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_fold9/CEBPB_multitask_profile_fold9_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold9/CEBPB_multitask_profile_fold9_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_fold9/CEBPB_multitask_profile_fold9_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 [05:06<00:00,  1.12s/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

9908 seqlets

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

Pattern 2/14

757 seqlets

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

Pattern 3/14

663 seqlets

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

Pattern 4/14

287 seqlets

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

Pattern 5/14

267 seqlets

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

Pattern 6/14

240 seqlets

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

Pattern 7/14

207 seqlets

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

Pattern 8/14

143 seqlets

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

Pattern 9/14

117 seqlets

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

Pattern 10/14

89 seqlets

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

Pattern 11/14

80 seqlets

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

Pattern 12/14

62 seqlets

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

Pattern 13/14

36 seqlets

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

Pattern 14/14

32 seqlets

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

Metacluster 2/2

Pattern 1/12

384 seqlets

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

Pattern 2/12

340 seqlets

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

Pattern 3/12

203 seqlets

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

Pattern 4/12

183 seqlets

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

Pattern 5/12

169 seqlets

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

Pattern 6/12

164 seqlets

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

Pattern 7/12

158 seqlets

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

Pattern 8/12

150 seqlets

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

Pattern 9/12

140 seqlets

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

Pattern 10/12

125 seqlets

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

Pattern 11/12

81 seqlets

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

Pattern 12/12

66 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
19908
2757
3663
4287
5267
6240
7207
8143
9117
1089
1180
1262
1336
1432

Metacluster 2/2

#SeqletsForwardReverse
1384
2340
3203
4183
5169
6164
7158
8150
9140
10125
1181
1266

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.A3.6004400000000006e-10
CEBPD_HUMAN.H11MO.0.C2.3097800000000003e-09
CEBPA_HUMAN.H11MO.0.A4.97696e-07
MA0836.2_CEBPD8.07747e-06
MA0102.4_CEBPA0.000122532
MA0837.1_CEBPE0.000586009Not shown
MA0466.2_CEBPB0.000586529Not shown
MA0838.1_CEBPG0.000907299Not shown
MA0025.2_NFIL30.00158398Not shown
DBP_HUMAN.H11MO.0.B0.00302359Not shown

Motif 2/14

Motif IDq-valPWM
FOSL1_HUMAN.H11MO.0.A1.7025299999999998e-06
FOSB_HUMAN.H11MO.0.A1.7025299999999998e-06
JUND_HUMAN.H11MO.0.A2.93155e-06
FOS_HUMAN.H11MO.0.A2.93155e-06
JUN_HUMAN.H11MO.0.A7.03572e-06
FOSL2_HUMAN.H11MO.0.A7.817460000000002e-06Not shown
JUNB_HUMAN.H11MO.0.A1.28451e-05Not shown
MA0478.1_FOSL21.6903e-05Not shown
MA1130.1_FOSL2::JUN3.89622e-05Not shown
MA0099.3_FOS::JUN3.89622e-05Not shown

Motif 3/14

Motif IDq-valPWM
MA0139.1_CTCF4.1683699999999993e-16
CTCF_HUMAN.H11MO.0.A2.35885e-14
CTCFL_HUMAN.H11MO.0.A7.52321e-08
MA1102.2_CTCFL4.14974e-05
MA1568.1_TCF21(var.2)0.112042
MA1638.1_HAND20.141816Not shown
ZIC3_HUMAN.H11MO.0.B0.19634100000000002Not shown
SNAI1_HUMAN.H11MO.0.C0.196876Not shown
ZIC2_HUMAN.H11MO.0.D0.290742Not shown
MA0155.1_INSM10.302689Not shown

Motif 4/14

Motif IDq-valPWM
SP1_HUMAN.H11MO.0.A0.00946131
SP2_HUMAN.H11MO.1.B0.012702600000000001
ZN467_HUMAN.H11MO.0.C0.012702600000000001
MA1513.1_KLF150.012702600000000001
ZN341_HUMAN.H11MO.0.C0.012702600000000001
E2F1_HUMAN.H11MO.0.A0.012702600000000001Not shown
TBX15_HUMAN.H11MO.0.D0.012702600000000001Not shown
KLF15_HUMAN.H11MO.0.A0.012702600000000001Not shown
SP3_HUMAN.H11MO.0.B0.012702600000000001Not shown
SP2_HUMAN.H11MO.0.A0.012702600000000001Not shown

Motif 5/14

Motif IDq-valPWM
MA1109.1_NEUROD10.356817
TYY1_HUMAN.H11MO.0.A0.356817
MA0748.2_YY20.356817
ZBTB4_HUMAN.H11MO.0.D0.372265
CEBPE_HUMAN.H11MO.0.A0.372265
MA1638.1_HAND20.382799Not shown
ATF7_HUMAN.H11MO.0.D0.382799Not shown
MA0638.1_CREB30.382799Not shown
MA1466.1_ATF60.382799Not shown
MA0656.1_JDP2(var.2)0.382799Not shown

Motif 6/14

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A1.57385e-06
FOXA1_HUMAN.H11MO.0.A6.20734e-06
FOXF2_HUMAN.H11MO.0.D9.76523e-06
FOXA3_HUMAN.H11MO.0.B1.4647799999999999e-05
MA0846.1_FOXC21.8788e-05
FOXM1_HUMAN.H11MO.0.A3.12275e-05Not shown
FOXD3_HUMAN.H11MO.0.D3.82363e-05Not shown
MA1607.1_Foxl29.2742e-05Not shown
MA0847.2_FOXD29.2742e-05Not shown
MA0032.2_FOXC10.000117925Not shown

