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_fold2/CEBPB_multitask_profile_fold2_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold2/CEBPB_multitask_profile_fold2_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_fold2/CEBPB_multitask_profile_fold2_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 [05:57<00:00,  1.31s/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

9379 seqlets

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

Pattern 2/14

902 seqlets

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

Pattern 3/14

583 seqlets

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

Pattern 4/14

419 seqlets

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

Pattern 5/14

356 seqlets

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

Pattern 6/14

261 seqlets

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

Pattern 7/14

179 seqlets

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

Pattern 8/14

102 seqlets

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

Pattern 9/14

93 seqlets

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

Pattern 10/14

91 seqlets

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

Pattern 11/14

42 seqlets

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

Pattern 12/14

38 seqlets

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

Pattern 13/14

35 seqlets

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

Pattern 14/14

31 seqlets

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

Metacluster 2/2

Pattern 1/11

330 seqlets

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

Pattern 2/11

251 seqlets

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

Pattern 3/11

238 seqlets

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

Pattern 4/11

216 seqlets

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

Pattern 5/11

201 seqlets

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

Pattern 6/11

198 seqlets

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

Pattern 7/11

135 seqlets

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

Pattern 8/11

121 seqlets

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

Pattern 9/11

76 seqlets

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

Pattern 10/11

74 seqlets

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

Pattern 11/11

39 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
19379
2902
3583
4419
5356
6261
7179
8102
993
1091
1142
1238
1335
1431

Metacluster 2/2

#SeqletsForwardReverse
1330
2251
3238
4216
5201
6198
7135
8121
976
1074
1139

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.A1.18158e-09
CEBPD_HUMAN.H11MO.0.C1.53498e-09
CEBPA_HUMAN.H11MO.0.A1.12976e-06
MA0836.2_CEBPD1.31477e-05
MA0102.4_CEBPA0.00011745299999999999
MA0837.1_CEBPE0.00041485800000000005Not shown
MA0466.2_CEBPB0.00042371300000000004Not shown
MA0838.1_CEBPG0.0008040739999999999Not shown
MA0025.2_NFIL30.00159415Not shown
DBP_HUMAN.H11MO.0.B0.00312872Not shown

Motif 2/14

Motif IDq-valPWM
JUN_HUMAN.H11MO.0.A1.9562699999999998e-05
FOSB_HUMAN.H11MO.0.A2.90612e-05
JUND_HUMAN.H11MO.0.A2.90612e-05
FOSL1_HUMAN.H11MO.0.A2.90612e-05
FOSL2_HUMAN.H11MO.0.A2.90612e-05
MA1130.1_FOSL2::JUN2.90612e-05Not shown
MA1128.1_FOSL1::JUN2.90612e-05Not shown
MA1141.1_FOS::JUND2.90612e-05Not shown
MA1622.1_Smad2::Smad33.01375e-05Not shown
MA0099.3_FOS::JUN4.33824e-05Not shown

Motif 3/14

Motif IDq-valPWM
MA0139.1_CTCF9.34998e-17
CTCF_HUMAN.H11MO.0.A2.64856e-13
CTCFL_HUMAN.H11MO.0.A7.55064e-08
MA1102.2_CTCFL3.9913800000000004e-05
MA1568.1_TCF21(var.2)0.15146500000000002
ZIC3_HUMAN.H11MO.0.B0.183949Not shown
MA1638.1_HAND20.183949Not shown
SNAI1_HUMAN.H11MO.0.C0.190334Not shown
MA0155.1_INSM10.30282Not shown
PLAL1_HUMAN.H11MO.0.D0.30282Not shown

Motif 4/14

Motif IDq-valPWM
CEBPE_HUMAN.H11MO.0.A0.000478805
MA0837.1_CEBPE0.00074463
MA0466.2_CEBPB0.00074463
MA0838.1_CEBPG0.00141091
CEBPD_HUMAN.H11MO.0.C0.036599400000000004
CEBPB_HUMAN.H11MO.0.A0.036599400000000004Not shown
MA0862.1_GMEB20.038676499999999996Not shown
HLF_HUMAN.H11MO.0.C0.058324400000000005Not shown
MA0836.2_CEBPD0.08389060000000001Not shown
DBP_HUMAN.H11MO.0.B0.08389060000000001Not shown

