fly

the_code/Fly/FLY_KC_EFS.ipynb

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+size 10747046

the_code/Fly/FLY_KC_EFS_Mutation_Combination_All3Mut.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "52b2bde6-b2de-45a4-838c-f26efc68efc7",
+   "metadata": {},
+   "source": [
+    "# This notebooks shows how to generate and score sequences with all possible 3 mutations\n",
+    "#### It uses the synthetic sequences file generated via FLY_KC_EFS notebook.\n",
+    "#### It consists of:\n",
+    "* Generating sequences with all possible 3 mutations\n",
+    "* Comparing prediction scores\n",
+    "#### Figures are saved to ./figures/mutation_combination"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84648f4f-ef03-44a5-8eaf-d1c7680b80df",
+   "metadata": {},
+   "source": [
+    "### General imports\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a3b52957-4ebe-4b5d-bd90-7b6064baa71e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys  \n",
+    "import os\n",
+    "import pickle\n",
+    "import utils\n",
+    "import numpy as np\n",
+    "import scipy\n",
+    "import tensorflow as tf\n",
+    "tf.disable_eager_execution()\n",
+    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "matplotlib.style.use(\"default\")\n",
+    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
+    "matplotlib.rcParams['ps.fonttype'] = 42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c81ec4b4-5c14-4a9f-8088-2f24e870c0d9",
+   "metadata": {},
+   "source": [
+    "### Loading the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5d1787be-f329-475d-911f-6d301279a8df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Loading model...')\n",
+    "import shap\n",
+    "tf.disable_eager_execution()\n",
+    "model_dict = {}\n",
+    "name = \"DeepFlyBrain\"\n",
+    "model_json_file = \"models/deepflybrain/model.json\"\n",
+    "model_hdf5_file = \"models/deepflybrain/model_epoch_83.hdf5\"\n",
+    "model_dict[name] = utils.load_model(model_json_file, model_hdf5_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62ec272e-205d-4f7e-b07d-c17b4b341788",
+   "metadata": {},
+   "source": [
+    "### Loading the generated sequences via in silico evolution¶"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "93b2fac3-9b87-48a0-a50a-4f74e82c4440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "f = open(\"data/deepflybrain/FLY_KC_EFS_6000_withmut.pkl\", \"rb\")\n",
+    "evolved_seq_6000_dict = pickle.load(f)\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75596bc9-afa3-485e-86c5-c667a659ae3a",
+   "metadata": {},
+   "source": [
+    "### Following code generates sequences with all possible 3 mutations starting from a single random sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24702a4b-13da-464e-ae92-24e5ac535c8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from itertools import combinations, combinations_with_replacement, product\n",
+    "location_combinations = list(combinations(list(range(500)), 3))\n",
+    "nucleotide_combinations = list(product(range(3), repeat = 3))\n",
+    "\n",
+    "nuc_to_onehot = {\"A\":[1, 0, 0, 0],\"C\":[0, 1, 0, 0],\"G\":[0, 0, 1, 0],\"T\":[0, 0, 0, 1]}\n",
+    "def create_possible_single_mutations(onehot):\n",
+    "    mutagenesis_X = {\"loc\":[],\"nuc_onehot\":[]}\n",
+    "    for mutloc,nt in enumerate(onehot):\n",
+    "        new_X = np.copy(onehot)\n",
+    "        if list(nt) == [1, 0, 0, 0]:\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 1, 0, 0], dtype='int8'))\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 0, 1, 0], dtype='int8'))\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 0, 0, 1], dtype='int8'))\n",
+    "        if list(nt) == [0, 1, 0, 0]:\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([1, 0, 0, 0], dtype='int8'))\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 0, 1, 0], dtype='int8'))\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 0, 0, 1], dtype='int8'))\n",
+    "        if list(nt) == [0, 0, 1, 0]:\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([1, 0, 0, 0], dtype='int8'))\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 1, 0, 0], dtype='int8'))\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 0, 0, 1], dtype='int8'))\n",
