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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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FLY_using_DeepFlyBrain: |
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This notebook shows how to load and use the provided model. |
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It shows how to calculate and plot: |
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Predictions |
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Deexplainer contribution scores |
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In silico saturation mutagenesis |
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DeepFlyBrain is provided in ./models/deepflybrain |
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The model can be downloaded from Zenodo, which is used by Kipoi database: |
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DeepFlyBrain: https://zenodo.org/records/5153337 |
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FLY_KC_EFS: |
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This notebook shows how to design synthetic sequences by using in silico evolution for Kenyon Cells. |
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It uses the selected enhancers from the MM_Cbust_Homer_Motif 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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Intermediate files are saved to ./data/deepflybrain folder |
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Figures are saved to ./figures/evolution_from_scratch |
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FLY_PNG_EFS: |
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This notebook shows how to design synthetic sequences by using in silico evolution for Glial Cells. |
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It uses the selected enhancers from the FLY_KC_EFS notebook |
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It consists of: |
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Performing in silico evolution experiments. |
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Plotting the findings. |
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Printing generated DNA sequences in nucleotide letters. |
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Intermediate files are saved to ./data/deepflybrain folder |
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Figures are saved to ./figures/evolution_from_scratch_PNG |
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FLC_KC_EFS_Steps_Rescue: |
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This notebook shows how to visualize mutational steps and to get sequences with additional mutations. |
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It uses the synthetic sequences file generated via FLY_KC_EFS 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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Figures are saved to ./figures/mutational_steps and ./figures/rescue folders |
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FLY_KC_EFS_Mutation_Combination: |
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This notebooks shows using alternative state space searches during in silico evolution |
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It uses the synthetic sequences file generated via FLY_KC_EFS notebook. |
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It consists of: |
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Choosing top 20 best mutations instead of only top 1 during in silico evolution |
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Investigating different evolution paths |
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Choosing 5 random mutations instead following the model's guidance |
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Intermediate files are saved to ./data/mutation_combination folder |
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Figures are saved to ./figures/mutation_combination |
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FLY_KC_EFS_Mutation_Combination_All3Mut: |
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This notebooks shows how to generate and score sequences with all possible 3 mutations |
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It uses the synthetic sequences file generated via FLY_KC_EFS notebook. |
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It consists of: |
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Generating sequences with all possible 3 mutations |
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Comparing prediction scores |
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Figures are saved to ./figures/mutation_combination |
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FLY_KC_Near_Enhancer_Seqs: |
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This notebook shows the in silico evolution of near-enhancer sequences. |
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Kenyon Cell accessibility bigwig file is provided in ./data/near_enhancer_seq |
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Chopped fly genome is provided in ./data/near_enhancer_seq |
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It consists of: |
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Calculating predictions on the 500bp chopped genomic sequences |
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Plotting prediction scores vs chromatin accessibility |
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Choosing sequences with low accessibility and high prediction score |
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Plotting ATAC-seq coverage on chosen regions |
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Performing additional in silico evolution mutations on the chosen regions |
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Applying mutations to selected position and substation to create repressor binding sites |
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Intermediate files are saved to ./data/near_enhancer_seq folder |
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Figures are saved to ./figures/near_enhancer_seq folder |
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FLY_KC_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 FLY_KC_EFS 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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Figures are saved to ./figures/repressors |
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FLY_KC_ATAC: |
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This notebook shows the experiments related to ATAC-seq on the brains of synthetic enhancer integrated fly lines. |
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Processed ATAC-seq data is in ./data/atac 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/atac folder |
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FLY_EFS_TFModisco: |
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This notebook shows the TFModiscco experiments. |
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It uses the synthetic sequences file generated via FLY_KC_EFS 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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FLY_Augmentation_Pruning: |
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This notebook shows the experiments related to dual-code enhancers |
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The cloned enhancers fasta file from Janssens et al is provided in ./data/augmentation_pruning |
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The notebook consists of: |
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Performing mutations on genomic enhancers to add a second code |
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Identifying genomic enhancers accessible in two or more cell lines |
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Performing mutations on genomic enhancers to remove the second code |
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Figures are saved to ./figures/augmentation_pruning folder |
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FLC_KC_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 and location preference experiments. |
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Identify enriched flankings at the motif implanted locations. |
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Cutting designed sequences. |
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Adding repressors sites by single mutations |
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Replacing the background sequence of an enhancer with 1 million random sequences |
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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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FLY_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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Visualising the results and contribution scores. |
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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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FLY_Cbust_Homer_Motif: |
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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 FLY_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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