Motif 5/14

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C0.0651597
CEBPB_HUMAN.H11MO.0.A0.0651597
CEBPA_HUMAN.H11MO.0.A0.0651597
MA0837.1_CEBPE0.0651597
CEBPE_HUMAN.H11MO.0.A0.0651597
MA0466.2_CEBPB0.0651597Not shown
MA0836.2_CEBPD0.06921089999999999Not shown
MA0838.1_CEBPG0.06921089999999999Not shown
MA0102.4_CEBPA0.102256Not shown
NRL_HUMAN.H11MO.0.D0.157677Not shown

Motif 6/14

Motif IDq-valPWM
FOXA1_HUMAN.H11MO.0.A1.7137900000000002e-06
FOXM1_HUMAN.H11MO.0.A1.7137900000000002e-06
FOXA2_HUMAN.H11MO.0.A3.3028300000000004e-06
FOXF2_HUMAN.H11MO.0.D6.22446e-06
FOXA3_HUMAN.H11MO.0.B1.4666500000000001e-05
MA0846.1_FOXC24.42297e-05Not shown
FOXD3_HUMAN.H11MO.0.D4.42297e-05Not shown
MA0847.2_FOXD26.43295e-05Not shown
FOXC1_HUMAN.H11MO.0.C0.000150629Not shown
MA0032.2_FOXC10.00108742Not shown

Motif 7/14

Motif IDq-valPWM
HNF4A_HUMAN.H11MO.0.A2.83019e-05
HNF4G_HUMAN.H11MO.0.B2.83019e-05
MA0115.1_NR1H2::RXRA0.00144241
NR1H2_HUMAN.H11MO.0.D0.0015477
MA0677.1_Nr2f60.0015477
MA1550.1_PPARD0.0015477Not shown
MA1111.1_NR2F20.0027346999999999996Not shown
COT2_HUMAN.H11MO.0.A0.002836Not shown
MA1494.1_HNF4A(var.2)0.00315459Not shown
MA1537.1_NR2F1(var.2)0.0038080999999999996Not shown

Motif 8/14

Motif IDq-valPWM
MA0766.2_GATA52.94455e-06
GATA4_HUMAN.H11MO.0.A0.00211689
GATA2_HUMAN.H11MO.1.A0.00259211
GATA1_HUMAN.H11MO.1.A0.00262897
GATA6_HUMAN.H11MO.0.A0.00914711
TAL1_HUMAN.H11MO.0.A0.00914711Not shown
MA0140.2_GATA1::TAL10.00931149Not shown
GATA1_HUMAN.H11MO.0.A0.0187864Not shown
GATA2_HUMAN.H11MO.0.A0.0322606Not shown
MA0037.3_GATA30.038179199999999996Not shown

Motif 9/14

Motif IDq-valPWM
FOXF2_HUMAN.H11MO.0.D0.128976
MA0849.1_FOXO60.128976
FOXA2_HUMAN.H11MO.0.A0.128976
FOXM1_HUMAN.H11MO.0.A0.128976
MA0031.1_FOXD10.128976
MA0042.2_FOXI10.128976Not shown
FOXA1_HUMAN.H11MO.0.A0.128976Not shown
MA0848.1_FOXO40.128976Not shown
MA1489.1_FOXN30.139596Not shown
MA1103.2_FOXK20.147351Not shown

Motif 10/14

Motif IDq-valPWM
ATF2_HUMAN.H11MO.0.B2.39036e-07
MA1136.1_FOSB::JUNB(var.2)1.50576e-05
MA0605.2_ATF31.50576e-05
MA1145.1_FOSL2::JUND(var.2)1.73077e-05
MA1129.1_FOSL1::JUN(var.2)1.75672e-05
MA1475.1_CREB3L4(var.2)1.75672e-05Not shown
MA1126.1_FOS::JUN(var.2)1.75672e-05Not shown
MA1131.1_FOSL2::JUN(var.2)1.87742e-05Not shown
MA0018.4_CREB11.87742e-05Not shown
MA1139.1_FOSL2::JUNB(var.2)1.87742e-05Not shown

