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: JUND
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/JUND_multitask_profile_fold7/JUND_multitask_profile_fold7_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/JUND_multitask_profile_fold7/JUND_multitask_profile_fold7_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/JUND_multitask_profile_fold7/JUND_multitask_profile_fold7_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%|██████████| 350/350 [03:35<00:00,  1.63it/s]
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 1/12

4294 seqlets

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

Pattern 2/12

1637 seqlets

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

Pattern 3/12

877 seqlets

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

Pattern 4/12

573 seqlets

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

Pattern 5/12

345 seqlets

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

Pattern 6/12

222 seqlets

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

Pattern 7/12

123 seqlets

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

Pattern 8/12

76 seqlets

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

Pattern 9/12

52 seqlets

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

Pattern 10/12

47 seqlets

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

Pattern 11/12

38 seqlets

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

Pattern 12/12

36 seqlets

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

Metacluster 2/2

Pattern 1/12

166 seqlets

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

Pattern 2/12

149 seqlets

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

Pattern 3/12

139 seqlets

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

Pattern 4/12

84 seqlets

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

Pattern 5/12

81 seqlets

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

Pattern 6/12

77 seqlets

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

Pattern 7/12

70 seqlets

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

Pattern 8/12

67 seqlets

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

Pattern 9/12

63 seqlets

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

Pattern 10/12

52 seqlets

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

Pattern 11/12

50 seqlets

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

Pattern 12/12

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
14294
21637
3877
4573
5345
6222
7123
876
952
1047
1138
1236

Metacluster 2/2

#SeqletsForwardReverse
1166
2149
3139
484
581
677
770
867
963
1052
1150
1241

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 1/12

Motif IDq-valPWM
MA0099.3_FOS::JUN9.923350000000001e-07
FOSL2_HUMAN.H11MO.0.A9.923350000000001e-07
MA1130.1_FOSL2::JUN9.923350000000001e-07
MA1141.1_FOS::JUND9.923350000000001e-07
MA1144.1_FOSL2::JUND1.10016e-06
JUN_HUMAN.H11MO.0.A1.3752000000000002e-06Not shown
MA1128.1_FOSL1::JUN1.61735e-06Not shown
MA1138.1_FOSL2::JUNB1.61735e-06Not shown
MA0477.2_FOSL11.61735e-06Not shown
MA1622.1_Smad2::Smad31.65024e-06Not shown

Motif 2/12

Motif IDq-valPWM
MA0605.2_ATF37.791490000000001e-07
MA1136.1_FOSB::JUNB(var.2)1.7041599999999999e-06
JDP2_HUMAN.H11MO.0.D1.81673e-06
MA1145.1_FOSL2::JUND(var.2)2.68693e-06
MA1129.1_FOSL1::JUN(var.2)2.72509e-06
MA1139.1_FOSL2::JUNB(var.2)2.72509e-06Not shown
MA1126.1_FOS::JUN(var.2)2.98228e-06Not shown
MA0656.1_JDP2(var.2)3.28111e-06Not shown
MA1133.1_JUN::JUNB(var.2)3.28111e-06Not shown
ATF2_HUMAN.H11MO.0.B3.2860199999999997e-06Not shown

Motif 3/12

Motif IDq-valPWM
MA1636.1_CEBPG(var.2)0.000507326
MA0833.2_ATF40.000507326
ATF4_HUMAN.H11MO.0.A0.000507326
CEBPG_HUMAN.H11MO.0.B0.0007378510000000001
DDIT3_HUMAN.H11MO.0.D0.000978851
BATF_HUMAN.H11MO.1.A0.000978851Not shown
CEBPD_HUMAN.H11MO.0.C0.00322921Not shown
CEBPB_HUMAN.H11MO.0.A0.00330066Not shown
MA0837.1_CEBPE0.00348129Not shown
MA0466.2_CEBPB0.00353655Not shown

Motif 4/12

Motif IDq-valPWM
BACH2_HUMAN.H11MO.0.A0.00210259
NF2L2_HUMAN.H11MO.0.A0.00210259
MA1622.1_Smad2::Smad30.00210259
MA0835.2_BATF30.00210259
MA0476.1_FOS0.00210259
MAFK_HUMAN.H11MO.0.A0.00210259Not shown
MAFG_HUMAN.H11MO.0.A0.00210259Not shown
NFE2_HUMAN.H11MO.0.A0.00210259Not shown
MA1633.1_BACH10.00210259Not shown
MA0490.2_JUNB0.00210259Not shown

