Direct links to results

TF-MoDISco results

Summary of motifs

TOMTOM matches to motifs

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

# 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_fold10/JUND_multitask_profile_fold10_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/JUND_multitask_profile_fold10/JUND_multitask_profile_fold10_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_fold10/JUND_multitask_profile_fold10_profile

# Define paths and constants
input_length = 2114
shap_score_center_size = 400

if tfm_motifs_cache_dir:
    os.makedirs(tfm_motifs_cache_dir, exist_ok=True)

Import SHAP scores and TF-MoDISco results

# 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 [10:43<00:00,  1.84s/it]

# 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

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

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 1/10

7287 seqlets

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

Pattern 2/10

1557 seqlets

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

Pattern 3/10

471 seqlets

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

Pattern 4/10

57 seqlets

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

Pattern 5/10

52 seqlets

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

Pattern 6/10

46 seqlets

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

Pattern 7/10

43 seqlets

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

Pattern 8/10

41 seqlets

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

Pattern 9/10

37 seqlets

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

Pattern 10/10

30 seqlets

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

Metacluster 2/2

Pattern 1/17

120 seqlets

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

Pattern 2/17

117 seqlets

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

Pattern 3/17

110 seqlets

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

Pattern 4/17

97 seqlets

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

Pattern 5/17

85 seqlets

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

Pattern 6/17

79 seqlets

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

Pattern 7/17

70 seqlets

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

Pattern 8/17

65 seqlets

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

Pattern 9/17

65 seqlets

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

Pattern 10/17

61 seqlets

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

Pattern 11/17

57 seqlets

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

Pattern 12/17

55 seqlets

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

Pattern 13/17

51 seqlets

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

Pattern 14/17

50 seqlets

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

Pattern 15/17

42 seqlets

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

Pattern 16/17

33 seqlets

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

Pattern 17/17

31 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.

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

#	Seqlets	Forward	Reverse
1	7287
2	1557
3	471
4	57
5	52
6	46
7	43
8	41
9	37
10	30

Metacluster 2/2

/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)

#	Seqlets	Forward	Reverse
1	120
2	117
3	110
4	97
5	85
6	79
7	70
8	65
9	65
10	61
11	57
12	55
13	51
14	50
15	42
16	33
17	31

Top TOMTOM matches for each motif

Here, the TF-MoDISco motifs are plotted as hCWMs, but the TOMTOM matches are shown as PWMs.

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 1/10

Motif ID	q-val	PWM
JUN_HUMAN.H11MO.0.A	3.4217799999999996e-07
FOSL2_HUMAN.H11MO.0.A	3.4217799999999996e-07
JUND_HUMAN.H11MO.0.A	6.55676e-07
FOSB_HUMAN.H11MO.0.A	6.55676e-07
FOSL1_HUMAN.H11MO.0.A	6.750560000000001e-07
MA0099.3_FOS::JUN	2.13117e-06	Not shown
MA1130.1_FOSL2::JUN	3.65343e-06	Not shown
MA1128.1_FOSL1::JUN	7.520410000000001e-06	Not shown
MA1141.1_FOS::JUND	7.520410000000001e-06	Not shown
MA1622.1_Smad2::Smad3	8.122039999999999e-06	Not shown

Motif 2/10

Motif ID	q-val	PWM
MA0605.2_ATF3	3.6507800000000003e-07
JDP2_HUMAN.H11MO.0.D	1.43405e-06
MA1475.1_CREB3L4(var.2)	1.43405e-06
MA0656.1_JDP2(var.2)	1.43405e-06
MA1127.1_FOSB::JUN	2.4963299999999997e-06
MA1140.2_JUNB(var.2)	3.0848700000000003e-06	Not shown
MA1145.1_FOSL2::JUND(var.2)	4.33227e-06	Not shown
ATF2_HUMAN.H11MO.2.C	4.33227e-06	Not shown
MA1131.1_FOSL2::JUN(var.2)	5.13879e-06	Not shown
MA1139.1_FOSL2::JUNB(var.2)	8.07524e-06	Not shown

Motif 3/10

Motif ID	q-val	PWM
ATF4_HUMAN.H11MO.0.A	0.00140825
BATF_HUMAN.H11MO.1.A	0.00140825
MA0833.2_ATF4	0.00140825
CEBPG_HUMAN.H11MO.0.B	0.00140825
MA1636.1_CEBPG(var.2)	0.00154207
DDIT3_HUMAN.H11MO.0.D	0.00198558	Not shown
MA1143.1_FOSL1::JUND(var.2)	0.002206	Not shown
MA0837.1_CEBPE	0.00649494	Not shown
MA0466.2_CEBPB	0.00649494	Not shown
CEBPD_HUMAN.H11MO.0.C	0.00880957	Not shown