+    "        if list(nt) == [0, 0, 0, 1]:\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"loc\"].append(mutloc)\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([1, 0, 0, 0], dtype='int8'))\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 1, 0, 0], dtype='int8'))\n",
+    "            mutagenesis_X[\"nuc_onehot\"].append(np.array([0, 0, 1, 0], dtype='int8'))\n",
+    "            \n",
+    "    mutagenesis_X[\"loc\"] = np.array(mutagenesis_X[\"loc\"])\n",
+    "    mutagenesis_X[\"nuc_onehot\"] = np.array(mutagenesis_X[\"nuc_onehot\"])\n",
+    "    return mutagenesis_X \n",
+    "    \n",
+    "id_ = 35\n",
+    "start_x = np.copy(evolved_seq_6000_dict[\"X\"][id_])\n",
+    "all_possible_single_mutation_onehot = create_possible_single_mutations(start_x)['nuc_onehot']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0e96bf1-713e-4027-9397-6e0d40cde63c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pickle\n",
+    "\n",
+    "# for out_of_27 in range(27):\n",
+    "#     mutatated_seqs = np.zeros((20708500,500,4))\n",
+    "#     for nuc_ in [nucleotide_combinations[out_of_27]]:\n",
+    "#         for i,loc_ in enumerate(location_combinations):\n",
+    "#             new_X = np.copy(start_x)\n",
+    "#             for mut_index in range(3):\n",
+    "#                 new_X[loc_[mut_index],:] = all_possible_single_mutation_onehot[loc_[mut_index]*3 + nuc_[mut_index]]\n",
+    "#             mutatated_seqs[i] = new_X\n",
+    "#     pred_ = model_dict[\"DeepFlyBrain\"].predict(mutatated_seqs)\n",
+    "    \n",
+    "#     f = open(\"data/all_possible_3_mut/pred_\"+str(out_of_27+1)+\"_27.pkl\", \"wb\")\n",
+    "#     pickle.dump(pred_, f ,protocol=4)\n",
+    "#     f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3fb96e65-3b56-43ca-851c-eaa1c2b229b4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "preds = []\n",
+    "for i in range(27):\n",
+    "    f = open(\".data/all_possible_3_mut/pred_\"+str(i)+\"_27.pkl\", \"rb\")\n",
+    "    pred_ = pickle.load(f)\n",
+    "    preds.append(pred_)\n",
+    "    f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a53eaed-c3d9-4410-a061-f12d39180600",
+   "metadata": {},
+   "source": [
+    "### Number of sequences with higher prediction scores compared to sequence following the greedy search path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2082f178-1791-4379-9522-7d2b467e1fb9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(KC_pred>evolved_seq_6000_dict['mut_pred'][[35],3,34])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35b496a4-52cf-4e64-a9ab-0bae241e0a92",
+   "metadata": {},
+   "source": [
+    "### Plotting prediction score distribution of the generated sequences with all possible 3 mutations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "24dd1748-4f32-45aa-bcd7-500878224cb2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,5))\n",
+    "ax = fig.add_subplot(1,2,1)\n",
+    "\n",
+    "_ = plt.hist(KC_pred,bins=100,range=[0,0.06],color='gray')\n",
+    "plt.axvline(evolved_seq_6000_dict['mut_pred'][[35],3,34],label=\"Greedy search (3 mutations)\")\n",
+    "plt.axvline(evolved_seq_6000_dict['mut_pred'][[35],0,34],label=\"Random sequence\",color='black',linestyle='--')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(\"Prediction score dist.\")\n",
+    "plt.xlabel(\"Prediction score\")\n",
+    "\n",
+    "ax = fig.add_subplot(1,2,2)\n",
+    "\n",
+    "plt.hist(KC_pred,bins=100,range=[0.03,0.06],color='gray')\n",
+    "plt.axvline(evolved_seq_6000_dict['mut_pred'][[35],3,34],label=\"Greedy search\")\n",
+    "plt.title(\"Prediction score dist.\")\n",
+    "plt.xlabel(\"Prediction score\")\n",
+    "\n",
+    "plt.savefig(\"figures/mutation_combination/EFS4_3Steps_allpossiblemuts_pred_distribution.pdf\",transparent=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Deeplearning tf1.15",
+   "language": "python",
+   "name": "deeplearning_tf115"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": false,
+  "toc-showmarkdowntxt": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

