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_fold3/CEBPB_multitask_profile_fold3_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold3/CEBPB_multitask_profile_fold3_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_fold3/CEBPB_multitask_profile_fold3_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 [08:26<00:00,  1.86s/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 1/13

10577 seqlets

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

Pattern 2/13

757 seqlets

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

Pattern 3/13

580 seqlets

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

Pattern 4/13

326 seqlets

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

Pattern 5/13

265 seqlets

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

Pattern 6/13

259 seqlets

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

Pattern 7/13

177 seqlets

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

Pattern 8/13

148 seqlets

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

Pattern 9/13

112 seqlets

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

Pattern 10/13

105 seqlets

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

Pattern 11/13

56 seqlets

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

Pattern 12/13

55 seqlets

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

Pattern 13/13

46 seqlets

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

Metacluster 2/2

Pattern 1/15

452 seqlets

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

Pattern 2/15

238 seqlets

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

Pattern 3/15

237 seqlets

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

Pattern 4/15

231 seqlets

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

Pattern 5/15

228 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

157 seqlets

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

Pattern 8/15

146 seqlets

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

Pattern 9/15

143 seqlets

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

Pattern 10/15

137 seqlets

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

Pattern 11/15

135 seqlets

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

Pattern 12/15

124 seqlets

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

Pattern 13/15

120 seqlets

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

Pattern 14/15

119 seqlets

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

Pattern 15/15

92 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
110577
2757
3580
4326
5265
6259
7177
8148
9112
10105
1156
1255
1346

Metacluster 2/2

#SeqletsForwardReverse
1452
2238
3237
4231
5228
6202
7157
8146
9143
10137
11135
12124
13120
14119
1592

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 1/13

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A1.6020699999999998e-09
CEBPD_HUMAN.H11MO.0.C2.04296e-09
CEBPA_HUMAN.H11MO.0.A7.685630000000001e-07
MA0836.2_CEBPD3.45188e-05
MA0102.4_CEBPA0.00017905900000000002
MA0837.1_CEBPE0.000527744Not shown
MA0466.2_CEBPB0.000532817Not shown
MA0838.1_CEBPG0.000846419Not shown
MA0025.2_NFIL30.00127265Not shown
DBP_HUMAN.H11MO.0.B0.00238437Not shown

Motif 2/13

Motif IDq-valPWM
JUN_HUMAN.H11MO.0.A4.6458599999999996e-05
FOSL2_HUMAN.H11MO.0.A4.6458599999999996e-05
NFE2_HUMAN.H11MO.0.A6.3378e-05
MA1141.1_FOS::JUND6.3378e-05
MA1130.1_FOSL2::JUN6.3378e-05
FOSL1_HUMAN.H11MO.0.A6.3378e-05Not shown
MA1128.1_FOSL1::JUN6.3378e-05Not shown
BACH2_HUMAN.H11MO.0.A6.3378e-05Not shown
MA0099.3_FOS::JUN6.3378e-05Not shown
FOSB_HUMAN.H11MO.0.A6.3378e-05Not shown

Motif 3/13

Motif IDq-valPWM
MA0139.1_CTCF1.0924699999999999e-19
CTCF_HUMAN.H11MO.0.A1.95814e-15
CTCFL_HUMAN.H11MO.0.A3.4476599999999997e-09
MA1102.2_CTCFL6.44106e-05
MA1568.1_TCF21(var.2)0.126573
MA1638.1_HAND20.15379500000000002Not shown
SNAI1_HUMAN.H11MO.0.C0.246245Not shown
ZIC3_HUMAN.H11MO.0.B0.257423Not shown
MA0155.1_INSM10.385734Not shown
ZIC2_HUMAN.H11MO.0.D0.385734Not shown

Motif 4/13

Motif IDq-valPWM
MA0139.1_CTCF0.077078
CTCF_HUMAN.H11MO.0.A0.0921168
NGN2_HUMAN.H11MO.0.D0.0921168
MA1638.1_HAND20.0921168
NDF1_HUMAN.H11MO.0.A0.170307
ATOH1_HUMAN.H11MO.0.B0.170307Not shown
CTCFL_HUMAN.H11MO.0.A0.272425Not shown
NDF2_HUMAN.H11MO.0.B0.272425Not shown
MA1568.1_TCF21(var.2)0.316114Not shown
ZIC2_HUMAN.H11MO.0.D0.363619Not shown