Motif 5/12

Motif IDq-valPWM
MA0146.2_Zfx0.13511099999999998
SP2_HUMAN.H11MO.0.A0.13511099999999998
USF2_HUMAN.H11MO.0.A0.13511099999999998
MA0830.2_TCF40.371771
SP1_HUMAN.H11MO.0.A0.371771
MA1513.1_KLF150.371771Not shown
SRBP2_HUMAN.H11MO.0.B0.371771Not shown
MA1102.2_CTCFL0.371771Not shown
MA1650.1_ZBTB140.371771Not shown
ATF6A_HUMAN.H11MO.0.B0.371771Not shown

Motif 6/12

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C0.086735
MA0466.2_CEBPB0.11604500000000001
MA0837.1_CEBPE0.11604500000000001
MA0838.1_CEBPG0.11604500000000001
BATF_HUMAN.H11MO.1.A0.30098
CEBPB_HUMAN.H11MO.0.A0.30098Not shown
HLF_HUMAN.H11MO.0.C0.30098Not shown
ATF4_HUMAN.H11MO.0.A0.317992Not shown
HLTF_HUMAN.H11MO.0.D0.317992Not shown
CEBPA_HUMAN.H11MO.0.A0.31958600000000004Not shown

Motif 7/12

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A1.96822e-06
SP1_HUMAN.H11MO.0.A1.96822e-06
SP3_HUMAN.H11MO.0.B4.818809999999999e-06
ZN770_HUMAN.H11MO.0.C4.02965e-05
MAZ_HUMAN.H11MO.0.A5.9e-05
KLF3_HUMAN.H11MO.0.B7.25456e-05Not shown
TBX15_HUMAN.H11MO.0.D9.022290000000001e-05Not shown
WT1_HUMAN.H11MO.0.C0.000122948Not shown
PATZ1_HUMAN.H11MO.0.C0.000122948Not shown
ZFX_HUMAN.H11MO.1.A0.000130431Not shown

Motif 8/12

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D3.71156e-05
MA1125.1_ZNF3840.025590099999999998
PRDM6_HUMAN.H11MO.0.C0.025590099999999998
FOXL1_HUMAN.H11MO.0.D0.025590099999999998
FOXG1_HUMAN.H11MO.0.D0.051089300000000004
ANDR_HUMAN.H11MO.0.A0.108319Not shown
MA0679.2_ONECUT10.11018800000000001Not shown
FOXJ3_HUMAN.H11MO.0.A0.11225Not shown
ONEC2_HUMAN.H11MO.0.D0.136148Not shown
FUBP1_HUMAN.H11MO.0.D0.146971Not shown

Motif 9/12

No TOMTOM matches passing threshold

Motif 10/12

No TOMTOM matches passing threshold

Motif 11/12

Motif IDq-valPWM
BATF_HUMAN.H11MO.1.A0.00669322
CEBPD_HUMAN.H11MO.0.C0.00669322
ATF4_HUMAN.H11MO.0.A0.00669322
CEBPG_HUMAN.H11MO.0.B0.00693831
CEBPB_HUMAN.H11MO.0.A0.0255462
MA0466.2_CEBPB0.0255462Not shown
MA0837.1_CEBPE0.0255462Not shown
MA0838.1_CEBPG0.0306843Not shown
MA0833.2_ATF40.0717119Not shown
CEBPA_HUMAN.H11MO.0.A0.0717119Not shown

Motif 12/12

Motif IDq-valPWM
MA0478.1_FOSL20.00023056700000000002
MA0591.1_Bach1::Mafk0.00026020599999999997
FOSL1_HUMAN.H11MO.0.A0.000722176
MA0150.2_Nfe2l20.000722176
JUND_HUMAN.H11MO.0.A0.00173026
MA1134.1_FOS::JUNB0.00173716Not shown
FOSL2_HUMAN.H11MO.0.A0.00235442Not shown
MA1138.1_FOSL2::JUNB0.00248092Not shown
MA1144.1_FOSL2::JUND0.00248092Not shown
BACH1_HUMAN.H11MO.0.A0.00248092Not shown