the_code/Fly/FLY_PNG_EFS.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12a71d659e37932f5e84c6445a5d64d77fc539e4530d683d37d030de1d9806d6
+size 14744364

the_code/Fly/README.txt

@@ -0,0 +1,175 @@
+Dependencies:
+    DL Python environment to use DeepMEL, DeepMEL2, and DeepFlyBrain:
+    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
+
+    DL Python environment to train GAN models:
+    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
+
+
+Deepexplainer script update:
+    In order to calculate nucleotide contribution scores for only the selected class,
+    conda_env/lib/python3.7/site-packages/shap/explainers/_deep/deep_tf.py is updated by inserting the following codes at line 277:
+        elif output_rank_order.isnumeric():
+            model_output_ranks = np.argsort(-model_output_values)
+            model_output_ranks[0] = int(output_rank_order)
+
+
+FLY_using_DeepFlyBrain:
+	This notebook shows how to load and use the provided model. 
+	It shows how to calculate and plot:
+	 	Predictions
+	 	Deexplainer contribution scores
+	 	In silico saturation mutagenesis
+	DeepFlyBrain is provided in ./models/deepflybrain
+	The model can be downloaded from Zenodo, which is used by Kipoi database:
+		DeepFlyBrain: https://zenodo.org/records/5153337
+
+	
+FLY_KC_EFS:
+	This notebook shows how to design synthetic sequences by using in silico evolution for Kenyon Cells.
+	It uses the selected enhancers from the MM_Cbust_Homer_Motif notebook
+	It consists of:
+		Generating GC-adjusted random sequences:
+		Performing in silico evolution and random drift experiments.
+		Plotting the findings.
+		Printing generated DNA sequences in nucleotide letters.
+	Intermediate files are saved to ./data/deepflybrain folder
+	Figures are saved to ./figures/evolution_from_scratch
+	
+
+FLY_PNG_EFS:
+	This notebook shows how to design synthetic sequences by using in silico evolution for Glial Cells.
+	It uses the selected enhancers from the FLY_KC_EFS notebook
+	It consists of:
+		Performing in silico evolution experiments.
+		Plotting the findings.
+		Printing generated DNA sequences in nucleotide letters.
+	Intermediate files are saved to ./data/deepflybrain folder
+	Figures are saved to ./figures/evolution_from_scratch_PNG
+	
+
+FLC_KC_EFS_Steps_Rescue:
+	This notebook shows how to visualize mutational steps and to get sequences with additional mutations.
+	It uses the synthetic sequences file generated via FLY_KC_EFS notebook.
+	It consists of:
+		Printing DNA sequences in nucleotide letters for different mutational steps.
+		Applying mutations to selected position and substation.
+		Plotting the findings.
+	Figures are saved to ./figures/mutational_steps and ./figures/rescue folders
+	
+	
+FLY_KC_EFS_Mutation_Combination:
+	This notebooks shows using alternative state space searches during in silico evolution
+	It uses the synthetic sequences file generated via FLY_KC_EFS notebook.
+	It consists of:
+		Choosing top 20 best mutations instead of only top 1 during in silico evolution
+		Investigating different evolution paths
+		Choosing 5 random mutations instead following the model's guidance
+	Intermediate files are saved to ./data/mutation_combination folder
+	Figures are saved to ./figures/mutation_combination
+	
+	
+FLY_KC_EFS_Mutation_Combination_All3Mut:
+	This notebooks shows how to generate and score sequences with all possible 3 mutations
+	It uses the synthetic sequences file generated via FLY_KC_EFS notebook.
+	It consists of:
+		Generating sequences with all possible 3 mutations
+		Comparing prediction scores
+	Figures are saved to ./figures/mutation_combination
+	
+	
+FLY_KC_Near_Enhancer_Seqs:
+	This notebook shows the in silico evolution of near-enhancer sequences.
+	Kenyon Cell accessibility bigwig file is provided in ./data/near_enhancer_seq 
+	Chopped fly genome is provided in ./data/near_enhancer_seq 
+	It consists of:
+		Calculating predictions on the 500bp chopped genomic sequences
+		Plotting prediction scores vs chromatin accessibility
+		Choosing sequences with low accessibility and high prediction score
+		Plotting ATAC-seq coverage on chosen regions
+		Performing additional in silico evolution mutations on the chosen regions
+		Applying mutations to selected position and substation to create repressor binding sites
+	Intermediate files are saved to ./data/near_enhancer_seq folder
+	Figures are saved to ./figures/near_enhancer_seq folder
+	
+	
+FLY_KC_Repressors:
+	This notebook shows how to perform mutations on generated sequences and visualize mutational steps.
+	It uses the synthetic sequences file generated via FLY_KC_EFS notebook.
+	It consists of:
+		Printing DNA sequences in nucleotide letters for different mutational steps.
