not shown
MA0786.1_POU3F10.057292499999999996Not shown
CEBPG_HUMAN.H11MO.0.B0.057615099999999995Not shown

Motif 11/14

Motif IDq-valPWM
GATA1_HUMAN.H11MO.1.A0.000210224
GATA2_HUMAN.H11MO.1.A0.000210224
GATA2_HUMAN.H11MO.0.A0.00036438800000000005
GATA4_HUMAN.H11MO.0.A0.000623731
GATA6_HUMAN.H11MO.0.A0.000873223
GATA1_HUMAN.H11MO.0.A0.00115836Not shown
TAL1_HUMAN.H11MO.0.A0.00115836Not shown
MA0036.3_GATA20.00147251Not shown
MA1104.2_GATA60.00183246Not shown
MA0482.2_GATA40.00410021Not shown

Motif 12/14

Motif IDq-valPWM
MA1135.1_FOSB::JUNB4.72624e-07
MA1138.1_FOSL2::JUNB6.04848e-07
MA1144.1_FOSL2::JUND6.04848e-07
MA0099.3_FOS::JUN8.33803e-07
MA0478.1_FOSL27.289119999999999e-06
MA1134.1_FOS::JUNB1.74527e-05Not shown
FOSB_HUMAN.H11MO.0.A0.00016963599999999997Not shown
JUN_HUMAN.H11MO.0.A0.00017057099999999998Not shown
MA1132.1_JUN::JUNB0.000192217Not shown
MA1128.1_FOSL1::JUN0.00019344700000000002Not shown

Motif 13/14

Motif IDq-valPWM
MA1138.1_FOSL2::JUNB0.000347309
MA1144.1_FOSL2::JUND0.000347309
MA1135.1_FOSB::JUNB0.000347309
MA0099.3_FOS::JUN0.000347309
MA1132.1_JUN::JUNB0.00125756
MA1142.1_FOSL1::JUND0.00168585Not shown
MA0491.2_JUND0.00168585Not shown
JUNB_HUMAN.H11MO.0.A0.00168585Not shown
JUN_HUMAN.H11MO.0.A0.00168585Not shown
MA0476.1_FOS0.00171052Not shown

Motif 14/14

Motif IDq-valPWM
JUND_HUMAN.H11MO.0.A9.787e-07
JUN_HUMAN.H11MO.0.A9.787e-07
MA0489.1_JUN(var.2)9.787e-07
MA1135.1_FOSB::JUNB1.4091e-05
MA1138.1_FOSL2::JUNB1.4091e-05
MA1144.1_FOSL2::JUND1.4091e-05Not shown
FOSL1_HUMAN.H11MO.0.A1.89575e-05Not shown
MA0099.3_FOS::JUN2.02654e-05Not shown
FOSL2_HUMAN.H11MO.0.A2.35427e-05Not shown
JUNB_HUMAN.H11MO.0.A4.21164e-05Not shown

Metacluster 2/2

Motif 1/11

Motif IDq-valPWM
MA0837.1_CEBPE2.42032e-06
MA0466.2_CEBPB1.03915e-05
MA0838.1_CEBPG1.27683e-05
CEBPD_HUMAN.H11MO.0.C0.00023924799999999998
CEBPE_HUMAN.H11MO.0.A0.00062778
CEBPB_HUMAN.H11MO.0.A0.000952724Not shown
CEBPA_HUMAN.H11MO.0.A0.0085229Not shown
MA0836.2_CEBPD0.0120909Not shown
MA0102.4_CEBPA0.0174715Not shown
HLF_HUMAN.H11MO.0.C0.034570800000000006Not shown

Motif 2/11

No TOMTOM matches passing threshold

Motif 3/11

No TOMTOM matches passing threshold

Motif 4/11

No TOMTOM matches passing threshold

Motif 5/11

Motif IDq-valPWM
MA1513.1_KLF150.46620200000000006
KLF12_HUMAN.H11MO.0.C0.46620200000000006
SP2_HUMAN.H11MO.1.B0.46620200000000006
SP1_HUMAN.H11MO.1.A0.46620200000000006
SP3_HUMAN.H11MO.0.B0.46620200000000006
MA0753.2_ZNF7400.46620200000000006Not shown
SP2_HUMAN.H11MO.0.A0.46620200000000006Not shown
SP1_HUMAN.H11MO.0.A0.46620200000000006Not shown
TAF1_HUMAN.H11MO.0.A0.46620200000000006Not shown
MA0516.2_SP20.46620200000000006Not shown

Motif 6/11

No TOMTOM matches passing threshold

Motif 7/11

No TOMTOM matches passing threshold

Motif 8/11

No TOMTOM matches passing threshold

Motif 9/11

No TOMTOM matches passing threshold

Motif 10/11

No TOMTOM matches passing threshold

Motif 11/11

No TOMTOM matches passing threshold