Motif 11/14

Motif IDq-valPWM
MA0037.3_GATA30.16249
MA0766.2_GATA50.16249
GATA1_HUMAN.H11MO.0.A0.16249
TAL1_HUMAN.H11MO.0.A0.197082
GATA2_HUMAN.H11MO.0.A0.21522800000000003
GATA4_HUMAN.H11MO.0.A0.22391Not shown
STAT6_HUMAN.H11MO.0.B0.22391Not shown
GATA1_HUMAN.H11MO.1.A0.358863Not shown
GATA2_HUMAN.H11MO.1.A0.358863Not shown

Motif 12/14

Motif IDq-valPWM
ZN121_HUMAN.H11MO.0.C0.00347365
PAX5_HUMAN.H11MO.0.A0.00662913
MA1633.1_BACH10.0132536
NFE2_HUMAN.H11MO.0.A0.0324275
BACH1_HUMAN.H11MO.0.A0.0324275
BACH2_HUMAN.H11MO.0.A0.0324275Not shown
MA1520.1_MAF0.0762928Not shown
MA1521.1_MAFA0.08086189999999999Not shown
MAFK_HUMAN.H11MO.0.A0.08086189999999999Not shown
MAFF_HUMAN.H11MO.1.B0.08086189999999999Not shown

Motif 13/14

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B1.64402e-05
HNF4A_HUMAN.H11MO.0.A1.64402e-05
MA1494.1_HNF4A(var.2)0.00180056
MA0114.4_HNF4A0.00603216
MA0484.2_HNF4G0.00603216
NR2E3_HUMAN.H11MO.0.C0.018715799999999998Not shown
MA0859.1_Rarg0.040858Not shown
MA0857.1_Rarb0.053450300000000006Not shown
MA0728.1_Nr2f6(var.2)0.0554012Not shown
MA0677.1_Nr2f60.0660313Not shown

Motif 14/14

Motif IDq-valPWM
FOS_HUMAN.H11MO.0.A0.000456355
MA1135.1_FOSB::JUNB0.000582794
MA1144.1_FOSL2::JUND0.000582794
MA1138.1_FOSL2::JUNB0.000582794
MA0099.3_FOS::JUN0.000582794
JUNB_HUMAN.H11MO.0.A0.000782818Not shown
FOSB_HUMAN.H11MO.0.A0.00131275Not shown
MA0476.1_FOS0.00249234Not shown
FOSL1_HUMAN.H11MO.0.A0.00268264Not shown
MA1132.1_JUN::JUNB0.00326214Not shown

Metacluster 2/2

Motif 1/11

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C1.53092e-05
MA0837.1_CEBPE1.53092e-05
MA0466.2_CEBPB2.54511e-05
CEBPB_HUMAN.H11MO.0.A2.54511e-05
MA0838.1_CEBPG5.75828e-05
CEBPE_HUMAN.H11MO.0.A0.0008486769999999999Not shown
CEBPA_HUMAN.H11MO.0.A0.00135788Not shown
MA0836.2_CEBPD0.00344843Not shown
MA0102.4_CEBPA0.0054104999999999995Not shown
HLF_HUMAN.H11MO.0.C0.0166486Not 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

No TOMTOM matches passing threshold

Motif 6/11

No TOMTOM matches passing threshold

Motif 7/11

No TOMTOM matches passing threshold

Motif 8/11

Motif IDq-valPWM
MAFG_HUMAN.H11MO.0.A0.440821

Motif 9/11

Motif IDq-valPWM
CEBPE_HUMAN.H11MO.0.A2.2741999999999997e-05
MA0837.1_CEBPE2.3839800000000002e-05
MA0466.2_CEBPB3.33943e-05
MA0838.1_CEBPG7.01676e-05
CEBPB_HUMAN.H11MO.0.A0.000186411
CEBPD_HUMAN.H11MO.0.C0.000683164Not shown
CEBPA_HUMAN.H11MO.0.A0.000749093Not shown
MA1636.1_CEBPG(var.2)0.00189316Not shown
MA0102.4_CEBPA0.00273957Not shown
MA0025.2_NFIL30.00303712Not shown

Motif 10/11

No TOMTOM matches passing threshold

Motif 11/11

Motif IDq-valPWM
MA0146.2_Zfx0.499855
ZN770_HUMAN.H11MO.0.C0.499855