+		Applying mutations to selected position and substation.
+		Plotting the findings.
+	Figures are saved to ./figures/repressors
+	
+
+FLY_KC_ATAC:
+	This notebook shows the experiments related to ATAC-seq on the brains of synthetic enhancer integrated fly lines.
+	Processed ATAC-seq data is in ./data/atac folder.
+	It consist of:
+		Reading ATAC-seq files and calculating the coverage on the enhancers
+	Figures are saved to ./figures/atac folder
+	
+
+FLY_EFS_TFModisco:
+	This notebook shows the TFModiscco experiments.
+	It uses the synthetic sequences file generated via FLY_KC_EFS notebook. 
+	It consists of:
+		Calculating contribution scores on synthetic sequences.
+		Performing TFModisco on contribution scores.
+		Plotting identified patterns.
+		Saving trimmed patterns as txt file to be later used for motif analysis.
+	Result files are saved to ./data/tfmodisco folder
+	Figures are saved to ./figures/tfmodisco folder
+
+
+FLY_Augmentation_Pruning:
+	This notebook shows the experiments related to dual-code enhancers
+	The cloned enhancers fasta file from Janssens et al is provided in ./data/augmentation_pruning
+	The notebook consists of:
+		Performing mutations on genomic enhancers to add a second code
+		Identifying genomic enhancers accessible in two or more cell lines
+		Performing mutations on genomic enhancers to remove the second code
+	Figures are saved to ./figures/augmentation_pruning folder
+		
+
+FLC_KC_Motif_Implanting:
+	This notebook shows how to design synthetic sequences by using motif implantation.
+	It consists of:
+		Performing motif implantation experiments.
+		Visualising motif distance and location preference experiments.
+		Identify enriched flankings at the motif implanted locations.
+		Cutting designed sequences.
+		Adding repressors sites by single mutations
+		Replacing the background sequence of an enhancer with 1 million random sequences		
+	Intermediate files are saved to ./data/motif_embedding folder
+	Figures are saved to ./figures/motif_embedding
+	
+	
+FLY_GAN:
+	This notebook shows how to load and analyse GAN generated sequences.
+	GAN generated sequences are provided in ./data/gan/generated_seqs folder.
+	Background sequences are provided in ./data/gan/background_seqs folder.
+	Genomic sequences are provided in ./data/gan folder
+	It consists of:
+		Reading GAN generated, genomic, and background sequences.
+		Scoring generated sequences with the DeepMEL model.
+		Visualising prediction scores on gan generated sequences at different training steps.
+		Comparing GC content of GAN generated and background sequences.
+		Visualising the results and contribution scores.
+	Intermediate files are saved to ./data/gan folder
+	Figures are saved to ./figures/gan folder
+	
+	
+FLY_Cbust_Homer_Motif:
+	This notebook shows ClusterBuster and Homer experiments.
+	It uses contribution scores and TFModisco scores generated in the FLY_EFS_TFModisco notebook.
+	The motif database file is provided in ./data/tomtom folder
+	It consists of:
+		Getting TFModisco patterns and saving as txt file to be later used by ClusterBuster.
+		Running Tomtom on TFModisco patterns.
+		Running ClusterBuster by using TFModisco pattern PWMs on the sequences generated by in silico evolution, motif implantation, and GAN.
+		Running Homer using Random and Evolved sequences as foreground and background sequences, and vice versa.
+	ClusterBuster results are in ./data/cbust folder.
+	Homer results are in ./data/homer folder.
+	Figures are saved to ./figures/cbust folder

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_0_pattern_4.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_0_pattern_5.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_0_pattern_6.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_0_pattern_7.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_0_pattern_8.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_0_pattern_9.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_0.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_1.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_10.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_2.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_3.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_4.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_5.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_6.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_7.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_8.cb

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the_code/Fly/data/cbust/EFS_M1_motifs/KCEFS_M1_metacluster_1_pattern_9.cb

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the_code/Fly/data/cbust/EFS_M1_results/EFS_cbust_mot_array_merged.pkl

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