Motif 5/13

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.00556373
MA1102.2_CTCFL0.00556373
SP3_HUMAN.H11MO.0.B0.013910399999999998
SP4_HUMAN.H11MO.0.A0.0215821
PATZ1_HUMAN.H11MO.0.C0.027583
WT1_HUMAN.H11MO.0.C0.031031Not shown
CTCF_HUMAN.H11MO.0.A0.0462491Not shown
SP4_HUMAN.H11MO.1.A0.0462491Not shown
MXI1_HUMAN.H11MO.0.A0.0462491Not shown
KLF3_HUMAN.H11MO.0.B0.0462491Not shown

Motif 6/13

Motif IDq-valPWM
FOXM1_HUMAN.H11MO.0.A1.37357e-07
FOXA1_HUMAN.H11MO.0.A3.6436300000000004e-07
FOXA2_HUMAN.H11MO.0.A4.3737300000000006e-06
FOXF2_HUMAN.H11MO.0.D4.3737300000000006e-06
FOXA3_HUMAN.H11MO.0.B6.99796e-06
FOXD3_HUMAN.H11MO.0.D2.05509e-05Not shown
MA0846.1_FOXC26.845600000000001e-05Not shown
FOXC1_HUMAN.H11MO.0.C8.83632e-05Not shown
MA0847.2_FOXD29.51566e-05Not shown
FOXD1_HUMAN.H11MO.0.D0.000881842Not shown

Motif 7/13

Motif IDq-valPWM
GATA4_HUMAN.H11MO.0.A3.28544e-05
GATA1_HUMAN.H11MO.1.A3.28544e-05
GATA6_HUMAN.H11MO.0.A3.28544e-05
GATA2_HUMAN.H11MO.0.A4.5761400000000005e-05
GATA2_HUMAN.H11MO.1.A0.000270092
GATA1_HUMAN.H11MO.0.A0.0005016Not shown
TAL1_HUMAN.H11MO.0.A0.0005016Not shown
MA0036.3_GATA20.000703615Not shown
MA0482.2_GATA40.000750523Not shown
MA1104.2_GATA60.000788049Not shown

Motif 8/13

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B7.042680000000001e-06
HNF4A_HUMAN.H11MO.0.A7.828939999999998e-06
MA0484.2_HNF4G7.83217e-06
MA0114.4_HNF4A1.28468e-05
COT2_HUMAN.H11MO.1.A5.8626400000000005e-05
COT2_HUMAN.H11MO.0.A0.000632247Not shown
MA0856.1_RXRG0.00066314Not shown
MA0512.2_Rxra0.0007488880000000001Not shown
MA0677.1_Nr2f60.0007488880000000001Not shown
COT1_HUMAN.H11MO.0.C0.0007488880000000001Not shown

Motif 9/13

Motif IDq-valPWM
HME1_HUMAN.H11MO.0.D0.155773
SHOX2_HUMAN.H11MO.0.D0.155773
SHOX_HUMAN.H11MO.0.D0.155773
GBX1_HUMAN.H11MO.0.D0.16860999999999998
EMX1_HUMAN.H11MO.0.D0.21993200000000002
VSX1_HUMAN.H11MO.0.D0.21993200000000002Not shown
ESX1_HUMAN.H11MO.0.D0.21993200000000002Not shown
EMX2_HUMAN.H11MO.0.D0.249167Not shown
NFYC_HUMAN.H11MO.0.A0.376817Not shown
RX_HUMAN.H11MO.0.D0.40025Not shown

Motif 10/13

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A2.67814e-07
SP3_HUMAN.H11MO.0.B4.65578e-07
SP1_HUMAN.H11MO.0.A4.65578e-07
TBX15_HUMAN.H11MO.0.D2.87848e-06
PATZ1_HUMAN.H11MO.0.C2.87848e-06
KLF16_HUMAN.H11MO.0.D2.87848e-06Not shown
WT1_HUMAN.H11MO.0.C2.87848e-06Not shown
ZN467_HUMAN.H11MO.0.C2.87848e-06Not shown
MAZ_HUMAN.H11MO.0.A7.123550000000001e-06Not shown
VEZF1_HUMAN.H11MO.0.C2.14353e-05Not shown

