test_repo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7cf70dc1-a1d3-40c2-8e99-5bbcf43564f4",
   "metadata": {},
   "source": [
    "# Multi Layer Perceptron (MNIST) Pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "13810de5-9135-4ad8-896d-169a5c2e8743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xlrd in /home/woreom/anaconda3/envs/dnabert/lib/python3.6/site-packages (1.2.0)\n",
      "Collecting xlrd\n",
      "  Using cached xlrd-2.0.1-py2.py3-none-any.whl (96 kB)\n",
      "Installing collected packages: xlrd\n",
      "  Attempting uninstall: xlrd\n",
      "    Found existing installation: xlrd 1.2.0\n",
      "    Uninstalling xlrd-1.2.0:\n",
      "      Successfully uninstalled xlrd-1.2.0\n",
      "Successfully installed xlrd-2.0.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3fb18bff-1afc-42fb-a68d-5538b9a27f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from torchvision import datasets\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "\n",
    "from transformers import (\n",
    "    WEIGHTS_NAME,\n",
    "    AdamW,\n",
    "    AlbertConfig,\n",
    "    AlbertForSequenceClassification,\n",
    "    AlbertTokenizer,\n",
    "    BertConfig,\n",
    "    BertForSequenceClassification,\n",
    "    BertForLongSequenceClassification,\n",
    "    BertForLongSequenceClassificationCat,\n",
    "    BertTokenizer,\n",
    "    DNATokenizer,\n",
    "    DistilBertConfig,\n",
    "    DistilBertForSequenceClassification,\n",
    "    DistilBertTokenizer,\n",
    "    FlaubertConfig,\n",
    "    FlaubertForSequenceClassification,\n",
    "    FlaubertTokenizer,\n",
    "    RobertaConfig,\n",
    "    RobertaForSequenceClassification,\n",
    "    RobertaTokenizer,\n",
    "    XLMConfig,\n",
    "    XLMForSequenceClassification,\n",
    "    XLMRobertaConfig,\n",
    "    XLMRobertaForSequenceClassification,\n",
    "    XLMRobertaTokenizer,\n",
    "    XLMTokenizer,\n",
    "    XLNetConfig,\n",
    "    XLNetForSequenceClassification,\n",
    "    XLNetTokenizer,\n",
    "    get_linear_schedule_with_warmup,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "320c055b-9c5f-45cd-a415-13f05eddfe84",
   "metadata": {},
   "outputs": [],
   "source": [
    "config_class, model_class, tokenizer_class=(BertConfig, BertForSequenceClassification, DNATokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5b45bcd-358c-4e24-b529-86ba416fae02",
   "metadata": {},
   "outputs": [],
   "source": [
    " model = model_class.from_pretrained(\"/mnt/d/M3/Projects/BCB/DNABERT/examples/OUTPUT/pre/3mer/checkpoint-17000\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7ecb30d-7d25-40b8-85c8-41452fac9f6f",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-a40327b55ea6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"input_ids\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"ACATCAGGTTACCTCTACCAAGG\"\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m\"attention_mask\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/dnabert/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    530\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/d/M3/Projects/BCB/DNABERT/src/transformers/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels)\u001b[0m\n\u001b[1;32m   1185\u001b[0m             \u001b[0mposition_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mposition_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m             \u001b[0mhead_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhead_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1187\u001b[0;31m             \u001b[0minputs_embeds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs_embeds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1188\u001b[0m         )\n\u001b[1;32m   1189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dnabert/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    530\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/d/M3/Projects/BCB/DNABERT/src/transformers/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask)\u001b[0m\n\u001b[1;32m    700\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"You cannot specify both input_ids and inputs_embeds at the same time\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0minput_ids\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m             \u001b[0minput_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0minputs_embeds\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m             \u001b[0minput_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs_embeds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'size'"
     ]
    }
   ],
   "source": [
    "model(**{\"input_ids\": \"ACATCAGGTTACCTCTACCAAGG\" , \"attention_mask\":3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0050ef8f-701a-4862-8f35-c8ebfc24be9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(69, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fbdb48f3-861e-48e0-b0ff-d83f59b91cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACATCA GGTTAC CTCTAC CAAGG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CTGATG CCAGCT AGTGGG CGAGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CTGTTT CCCATC CTTCCG GGTGG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AATGTA TGCACA GGGAAC AGAGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCAGAC TCACCC GCTTGC CCAGG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16744</th>\n",
       "      <td>CAACGC CCTGCT GCGGCG GCTGG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16745</th>\n",
       "      <td>CTAAGA AATCCT CTATCT TCAGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16746</th>\n",
       "      <td>TGATCC GCCAGC GCCATA TCAGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16747</th>\n",
       "      <td>ATCCGA GGTGGT ACCTGA TATGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16748</th>\n",
       "      <td>TCTCTC TGCGCT CTCCCT ACGGG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16749 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                0  1\n",
       "0      ACATCA GGTTAC CTCTAC CAAGG  1\n",
       "1      CTGATG CCAGCT AGTGGG CGAGG  0\n",
       "2      CTGTTT CCCATC CTTCCG GGTGG  1\n",
       "3      AATGTA TGCACA GGGAAC AGAGG  0\n",
       "4      CCAGAC TCACCC GCTTGC CCAGG  1\n",
       "...                           ... ..\n",
