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_count_tfm.h5
Importance score key: count_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_count
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 1/12

11657 seqlets

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

Pattern 2/12

948 seqlets

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

Pattern 3/12

638 seqlets

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

Pattern 4/12

257 seqlets

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

Pattern 5/12

237 seqlets

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

Pattern 6/12

163 seqlets

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

Pattern 7/12

128 seqlets

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

Pattern 8/12

128 seqlets

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

Pattern 9/12

82 seqlets

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

Pattern 10/12

35 seqlets

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

Pattern 11/12

33 seqlets

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

Pattern 12/12

30 seqlets

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

Metacluster 2/2

Pattern 1/10

283 seqlets

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

Pattern 2/10

160 seqlets

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

Pattern 3/10

136 seqlets

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

Pattern 4/10

116 seqlets

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

Pattern 5/10

104 seqlets

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

Pattern 6/10

91 seqlets

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

Pattern 7/10

64 seqlets

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

Pattern 8/10

58 seqlets

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

Pattern 9/10

57 seqlets

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

Pattern 10/10

56 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
111657
2948
3638
4257
5237
6163
7128
8128
982
1035
1133
1230

Metacluster 2/2

#SeqletsForwardReverse
1283
2160
3136
4116
5104
691
764
858
957
1056

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
CEBPB_HUMAN.H11MO.0.A1.7856999999999998e-10
CEBPD_HUMAN.H11MO.0.C7.60232e-09
CEBPA_HUMAN.H11MO.0.A1.5153799999999998e-06
MA0836.2_CEBPD3.0011500000000002e-05
MA0837.1_CEBPE0.000179553
MA0466.2_CEBPB0.000185865Not shown
MA0102.4_CEBPA0.000189321Not shown
MA0838.1_CEBPG0.00037020900000000003Not shown
MA0025.2_NFIL30.000816505Not shown
NFIL3_HUMAN.H11MO.0.D0.00205366Not shown

Motif 2/12

Motif IDq-valPWM
FOSL1_HUMAN.H11MO.0.A3.44279e-06
FOSB_HUMAN.H11MO.0.A4.19915e-06
JUND_HUMAN.H11MO.0.A8.39829e-06
JUN_HUMAN.H11MO.0.A9.517200000000001e-06
FOSL2_HUMAN.H11MO.0.A9.517200000000001e-06
FOS_HUMAN.H11MO.0.A9.517200000000001e-06Not shown
JUNB_HUMAN.H11MO.0.A1.38151e-05Not shown
MA0478.1_FOSL21.38151e-05Not shown
MA0099.3_FOS::JUN1.89915e-05Not shown
MA1130.1_FOSL2::JUN3.41846e-05Not shown

Motif 3/12

Motif IDq-valPWM
MA0139.1_CTCF1.69985e-17
CTCF_HUMAN.H11MO.0.A1.69667e-15
CTCFL_HUMAN.H11MO.0.A6.750649999999999e-09
MA1102.2_CTCFL6.421939999999999e-05
MA1568.1_TCF21(var.2)0.133216
MA1638.1_HAND20.153826Not shown
ZIC3_HUMAN.H11MO.0.B0.25531Not shown
SNAI1_HUMAN.H11MO.0.C0.25531Not shown
ZIC2_HUMAN.H11MO.0.D0.378884Not shown
MA0155.1_INSM10.378884Not shown

Motif 4/12

Motif IDq-valPWM
CTCFL_HUMAN.H11MO.0.A7.30918e-05
MA1102.2_CTCFL0.000504462
CTCF_HUMAN.H11MO.0.A0.000525293
MA0139.1_CTCF0.00270395
SP2_HUMAN.H11MO.0.A0.00279211
SP3_HUMAN.H11MO.0.B0.0088664Not shown
MA1513.1_KLF150.00927337Not shown
SP2_HUMAN.H11MO.1.B0.0164292Not shown
SP1_HUMAN.H11MO.0.A0.025965600000000002Not shown
SP4_HUMAN.H11MO.0.A0.025965600000000002Not shown

