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  1. the_code/Human/MM_Cbust_Homer_Motif.ipynb +0 -0
  2. the_code/Human/MM_ChromBPnet_Experiments.ipynb +0 -0
  3. the_code/Human/MM_EFS.ipynb +0 -0
  4. the_code/Human/MM_EFS_Steps_Repressors.ipynb +3 -0
  5. the_code/Human/MM_EFS_TFModisco.ipynb +0 -0
  6. the_code/Human/MM_Enformer_Experiments.ipynb +3 -0
  7. the_code/Human/MM_Enhance_Rescue.ipynb +0 -0
  8. the_code/Human/MM_GAN.ipynb +0 -0
  9. the_code/Human/MM_IRF4_Experiments.ipynb +0 -0
  10. the_code/Human/MM_Lenti_ATAC.ipynb +0 -0
  11. the_code/Human/MM_Motif_Implanting.ipynb +0 -0
  12. the_code/Human/MM_ZEB2_ChIP.ipynb +0 -0
  13. the_code/Human/MM_using_DeepMELs.ipynb +0 -0
  14. the_code/Human/README.txt +160 -0
  15. the_code/Human/data/IRF4.gtf +3 -0
  16. the_code/Human/data/IRF4_enhancer.bed +3 -0
  17. the_code/Human/data/IRF4_locus.bed +3 -0
  18. the_code/Human/data/cbust/BG_M4_results/BG_cbust_mot_array_merged.pkl +3 -0
  19. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_0.bed +3 -0
  20. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_0.motif.bed +3 -0
  21. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_0.motif.merged.bed +3 -0
  22. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_1.bed +3 -0
  23. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_1.motif.bed +3 -0
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  26. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_10.motif.bed +3 -0
  27. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_10.motif.merged.bed +3 -0
  28. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_11.bed +3 -0
  29. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_11.motif.bed +3 -0
  30. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_11.motif.merged.bed +3 -0
  31. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_12.bed +3 -0
  32. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_12.motif.bed +3 -0
  33. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_12.motif.merged.bed +3 -0
  34. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_2.bed +3 -0
  35. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_2.motif.bed +3 -0
  36. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_2.motif.merged.bed +3 -0
  37. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_3.bed +3 -0
  38. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_3.motif.bed +3 -0
  39. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_3.motif.merged.bed +3 -0
  40. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_4.bed +3 -0
  41. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_4.motif.bed +3 -0
  42. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_4.motif.merged.bed +3 -0
  43. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_5.bed +3 -0
  44. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_5.motif.bed +3 -0
  45. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_5.motif.merged.bed +3 -0
  46. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_6.bed +3 -0
  47. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_6.motif.bed +3 -0
  48. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_6.motif.merged.bed +3 -0
  49. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_7.bed +3 -0
  50. the_code/Human/data/cbust/BG_M4_results/seq_I0/bg_I0.metacluster_0_pattern_7.motif.bed +3 -0
the_code/Human/MM_Cbust_Homer_Motif.ipynb ADDED
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the_code/Human/MM_ChromBPnet_Experiments.ipynb ADDED
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the_code/Human/MM_EFS.ipynb ADDED
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the_code/Human/MM_EFS_TFModisco.ipynb ADDED
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the_code/Human/MM_Enhance_Rescue.ipynb ADDED
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the_code/Human/MM_GAN.ipynb ADDED
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the_code/Human/MM_IRF4_Experiments.ipynb ADDED
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the_code/Human/MM_Lenti_ATAC.ipynb ADDED
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the_code/Human/MM_Motif_Implanting.ipynb ADDED
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the_code/Human/MM_ZEB2_ChIP.ipynb ADDED
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the_code/Human/MM_using_DeepMELs.ipynb ADDED
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the_code/Human/README.txt ADDED
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1
+ Dependencies:
2
+ DL Python environment to use DeepMEL, DeepMEL2, and DeepFlyBrain:
3
+ python=3.7 tensorflow-gpu=1.15 numpy=1.19.5 matplotlib=3.1.1 shap=0.29.3 ipykernel=5.1.2 h5py=2.10.0 TF-MoDISco 0.5.5.4
4
+
5
+ DL Python environment to train GAN models:
6
+ python=3.6 tensorflow-gpu=1.14.0 keras-gpu=2.2.4 numpy=1.16.2 matplotlib=3.1.1 shap=0.29.3 ipykernel=5.1.2
7
+
8
+
9
+ Deepexplainer script update:
10
+ In order to calculate nucleotide contribution scores for only the selected class,
11
+ conda_env/lib/python3.7/site-packages/shap/explainers/_deep/deep_tf.py is updated by inserting the following codes at line 277:
12
+ elif output_rank_order.isnumeric():
13
+ model_output_ranks = np.argsort(-model_output_values)
14
+ model_output_ranks[0] = int(output_rank_order)
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+
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+
17
+ MM_using_DeepMELs:
18
+ This notebook shows how to load and use the provided models.