Motif 11/13

Motif IDq-valPWM
FOSL2_HUMAN.H11MO.0.A1.74236e-07
JUND_HUMAN.H11MO.0.A3.11706e-07
JUN_HUMAN.H11MO.0.A3.11706e-07
FOSL1_HUMAN.H11MO.0.A6.17481e-07
MA1130.1_FOSL2::JUN1.30476e-05
MA1137.1_FOSL1::JUNB1.30476e-05Not shown
JUNB_HUMAN.H11MO.0.A1.30476e-05Not shown
FOSB_HUMAN.H11MO.0.A0.00013755Not shown
FOS_HUMAN.H11MO.0.A0.000178807Not shown
MA0489.1_JUN(var.2)0.000178807Not shown

Motif 12/13

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D4.9704e-05
MA1125.1_ZNF3840.024229499999999998
FOXL1_HUMAN.H11MO.0.D0.024229499999999998
PRDM6_HUMAN.H11MO.0.C0.024229499999999998
FOXG1_HUMAN.H11MO.0.D0.06530069999999999
ANDR_HUMAN.H11MO.0.A0.111321Not shown
MA0679.2_ONECUT10.111321Not shown
FOXJ3_HUMAN.H11MO.0.A0.12026300000000001Not shown
ONEC2_HUMAN.H11MO.0.D0.15991Not shown
FUBP1_HUMAN.H11MO.0.D0.20825900000000003Not shown

Motif 13/13

Motif IDq-valPWM
ZN770_HUMAN.H11MO.0.C8.14296e-07
MA1596.1_ZNF4600.000251504
ZN770_HUMAN.H11MO.1.C0.00120345
MA1587.1_ZNF1350.019163200000000002
PITX2_HUMAN.H11MO.0.D0.059217099999999995
ZSC22_HUMAN.H11MO.0.C0.281896Not shown
IKZF1_HUMAN.H11MO.0.C0.290091Not shown
ZN250_HUMAN.H11MO.0.C0.47169399999999995Not shown

Metacluster 2/2

Motif 1/15

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C0.0006635289999999999
CEBPB_HUMAN.H11MO.0.A0.00109764
MA0837.1_CEBPE0.00109764
MA0466.2_CEBPB0.00109764
MA0838.1_CEBPG0.00209146
CEBPG_HUMAN.H11MO.0.B0.00773719Not shown
ATF4_HUMAN.H11MO.0.A0.00773719Not shown
MA1636.1_CEBPG(var.2)0.00773719Not shown
DDIT3_HUMAN.H11MO.0.D0.00773719Not shown
BATF_HUMAN.H11MO.1.A0.00773719Not shown

Motif 2/15

Motif IDq-valPWM
AP2B_HUMAN.H11MO.0.B0.101877

Motif 3/15

No TOMTOM matches passing threshold

Motif 4/15

No TOMTOM matches passing threshold

Motif 5/15

No TOMTOM matches passing threshold

Motif 6/15

Motif IDq-valPWM
NFIA_HUMAN.H11MO.0.C0.481699
KLF6_HUMAN.H11MO.0.A0.481699
EPAS1_HUMAN.H11MO.0.B0.481699
MA0616.2_HES20.481699
RARB_HUMAN.H11MO.0.D0.481699
ARNT_HUMAN.H11MO.0.B0.481699Not shown
MA0823.1_HEY10.481699Not shown
SP2_HUMAN.H11MO.1.B0.481699Not shown
ZN281_HUMAN.H11MO.0.A0.481699Not shown
SALL4_HUMAN.H11MO.0.B0.481699Not shown

Motif 7/15

Motif IDq-valPWM
SPIC_HUMAN.H11MO.0.D0.40212800000000004
VEZF1_HUMAN.H11MO.1.C0.40212800000000004
ZN263_HUMAN.H11MO.1.A0.40212800000000004
ZSC22_HUMAN.H11MO.0.C0.40212800000000004
WT1_HUMAN.H11MO.1.B0.421966
ZN341_HUMAN.H11MO.0.C0.421966Not shown
ETV7_HUMAN.H11MO.0.D0.421966Not shown
FLI1_HUMAN.H11MO.0.A0.421966Not shown
MA1652.1_ZKSCAN50.421966Not shown
NFAT5_HUMAN.H11MO.0.D0.421966Not shown

Motif 8/15

No TOMTOM matches passing threshold

Motif 9/15

No TOMTOM matches passing threshold

Motif 10/15

No TOMTOM matches passing threshold

Motif 11/15

No TOMTOM matches passing threshold

Motif 12/15

No TOMTOM matches passing threshold

Motif 13/15

No TOMTOM matches passing threshold

Motif 14/15

No TOMTOM matches passing threshold

Motif 15/15

No TOMTOM matches passing threshold