Motif 5/12

Motif IDq-valPWM
FOXA1_HUMAN.H11MO.0.A1.1056800000000001e-07
FOXA2_HUMAN.H11MO.0.A1.28533e-07
FOXM1_HUMAN.H11MO.0.A1.09051e-06
FOXA3_HUMAN.H11MO.0.B4.83517e-06
FOXF2_HUMAN.H11MO.0.D4.83517e-06
MA0846.1_FOXC26.04396e-06Not shown
FOXD3_HUMAN.H11MO.0.D2.2702800000000002e-05Not shown
MA0847.2_FOXD22.7177199999999998e-05Not shown
FOXC1_HUMAN.H11MO.0.C6.99258e-05Not shown
MA0032.2_FOXC10.000269119Not shown

Motif 6/12

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A0.00129154
MA0850.1_FOXP30.00129154
FOXF2_HUMAN.H11MO.0.D0.00129154
MA0593.1_FOXP20.00129154
FOXA1_HUMAN.H11MO.0.A0.00129154
FOXD3_HUMAN.H11MO.0.D0.00129154Not shown
MA0851.1_Foxj30.00220096Not shown
MA1683.1_FOXA30.00220096Not shown
MA0033.2_FOXL10.00294668Not shown
FOXM1_HUMAN.H11MO.0.A0.00294668Not shown

Motif 7/12

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B9.30167e-11
HNF4A_HUMAN.H11MO.0.A1.99017e-09
MA1494.1_HNF4A(var.2)0.000127107
MA0856.1_RXRG0.000127107
MA0677.1_Nr2f60.000127107
MA0512.2_Rxra0.000127107Not shown
MA1550.1_PPARD0.000127107Not shown
MA1574.1_THRB0.000127107Not shown
MA0855.1_RXRB0.000140224Not shown
MA1148.1_PPARA::RXRA0.000149641Not shown

Motif 8/12

Motif IDq-valPWM
GATA1_HUMAN.H11MO.1.A0.00028967
GATA2_HUMAN.H11MO.1.A0.00028967
GATA6_HUMAN.H11MO.0.A0.00028967
GATA2_HUMAN.H11MO.0.A0.000403469
GATA4_HUMAN.H11MO.0.A0.000451491
MA0037.3_GATA30.00119475Not shown
MA0036.3_GATA20.00119475Not shown
MA1104.2_GATA60.00146357Not shown
MA0482.2_GATA40.00474711Not shown
GATA1_HUMAN.H11MO.0.A0.00474711Not shown

Motif 9/12

Motif IDq-valPWM
FOXD2_HUMAN.H11MO.0.D0.0313999
FOXB1_HUMAN.H11MO.0.D0.0313999
HNF1B_HUMAN.H11MO.0.A0.042668300000000006
HNF1B_HUMAN.H11MO.1.A0.057594799999999995
HNF1A_HUMAN.H11MO.0.C0.0724734
CEBPE_HUMAN.H11MO.0.A0.0724734Not shown
MA0046.2_HNF1A0.0911478Not shown
MA0845.1_FOXB10.205708Not shown
MA0032.2_FOXC10.262326Not shown
MA0148.4_FOXA10.262326Not shown

Motif 10/12

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A0.287455
FOXM1_HUMAN.H11MO.0.A0.287455
FOXA1_HUMAN.H11MO.0.A0.443535

Motif 11/12

Motif IDq-valPWM
FOSB_HUMAN.H11MO.0.A0.00036655
FOS_HUMAN.H11MO.0.A0.00036655
MA0478.1_FOSL20.000567563
JUND_HUMAN.H11MO.0.A0.000567563
FOSL1_HUMAN.H11MO.0.A0.000567563
MA0489.1_JUN(var.2)0.0007292310000000001Not shown
FOSL2_HUMAN.H11MO.0.A0.0007292310000000001Not shown
MA0655.1_JDP20.0007292310000000001Not shown
JUN_HUMAN.H11MO.0.A0.0007292310000000001Not shown
MA1142.1_FOSL1::JUND0.00117643Not shown

Motif 12/12

Motif IDq-valPWM
FOXA1_HUMAN.H11MO.0.A0.00222837
FOXK1_HUMAN.H11MO.0.A0.00222837
FOXA3_HUMAN.H11MO.0.B0.00222837
FOXA2_HUMAN.H11MO.0.A0.00280348
FOXM1_HUMAN.H11MO.0.A0.00353338
FOXF2_HUMAN.H11MO.0.D0.00470155Not shown
MA0042.2_FOXI10.00474674Not shown
MA0849.1_FOXO60.00474674Not shown
MA0848.1_FOXO40.00889214Not shown
MA1683.1_FOXA30.0114396Not shown