19
+ It shows how to calculate and plot:
20
+ Predictions
21
+ Deepexplainer contribution scores
22
+ In silico saturation mutagenesis
23
+ 3 models are provided: DeepMEL, DeepMEL2, and DeepMEL2 with GABPA extension.
24
+ These models can be downloaded from Zenodo, which are used by Kipoi database:
25
+ DeepMEL: https://zenodo.org/records/3592129
26
+ DeepMEL2: https://zenodo.org/records/4590308
27
+ DeepMEL_GABPA: https://zenodo.org/records/4590405
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+
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+
30
+ MM_EFS:
31
+ This notebook shows how to design synthetic sequences by using in silico evolution.
32
+ It uses the selected enhancers from the MM_Cbust_Homer notebook
33
+ It consists of:
34
+ Generating GC-adjusted random sequences:
35
+ Performing in silico evolution and random drift experiments.
36
+ Plotting the findings.
37
+ Printing generated DNA sequences in nucleotide letters.
38
+ Luciferase values are in ./data/luciferase folder
39
+ Intermediate files are saved to ./data/deepmel2 folder
40
+ Figures are saved to ./figures/evolution_from_scratch
41
+
42
+
43
+ MM_EFS_TFModisco:
44
+ This notebook shows the TFModiscco experiments.
45
+ It uses the synthetic sequences file generated via MM_using_DeepMELs notebook.
46
+ It consists of:
47
+ Calculating contribution scores on synthetic sequences.
48
+ Performing TFModisco on contribution scores.
49
+ Plotting identified patterns.
50
+ Saving trimmed patterns as txt file to be later used for motif analysis.
51
+ Result files are saved to ./data/tfmodisco folder
52
+ Figures are saved to ./figures/tfmodisco folder
53
+
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+
55
+ MM_EFS_Steps_Repressors:
56
+ This notebook shows how to perform mutations on generated sequences and visualize mutational steps.
57
+ It uses the synthetic sequences file generated via MM_using_DeepMELs notebook.
58
+ It consists of:
59
+ Printing DNA sequences in nucleotide letters for different mutational steps.
60
+ Applying mutations to selected position and substation.
61
+ Plotting the findings.
62
+ Luciferase values are in ./data/luciferase folder
63
+ Result files are saved to ./data/tfmodisco folder
64
+ Figures are saved to ./figures/mutational_steps and ./figures/repressor_addition folders
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+
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+
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+ MM_Enhance_Rescue:
68
+ This notebook shows the near-enhancer and enhancing active enhancer experiments.
69
+ Luciferase values are in ./data/enhance_rescue/luciferase folder
70
+ Figures are saved to ./figures/enhance_rescue folder
71
+
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+
73
+ MM_IRF4_Experiments:
74
+ This notebook shows the experiments performed on IRF4 enhancer.
75
+ It consists of:
76
+ Loading the IRF4 enhancer sequence with different motif modifications.
77
+ Loading saturation mutagenesis assay performed on IRF4 enhancer by Kircher et al.
78
+ Showing individual mutations generating repressor binding sites on IRF4 enhancer.
79
+ Plotting the findings.
80
+ In vitro saturation mutagenesis assay value file is in ./data/irf4/
81
+ Luciferase values are in ./data/luciferase folder
82
+ Figures are saved to ./figures/irf4 folder
83
+
84
+
85
+ MM_ZEB2_ChIP:
86
+ This notebook shows the experiments related to ZEB2 ChIP-seq on MM001 cell line.
