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Dependencies: |
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DL Python environment to use DeepMEL, DeepMEL2, and DeepFlyBrain: |
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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 |
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DL Python environment to train GAN models: |
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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 |
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Deepexplainer script update: |
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In order to calculate nucleotide contribution scores for only the selected class, |
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conda_env/lib/python3.7/site-packages/shap/explainers/_deep/deep_tf.py is updated by inserting the following codes at line 277: |
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elif output_rank_order.isnumeric(): |
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model_output_ranks = np.argsort(-model_output_values) |
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model_output_ranks[0] = int(output_rank_order) |
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MM_using_DeepMELs: |
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This notebook shows how to load and use the provided models. |
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It shows how to calculate and plot: |
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Predictions |
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Deepexplainer contribution scores |
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In silico saturation mutagenesis |
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3 models are provided: DeepMEL, DeepMEL2, and DeepMEL2 with GABPA extension. |
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These models can be downloaded from Zenodo, which are used by Kipoi database: |
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DeepMEL: https://zenodo.org/records/3592129 |
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DeepMEL2: https://zenodo.org/records/4590308 |
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DeepMEL_GABPA: https://zenodo.org/records/4590405 |
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MM_EFS: |
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This notebook shows how to design synthetic sequences by using in silico evolution. |
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It uses the selected enhancers from the MM_Cbust_Homer notebook |
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It consists of: |
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Generating GC-adjusted random sequences: |
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Performing in silico evolution and random drift experiments. |
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Plotting the findings. |
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Printing generated DNA sequences in nucleotide letters. |
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Luciferase values are in ./data/luciferase folder |
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Intermediate files are saved to ./data/deepmel2 folder |
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Figures are saved to ./figures/evolution_from_scratch |
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MM_EFS_TFModisco: |
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This notebook shows the TFModiscco experiments. |
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It uses the synthetic sequences file generated via MM_using_DeepMELs notebook. |
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It consists of: |
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Calculating contribution scores on synthetic sequences. |
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Performing TFModisco on contribution scores. |
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Plotting identified patterns. |
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Saving trimmed patterns as txt file to be later used for motif analysis. |
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Result files are saved to ./data/tfmodisco folder |
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Figures are saved to ./figures/tfmodisco folder |
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MM_EFS_Steps_Repressors: |
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This notebook shows how to perform mutations on generated sequences and visualize mutational steps. |
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It uses the synthetic sequences file generated via MM_using_DeepMELs notebook. |
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It consists of: |
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Printing DNA sequences in nucleotide letters for different mutational steps. |
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Applying mutations to selected position and substation. |
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Plotting the findings. |
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Luciferase values are in ./data/luciferase folder |
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Result files are saved to ./data/tfmodisco folder |
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Figures are saved to ./figures/mutational_steps and ./figures/repressor_addition folders |
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MM_Enhance_Rescue: |
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This notebook shows the near-enhancer and enhancing active enhancer experiments. |
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Luciferase values are in ./data/enhance_rescue/luciferase folder |
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Figures are saved to ./figures/enhance_rescue folder |
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MM_IRF4_Experiments: |
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This notebook shows the experiments performed on IRF4 enhancer. |
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It consists of: |
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Loading the IRF4 enhancer sequence with different motif modifications. |
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Loading saturation mutagenesis assay performed on IRF4 enhancer by Kircher et al. |
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Showing individual mutations generating repressor binding sites on IRF4 enhancer. |
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Plotting the findings. |
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In vitro saturation mutagenesis assay value file is in ./data/irf4/ |
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Luciferase values are in ./data/luciferase folder |
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Figures are saved to ./figures/irf4 folder |
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MM_ZEB2_ChIP: |
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This notebook shows the experiments related to ZEB2 ChIP-seq on MM001 cell line. |
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Processed ZEB2 ChIP-seq (Antibody and input), ATAC-seq, and SOX10 ChIP-seq on MM001 files are in ./data/chip_seq |
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ZEB2 ChIP-seq summit file is in ./data/chip_seq |
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The notebook consists of: |
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Plotting ZEB2 vs SOX10 ChIP-seq values compared with accessibility. |
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Finding and plotting regions with high ZEB2 signal. |
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Plotting ZEB2 and SOX10 ChIP-seq values on irf4 locus |
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Figures are saved to ./figures/chip_seq folder |
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MM_Lenti_ATAC: |
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This notebook shows the experiments related to ATAC-seq on synthetic enhancer integrated cell lines. |
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Processed ATAC-seq data is in data/lenti_atac_chip folder. |
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It consist of: |
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Reading ATAC-seq files and calculating the coverage on the enhancers |
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Figures are saved to ./figures/lenti_atac_chip folder |
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MM_ChromBPnet_Experiments: |
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This notebooks shows scoring synthetic and genomic enhancer by using the ChromBPNet models trained on MM001 and MM047 cell lines. |
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It uses the synthetic sequences file generated via MM_using_DeepMELs notebook. |
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The model files are provided in ./data/chrombpnet. |
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Figures are saved to ./figures/chrombpnet folder. |
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MM_Enformer_Experiments: |
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This notebooks shows scoring synthetic and genomic enhancer by using the Enformer model. |
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Enformer model is loaded from "https://tfhub.dev/deepmind/enformer/1" |
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Enformer class annotation is in ./data/enformer folder. |
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It uses the synthetic sequences file generated via MM_using_DeepMELs notebook. |
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The intermediate prediction files are in ./data/enformer folder. |
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Figures are saved to ./figures/enformer folder. |
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MM_Motif_Implanting: |
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This notebook shows how to design synthetic sequences by using motif implantation. |
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It consists of: |
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Performing motif implantation experiments. |
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Visualising motif distance preference experiments. |
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Replacing motifs on synthetic sequences with weaker ones from IRF4 enhancer. |
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Cutting and shortening designed sequences. |
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Luciferase values are in ./data/motif_embedding folder |
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Intermediate files are saved to ./data/motif_embedding folder |
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Figures are saved to ./figures/motif_embedding |
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MM_GAN: |
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This notebook shows how to load and analyse GAN generated sequences. |
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GAN generated sequences are provided in ./data/gan/generated_seqs folder. |
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Background sequences are provided in ./data/gan/background_seqs folder. |
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Genomic sequences are provided in ./data/gan folder |
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It consists of: |
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Reading GAN generated, genomic, and background sequences. |
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Scoring generated sequences with the DeepMEL model. |
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Visualising prediction scores on gan generated sequences at different training steps. |
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Comparing GC content of GAN generated and background sequences. |
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Visializing the luciferase results and contribution score plots. |
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Luciferase values are in ./data/luciferase folder |
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Intermediate files are saved to ./data/gan folder |
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Figures are saved to ./figures/gan folder |
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MM_Cbust_Homer: |
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This notebook shows ClusterBuster and Homer experiments. |
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It uses contribution scores and TFModisco scores generated in the MM_EFS_TFModisco notebook. |
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The motif database file is provided in ./data/tomtom folder |
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It consists of: |
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Getting TFModisco patterns and saving as txt file to be later used by ClusterBuster. |
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Running Tomtom on TFModisco patterns. |
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Running ClusterBuster by using TFModisco pattern PWMs on the sequences generated by in silico evolution, motif implantation, and GAN. |
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Running Homer using Random and Evolved sequences as foreground and background sequences, and vice versa. |
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ClusterBuster results are in ./data/cbust folder. |
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Homer results are in ./data/homer folder. |
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Figures are saved to ./figures/cbust folder |
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