Metacluster 2/2

Motif 1/10

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C1.8455200000000002e-07
CEBPB_HUMAN.H11MO.0.A2.5094e-07
CEBPA_HUMAN.H11MO.0.A2.5938600000000003e-05
MA0837.1_CEBPE0.00011516799999999999
MA0466.2_CEBPB0.000117185
MA0836.2_CEBPD0.00023976799999999999Not shown
MA0838.1_CEBPG0.00030890599999999996Not shown
MA1636.1_CEBPG(var.2)0.00055502Not shown
MA0025.2_NFIL30.000717118Not shown
MA0102.4_CEBPA0.000717118Not shown

Motif 2/10

Motif IDq-valPWM
ZN341_HUMAN.H11MO.0.C0.07258300000000001
ZN281_HUMAN.H11MO.0.A0.09412310000000002
MA1627.1_Wt10.09412310000000002
MA1653.1_ZNF1480.09412310000000002
VEZF1_HUMAN.H11MO.0.C0.09412310000000002
SPIC_HUMAN.H11MO.0.D0.09412310000000002Not shown
MA1522.1_MAZ0.09412310000000002Not shown
MA1630.1_Znf2810.09412310000000002Not shown
MAZ_HUMAN.H11MO.0.A0.09412310000000002Not shown
MA0528.2_ZNF2630.09412310000000002Not shown

Motif 3/10

Motif IDq-valPWM
MA1529.1_NHLH20.0772412
MA1596.1_ZNF4600.273575
BHA15_HUMAN.H11MO.0.B0.273575
HEN1_HUMAN.H11MO.0.C0.273575
TAF1_HUMAN.H11MO.0.A0.274161
ZN554_HUMAN.H11MO.1.D0.296122Not shown
ATF4_HUMAN.H11MO.0.A0.296122Not shown
CEBPG_HUMAN.H11MO.0.B0.296122Not shown
MA1583.1_ZFP570.296122Not shown
MA0048.2_NHLH10.379831Not shown

Motif 4/10

Motif IDq-valPWM
MA0116.1_Znf4230.343913
ZNF41_HUMAN.H11MO.1.C0.343913
KLF14_HUMAN.H11MO.0.D0.343913
SNAI1_HUMAN.H11MO.0.C0.343913
HTF4_HUMAN.H11MO.0.A0.343913
KLF15_HUMAN.H11MO.0.A0.343913Not shown
MA1620.1_Ptf1a(var.3)0.343913Not shown
BRAC_HUMAN.H11MO.0.A0.343913Not shown
MA1102.2_CTCFL0.343913Not shown
KLF13_HUMAN.H11MO.0.D0.343913Not shown

Motif 5/10

No TOMTOM matches passing threshold

Motif 6/10

Motif IDq-valPWM
TFAP4_HUMAN.H11MO.0.A0.0688817
MA1629.1_Zic20.131128
ZIC2_HUMAN.H11MO.0.D0.131128
ZIC3_HUMAN.H11MO.0.B0.131128
LYL1_HUMAN.H11MO.0.A0.307664
HTF4_HUMAN.H11MO.0.A0.372947Not shown
FIGLA_HUMAN.H11MO.0.D0.402339Not shown
MA0796.1_TGIF10.402339Not shown
NDF1_HUMAN.H11MO.0.A0.402339Not shown
SMAD3_HUMAN.H11MO.0.B0.402339Not shown

Motif 7/10

No TOMTOM matches passing threshold

Motif 8/10

No TOMTOM matches passing threshold

Motif 9/10

Motif IDq-valPWM
EGR2_HUMAN.H11MO.0.A0.00633282
ZN281_HUMAN.H11MO.0.A0.00633282
MA1627.1_Wt10.00633282
ZN148_HUMAN.H11MO.0.D0.00633282
MA1630.1_Znf2810.00633282
ZBT17_HUMAN.H11MO.0.A0.00633282Not shown
MA1653.1_ZNF1480.00665454Not shown
MAZ_HUMAN.H11MO.0.A0.0107413Not shown
MA0528.2_ZNF2630.013500999999999999Not shown
EGR2_HUMAN.H11MO.1.A0.013500999999999999Not shown

Motif 10/10

No TOMTOM matches passing threshold