87
+ Processed ZEB2 ChIP-seq (Antibody and input), ATAC-seq, and SOX10 ChIP-seq on MM001 files are in ./data/chip_seq
88
+ ZEB2 ChIP-seq summit file is in ./data/chip_seq
89
+ The notebook consists of:
90
+ Plotting ZEB2 vs SOX10 ChIP-seq values compared with accessibility.
91
+ Finding and plotting regions with high ZEB2 signal.
92
+ Plotting ZEB2 and SOX10 ChIP-seq values on irf4 locus
93
+ Figures are saved to ./figures/chip_seq folder
94
+
95
+
96
+ MM_Lenti_ATAC:
97
+ This notebook shows the experiments related to ATAC-seq on synthetic enhancer integrated cell lines.
98
+ Processed ATAC-seq data is in data/lenti_atac_chip folder.
99
+ It consist of:
100
+ Reading ATAC-seq files and calculating the coverage on the enhancers
101
+ Figures are saved to ./figures/lenti_atac_chip folder
102
+
103
+
104
+ MM_ChromBPnet_Experiments:
105
+ This notebooks shows scoring synthetic and genomic enhancer by using the ChromBPNet models trained on MM001 and MM047 cell lines.
106
+ It uses the synthetic sequences file generated via MM_using_DeepMELs notebook.
107
+ The model files are provided in ./data/chrombpnet.
108
+ Figures are saved to ./figures/chrombpnet folder.
109
+
110
+
111
+ MM_Enformer_Experiments:
112
+ This notebooks shows scoring synthetic and genomic enhancer by using the Enformer model.
113
+ Enformer model is loaded from "https://tfhub.dev/deepmind/enformer/1"
114
+ Enformer class annotation is in ./data/enformer folder.
115
+ It uses the synthetic sequences file generated via MM_using_DeepMELs notebook.
116
+ The intermediate prediction files are in ./data/enformer folder.
117
+ Figures are saved to ./figures/enformer folder.
118
+
119
+
120
+ MM_Motif_Implanting:
121
+ This notebook shows how to design synthetic sequences by using motif implantation.
122
+ It consists of:
123
+ Performing motif implantation experiments.
124
+ Visualising motif distance preference experiments.
125
+ Replacing motifs on synthetic sequences with weaker ones from IRF4 enhancer.
126
+ Cutting and shortening designed sequences.
127
+ Luciferase values are in ./data/motif_embedding folder
128
+ Intermediate files are saved to ./data/motif_embedding folder
129
+ Figures are saved to ./figures/motif_embedding
130
+
131
+
132
+ MM_GAN:
133
+ This notebook shows how to load and analyse GAN generated sequences.
134
+ GAN generated sequences are provided in ./data/gan/generated_seqs folder.
135
+ Background sequences are provided in ./data/gan/background_seqs folder.
136
+ Genomic sequences are provided in ./data/gan folder
137
+ It consists of:
138
+ Reading GAN generated, genomic, and background sequences.
139
+ Scoring generated sequences with the DeepMEL model.
140
+ Visualising prediction scores on gan generated sequences at different training steps.
141
+ Comparing GC content of GAN generated and background sequences.
142
+ Visializing the luciferase results and contribution score plots.
143
+ Luciferase values are in ./data/luciferase folder
144
+ Intermediate files are saved to ./data/gan folder
145
+ Figures are saved to ./figures/gan folder
146
+
147
+
148
+ MM_Cbust_Homer:
149
+ This notebook shows ClusterBuster and Homer experiments.
150
+ It uses contribution scores and TFModisco scores generated in the MM_EFS_TFModisco notebook.
151
+ The motif database file is provided in ./data/tomtom folder
152
+ It consists of:
153
+ Getting TFModisco patterns and saving as txt file to be later used by ClusterBuster.
154
+ Running Tomtom on TFModisco patterns.
155
+ Running ClusterBuster by using TFModisco pattern PWMs on the sequences generated by in silico evolution, motif implantation, and GAN.
156
+ Running Homer using Random and Evolved sequences as foreground and background sequences, and vice versa.
157
+ ClusterBuster results are in ./data/cbust folder.
158
+ Homer results are in ./data/homer folder.
159
+ Figures are saved to ./figures/cbust folder
160
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