Motif 4/10

No TOMTOM matches passing threshold

Motif 5/10

Motif ID	q-val	PWM
MA0052.4_MEF2A	0.08358360000000001
MEF2B_HUMAN.H11MO.0.A	0.213015
MA0773.1_MEF2D	0.213015
HXD13_HUMAN.H11MO.0.D	0.213015
MA0679.2_ONECUT1	0.213015
MEF2D_HUMAN.H11MO.0.A	0.213015	Not shown
HMGA1_HUMAN.H11MO.0.D	0.22946799999999998	Not shown
PRDM6_HUMAN.H11MO.0.C	0.256704	Not shown
CPEB1_HUMAN.H11MO.0.D	0.256704	Not shown
MA0757.1_ONECUT3	0.256704	Not shown

Motif 6/10

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	0.000544849
SP1_HUMAN.H11MO.0.A	0.00106316
SP3_HUMAN.H11MO.0.B	0.00198292
THAP1_HUMAN.H11MO.0.C	0.00292203
AP2B_HUMAN.H11MO.0.B	0.00912454
MBD2_HUMAN.H11MO.0.B	0.00912454	Not shown
SP1_HUMAN.H11MO.1.A	0.00996732	Not shown
MA0146.2_Zfx	0.010996899999999999	Not shown
MXI1_HUMAN.H11MO.0.A	0.0159266	Not shown
KLF16_HUMAN.H11MO.0.D	0.0159266	Not shown

Motif 7/10

Motif ID	q-val	PWM
SP3_HUMAN.H11MO.0.B	0.00287321
SP2_HUMAN.H11MO.0.A	0.00287321
SP1_HUMAN.H11MO.0.A	0.013499500000000001
MA1615.1_Plagl1	0.014103000000000001
WT1_HUMAN.H11MO.0.C	0.031907599999999994
KLF16_HUMAN.H11MO.0.D	0.038005199999999996	Not shown
TBX15_HUMAN.H11MO.0.D	0.052672800000000006	Not shown
SP1_HUMAN.H11MO.1.A	0.06959589999999999	Not shown
EGR4_HUMAN.H11MO.0.D	0.06959589999999999	Not shown
ZFX_HUMAN.H11MO.1.A	0.06959589999999999	Not shown

Motif 8/10

Motif ID	q-val	PWM
CPEB1_HUMAN.H11MO.0.D	4.34245e-05
MA1125.1_ZNF384	0.037427300000000004
PRDM6_HUMAN.H11MO.0.C	0.037427300000000004
FOXL1_HUMAN.H11MO.0.D	0.038732800000000005
FOXG1_HUMAN.H11MO.0.D	0.0678408
MA0679.2_ONECUT1	0.14674700000000002	Not shown
ANDR_HUMAN.H11MO.0.A	0.14674700000000002	Not shown
FOXJ3_HUMAN.H11MO.0.A	0.14674700000000002	Not shown
ONEC2_HUMAN.H11MO.0.D	0.173016	Not shown
FUBP1_HUMAN.H11MO.0.D	0.275468	Not shown

Motif 9/10

Motif ID	q-val	PWM
CPEB1_HUMAN.H11MO.0.D	5.61576e-05
PRDM6_HUMAN.H11MO.0.C	0.0351791
MA1125.1_ZNF384	0.0351791
FOXL1_HUMAN.H11MO.0.D	0.0455093
FOXG1_HUMAN.H11MO.0.D	0.0953563
HXC10_HUMAN.H11MO.0.D	0.119854	Not shown
ANDR_HUMAN.H11MO.0.A	0.136481	Not shown
MA0679.2_ONECUT1	0.136481	Not shown
FOXJ3_HUMAN.H11MO.0.A	0.136481	Not shown
ZFP28_HUMAN.H11MO.0.C	0.164089	Not shown

Motif 10/10

Motif ID	q-val	PWM
CPEB1_HUMAN.H11MO.0.D	0.00015962
PRDM6_HUMAN.H11MO.0.C	0.0249903
FOXL1_HUMAN.H11MO.0.D	0.0249903
MA1125.1_ZNF384	0.0249903
FOXG1_HUMAN.H11MO.0.D	0.07045660000000001
ANDR_HUMAN.H11MO.0.A	0.0785665	Not shown
FUBP1_HUMAN.H11MO.0.D	0.117013	Not shown
FOXJ3_HUMAN.H11MO.0.A	0.117013	Not shown
MA0679.2_ONECUT1	0.11753	Not shown
ONEC2_HUMAN.H11MO.0.D	0.138249	Not shown