Motif 7/14

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B8.871e-09
HNF4A_HUMAN.H11MO.0.A2.5194700000000002e-08
MA1550.1_PPARD0.000621635
MA1494.1_HNF4A(var.2)0.000621635
MA0677.1_Nr2f60.00139299
MA0115.1_NR1H2::RXRA0.00139299Not shown
MA0856.1_RXRG0.00165966Not shown
NR1H2_HUMAN.H11MO.0.D0.00168251Not shown
MA0512.2_Rxra0.00168485Not shown
MA0504.1_NR2C20.00168485Not shown

Motif 8/14

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D1.86209e-05
MA1125.1_ZNF3840.00733009
FOXG1_HUMAN.H11MO.0.D0.00733009
LMX1A_HUMAN.H11MO.0.D0.00733009
FOXJ3_HUMAN.H11MO.0.A0.0124702
HXC10_HUMAN.H11MO.0.D0.0129392Not shown
FOXL1_HUMAN.H11MO.0.D0.0129392Not shown
PRDM6_HUMAN.H11MO.0.C0.0328545Not shown
ANDR_HUMAN.H11MO.0.A0.0424909Not shown
FOXJ3_HUMAN.H11MO.1.B0.0424909Not shown

Motif 9/14

Motif IDq-valPWM
CEBPA_HUMAN.H11MO.0.A0.288258
MA1643.1_NFIB0.288258
CEBPB_HUMAN.H11MO.0.A0.288258
MA0102.4_CEBPA0.4644
CEBPD_HUMAN.H11MO.0.C0.4644

Motif 10/14

No TOMTOM matches passing threshold

Motif 11/14

Motif IDq-valPWM
MA1596.1_ZNF4603.19267e-05
MA1587.1_ZNF1350.00010358
ZN770_HUMAN.H11MO.1.C0.00514213
ZN770_HUMAN.H11MO.0.C0.0213623
THA_HUMAN.H11MO.0.C0.181108
ESR1_HUMAN.H11MO.0.A0.321707Not shown
MA0258.2_ESR20.478468Not shown
RARB_HUMAN.H11MO.0.D0.478468Not shown
MA0505.1_Nr5a20.478468Not shown
ZFX_HUMAN.H11MO.1.A0.478468Not shown

Motif 12/14

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A0.0025429000000000003
FOXA1_HUMAN.H11MO.0.A0.00422606
FOXD3_HUMAN.H11MO.0.D0.00471713
FOXC1_HUMAN.H11MO.0.C0.00496317
MA0846.1_FOXC20.00496317
MA0847.2_FOXD20.00496317Not shown
FOXA3_HUMAN.H11MO.0.B0.00496317Not shown
FOXF2_HUMAN.H11MO.0.D0.00496317Not shown
FOXD1_HUMAN.H11MO.0.D0.0114875Not shown
FOXM1_HUMAN.H11MO.0.A0.011993299999999998Not shown

Motif 13/14

Motif IDq-valPWM
CEBPE_HUMAN.H11MO.0.A0.00884834
CEBPA_HUMAN.H11MO.0.A0.012835599999999999
CEBPB_HUMAN.H11MO.0.A0.046154
MA0102.4_CEBPA0.0837262
MA0836.2_CEBPD0.0837262
CEBPD_HUMAN.H11MO.0.C0.14915499999999998Not shown
MA0025.2_NFIL30.259833Not shown
DBP_HUMAN.H11MO.0.B0.26661999999999997Not shown
MA0466.2_CEBPB0.26661999999999997Not shown
MA0837.1_CEBPE0.26661999999999997Not shown

Motif 14/14

Motif IDq-valPWM
ISL1_HUMAN.H11MO.0.A0.243592
MA0592.3_ESRRA0.243592
ESR1_HUMAN.H11MO.0.A0.347975
MA0258.2_ESR20.408395
MA1540.1_NR5A10.408395

Metacluster 2/2

Motif 1/12

No TOMTOM matches passing threshold

Motif 2/12

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A2.07425e-05
CEBPD_HUMAN.H11MO.0.C3.1337699999999996e-05
CEBPA_HUMAN.H11MO.0.A0.00175149
MA0043.3_HLF0.00175149
MA1636.1_CEBPG(var.2)0.00175149
MA0025.2_NFIL30.00176542Not shown
MA0466.2_CEBPB0.00189973Not shown
MA0837.1_CEBPE0.00203265Not shown
MA0833.2_ATF40.00236234Not shown
NFIL3_HUMAN.H11MO.0.D0.00236234Not shown

Motif 3/12

No TOMTOM matches passing threshold

Motif 4/12

Motif IDq-valPWM
GFI1_HUMAN.H11MO.0.C0.18024500000000002

Motif 5/12

No TOMTOM matches passing threshold

Motif 6/12

No TOMTOM matches passing threshold

Motif 7/12

No TOMTOM matches passing threshold

Motif 8/12

No TOMTOM matches passing threshold

Motif 9/12

No TOMTOM matches passing threshold

Motif 10/12

No TOMTOM matches passing threshold

Motif 11/12

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C7.384980000000001e-06
MA0466.2_CEBPB7.384980000000001e-06
MA0837.1_CEBPE7.384980000000001e-06
CEBPB_HUMAN.H11MO.0.A9.61913e-06
MA0838.1_CEBPG1.88697e-05
CEBPA_HUMAN.H11MO.0.A0.000267298Not shown
MA0836.2_CEBPD0.0008354360000000001Not shown
CEBPE_HUMAN.H11MO.0.A0.0022207Not shown
MA0102.4_CEBPA0.00223164Not shown
HLF_HUMAN.H11MO.0.C0.00343165Not shown

Motif 12/12

No TOMTOM matches passing threshold