Metacluster 2/2

Motif 1/12

No TOMTOM matches passing threshold

Motif 2/12

No TOMTOM matches passing threshold

Motif 3/12

No TOMTOM matches passing threshold

Motif 4/12

No TOMTOM matches passing threshold

Motif 5/12

No TOMTOM matches passing threshold

Motif 6/12

Motif IDq-valPWM
MA0499.2_MYOD10.141967
PTF1A_HUMAN.H11MO.1.B0.141967
MYF6_HUMAN.H11MO.0.C0.141967
MA0138.2_REST0.141967
MA0500.2_MYOG0.141967
BHA15_HUMAN.H11MO.0.B0.141967Not shown
MYOD1_HUMAN.H11MO.1.A0.141967Not shown
MA0521.1_Tcf120.141967Not shown
TFE2_HUMAN.H11MO.0.A0.141967Not shown
MA1631.1_ASCL1(var.2)0.141967Not shown

Motif 7/12

Motif IDq-valPWM
MA0154.4_EBF10.16385999999999998
MA0116.1_Znf4230.16385999999999998
CTCF_HUMAN.H11MO.0.A0.16385999999999998
MECP2_HUMAN.H11MO.0.C0.16385999999999998
MA0815.1_TFAP2C(var.3)0.16385999999999998
MA0810.1_TFAP2A(var.2)0.16385999999999998Not shown
MA0872.1_TFAP2A(var.3)0.16385999999999998Not shown
MA0139.1_CTCF0.16385999999999998Not shown
COE1_HUMAN.H11MO.0.A0.16385999999999998Not shown
MA0524.2_TFAP2C0.21819299999999997Not shown

Motif 8/12

Motif IDq-valPWM
MA1143.1_FOSL1::JUND(var.2)0.00194417
CREB1_HUMAN.H11MO.0.A0.0170166
ATF1_HUMAN.H11MO.0.B0.0170166
MA0604.1_Atf10.017305900000000003
MA1129.1_FOSL1::JUN(var.2)0.017305900000000003
MA1136.1_FOSB::JUNB(var.2)0.017305900000000003Not shown
BATF_HUMAN.H11MO.1.A0.017305900000000003Not shown
CREB5_HUMAN.H11MO.0.D0.017305900000000003Not shown
MA1131.1_FOSL2::JUN(var.2)0.017305900000000003Not shown
ATF7_HUMAN.H11MO.0.D0.0219149Not shown

Motif 9/12

Motif IDq-valPWM
RFX1_HUMAN.H11MO.0.B0.159405
NR2C1_HUMAN.H11MO.0.C0.159405
MA1513.1_KLF150.159405
AP2B_HUMAN.H11MO.0.B0.159405
ZBT7B_HUMAN.H11MO.0.D0.159405
AP2D_HUMAN.H11MO.0.D0.159405Not shown
SP1_HUMAN.H11MO.0.A0.159405Not shown
MA1615.1_Plagl10.159405Not shown
PLAL1_HUMAN.H11MO.0.D0.159405Not shown
MA0163.1_PLAG10.159405Not shown

Motif 10/12

No TOMTOM matches passing threshold

Motif 11/12

Motif IDq-valPWM
COE1_HUMAN.H11MO.0.A0.47778400000000004
MA0154.4_EBF10.47778400000000004
SP1_HUMAN.H11MO.0.A0.47778400000000004
ZN436_HUMAN.H11MO.0.C0.47778400000000004
MA0872.1_TFAP2A(var.3)0.47778400000000004
MA0155.1_INSM10.47778400000000004Not shown
MA0815.1_TFAP2C(var.3)0.47778400000000004Not shown
MA0737.1_GLIS30.47778400000000004Not shown
MA0813.1_TFAP2B(var.3)0.47778400000000004Not shown
GLIS1_HUMAN.H11MO.0.D0.47778400000000004Not shown

Motif 12/12

Motif IDq-valPWM
JUND_HUMAN.H11MO.0.A0.0242049
MA1138.1_FOSL2::JUNB0.0242049
MA1135.1_FOSB::JUNB0.0242049
MA1144.1_FOSL2::JUND0.0242049
JUN_HUMAN.H11MO.0.A0.0242049
FOS_HUMAN.H11MO.0.A0.0242049Not shown
MA0478.1_FOSL20.0242049Not shown
MA0655.1_JDP20.0242049Not shown
MA1130.1_FOSL2::JUN0.0251638Not shown
MA0099.3_FOS::JUN0.0251638Not shown