Metacluster 2/2

Motif 1/17

Motif ID	q-val	PWM
ZN680_HUMAN.H11MO.0.C	0.42188400000000004
SMCA1_HUMAN.H11MO.0.C	0.42188400000000004

Motif 2/17

No TOMTOM matches passing threshold

Motif 3/17

Motif ID	q-val	PWM
P53_HUMAN.H11MO.1.A	0.49984399999999996

Motif 4/17

Motif ID	q-val	PWM
SP3_HUMAN.H11MO.0.B	0.00032676900000000003
SP2_HUMAN.H11MO.0.A	0.00176721
KLF3_HUMAN.H11MO.0.B	0.00176721
SP1_HUMAN.H11MO.0.A	0.00176721
VEZF1_HUMAN.H11MO.0.C	0.00478623
WT1_HUMAN.H11MO.0.C	0.0112058	Not shown
MA0528.2_ZNF263	0.0112058	Not shown
ZN467_HUMAN.H11MO.0.C	0.0126795	Not shown
ZN770_HUMAN.H11MO.0.C	0.0126795	Not shown
SP4_HUMAN.H11MO.1.A	0.017725599999999998	Not shown

Motif 5/17

No TOMTOM matches passing threshold

Motif 6/17

No TOMTOM matches passing threshold

Motif 7/17

No TOMTOM matches passing threshold

Motif 8/17

Motif ID	q-val	PWM
FLI1_HUMAN.H11MO.0.A	0.167302
ETV4_HUMAN.H11MO.0.B	0.361757
VEZF1_HUMAN.H11MO.1.C	0.361757
E2F6_HUMAN.H11MO.0.A	0.361757
MA0528.2_ZNF263	0.361757
FLI1_HUMAN.H11MO.1.A	0.361757	Not shown
ETV7_HUMAN.H11MO.0.D	0.361757	Not shown
MA1522.1_MAZ	0.361757	Not shown
E2F1_HUMAN.H11MO.0.A	0.361757	Not shown
E2F7_HUMAN.H11MO.0.B	0.361757	Not shown

Motif 9/17

Motif ID	q-val	PWM
MA0815.1_TFAP2C(var.3)	0.19513699999999998
MA0872.1_TFAP2A(var.3)	0.19513699999999998
MA1569.1_TFAP2E	0.19513699999999998
AP2B_HUMAN.H11MO.0.B	0.19513699999999998
MA0154.4_EBF1	0.20415899999999998
NR1H4_HUMAN.H11MO.0.B	0.20415899999999998	Not shown
MA1615.1_Plagl1	0.20415899999999998	Not shown
AP2D_HUMAN.H11MO.0.D	0.20415899999999998	Not shown
MA0813.1_TFAP2B(var.3)	0.23364400000000002	Not shown
COE1_HUMAN.H11MO.0.A	0.23364400000000002	Not shown

Motif 10/17

No TOMTOM matches passing threshold

Motif 11/17

Motif ID	q-val	PWM
MA0493.1_Klf1	0.157307
MA1107.2_KLF9	0.207589
EGR3_HUMAN.H11MO.0.D	0.42829799999999996

Motif 12/17

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	0.00297052
SP1_HUMAN.H11MO.0.A	0.00297052
SP3_HUMAN.H11MO.0.B	0.00297052
CTCFL_HUMAN.H11MO.0.A	0.034427
SP1_HUMAN.H11MO.1.A	0.034427
WT1_HUMAN.H11MO.0.C	0.034427	Not shown
USF2_HUMAN.H11MO.0.A	0.034427	Not shown
KLF3_HUMAN.H11MO.0.B	0.0434062	Not shown
THAP1_HUMAN.H11MO.0.C	0.046915899999999996	Not shown
MA0146.2_Zfx	0.0588837	Not shown

Motif 13/17

Motif ID	q-val	PWM
FOSL1_HUMAN.H11MO.0.A	0.00382127
JUND_HUMAN.H11MO.0.A	0.00382127
MA0478.1_FOSL2	0.00382127
JUN_HUMAN.H11MO.0.A	0.00382127
MA0099.3_FOS::JUN	0.00382127
FOSL2_HUMAN.H11MO.0.A	0.00382127	Not shown
MA1142.1_FOSL1::JUND	0.00382127	Not shown
JUNB_HUMAN.H11MO.0.A	0.00382127	Not shown
MA1138.1_FOSL2::JUNB	0.00382127	Not shown
MA1144.1_FOSL2::JUND	0.00382127	Not shown

Motif 14/17

No TOMTOM matches passing threshold

Motif 15/17

No TOMTOM matches passing threshold

Motif 16/17

No TOMTOM matches passing threshold

Motif 17/17

No TOMTOM matches passing threshold