       "16744  CAACGC CCTGCT GCGGCG GCTGG  1\n",
       "16745  CTAAGA AATCCT CTATCT TCAGG  0\n",
       "16746  TGATCC GCCAGC GCCATA TCAGG  0\n",
       "16747  ATCCGA GGTGGT ACCTGA TATGG  0\n",
       "16748  TCTCTC TGCGCT CTCCCT ACGGG  0\n",
       "\n",
       "[16749 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of subprocesses to use for data loading\n",
    "num_workers = 0\n",
    "# how many samples per batch to load\n",
    "batch_size = 32\n",
    "# percentage of training set to use as validation\n",
    "valid_size = 0.2\n",
    "# convert data to torch.FloatTensor\n",
    "transform = transforms.ToTensor()\n",
    "# choose the training and testing datasets\n",
    "train_data_path = \"/mnt/d/M3/Projects/BCB/DNABERT/examples/sample_data/ft/prom-core/3/train.tsv\"\n",
    "test_data_path = \"/mnt/d/M3/Projects/BCB/DNABERT/examples/sample_data/ft/prom-core/3/dev.tsv\"\n",
    "train_data = pd.read_csv(train_data_path,sep=\"\\t\", header=None)\n",
    "test_data = pd.read_csv(test_data_path,sep=\"\\t\", header=None)\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8bca0976-7c6d-4a77-aeb4-9482fe60b4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain training indices that will be used for validation\n",
    "num_train = len(train_data)\n",
    "indices = list(range(num_train))\n",
    "np.random.shuffle(indices)\n",
    "split = int(np.floor(valid_size * num_train))\n",
    "train_index, valid_index = indices[split:], indices[:split]\n",
    "# define samplers for obtaining training and validation batches\n",
    "train_sampler = SubsetRandomSampler(train_index)\n",
    "valid_sampler = SubsetRandomSampler(valid_index)\n",
    "# prepare data loaders\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, \n",
    "                                           sampler = train_sampler, num_workers = num_workers)\n",
    "valid_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size,\n",
    "                                          sampler = valid_sampler, num_workers = num_workers)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size = batch_size,\n",
    "                                         num_workers = num_workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b9b3421-afec-4572-9f9b-e0506c64ea2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96b68629-4887-4789-bc48-4626570b8e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=784, out_features=512, bias=True)\n",
      "  (fc2): Linear(in_features=512, out_features=512, bias=True)\n",
      "  (fc3): Linear(in_features=512, out_features=10, bias=True)\n",
      "  (droput): Dropout(p=0.2, inplace=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "# define NN architecture\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net,self).__init__()\n",
    "        # number of hidden nodes in each layer (512)\n",
    "        hidden_1 = 512\n",
    "        hidden_2 = 512\n",
    "        # linear layer (784 -> hidden_1)\n",
    "        self.fc1 = nn.Linear(28*28, 512)\n",
    "        # linear layer (n_hidden -> hidden_2)\n",
    "        self.fc2 = nn.Linear(512,512)\n",
    "        # linear layer (n_hidden -> 10)\n",
    "        self.fc3 = nn.Linear(512,10)\n",
    "        # dropout layer (p=0.2)\n",
    "        # dropout prevents overfitting of data\n",
    "        self.droput = nn.Dropout(0.2)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        # flatten image input\n",
    "        x = x.view(-1,28*28)\n",
    "        # add hidden layer, with relu activation function\n",
    "        x = F.relu(self.fc1(x))\n",
    "        # add dropout layer\n",
    "        x = self.droput(x)\n",
    "         # add hidden layer, with relu activation function\n",
    "        x = F.relu(self.fc2(x))\n",
    "        # add dropout layer\n",
    "        x = self.droput(x)\n",
    "        # add output layer\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "# initialize the NN\n",
    "model = Net()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8cba29-570f-43d6-90c4-de2979d7036f",
   "metadata": {},
   "source": [
    "# Labeled SGRNA maker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac5663b-3d92-49e2-b282-186389b1085e",
   "metadata": {},
   "source": [
    "## df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b3d19cef-76a5-4119-9eb8-5c9a5752d607",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5510b669-59b4-4507-a2d8-fcabbb07ef63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACA TCA GGT TAC CTC TAC CAA GG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CTG ATG CCA GCT AGT GGG CGA GG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CTG TTT CCC ATC CTT CCG GGT GG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAT GTA TGC ACA GGG AAC AGA GG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCA GAC TCA CCC GCT TGC CCA GG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16744</th>\n",
       "      <td>CAA CGC CCT GCT GCG GCG GCT GG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16745</th>\n",
       "      <td>CTA AGA AAT CCT CTA TCT TCA GG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16746</th>\n",
       "      <td>TGA TCC GCC AGC GCC ATA TCA GG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16747</th>\n",
       "      <td>ATC CGA GGT GGT ACC TGA TAT GG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16748</th>\n",
       "      <td>TCT CTC TGC GCT CTC CCT ACG GG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16749 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0  1\n",
       "0      ACA TCA GGT TAC CTC TAC CAA GG   1\n",
       "1      CTG ATG CCA GCT AGT GGG CGA GG   0\n",
       "2      CTG TTT CCC ATC CTT CCG GGT GG   1\n",
       "3      AAT GTA TGC ACA GGG AAC AGA GG   0\n",
       "4      CCA GAC TCA CCC GCT TGC CCA GG   1\n",
       "...                                ... ..\n",
       "16744  CAA CGC CCT GCT GCG GCG GCT GG   1\n",
       "16745  CTA AGA AAT CCT CTA TCT TCA GG   0\n",
       "16746  TGA TCC GCC AGC GCC ATA TCA GG   0\n",
       "16747  ATC CGA GGT GGT ACC TGA TAT GG   0\n",
       "16748  TCT CTC TGC GCT CTC CCT ACG GG   0\n",
       "\n",
       "[16749 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('examples/sample_data/ft/prom-core/3/train.tsv', sep='\\t', header=None)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3a1c9a66-b367-4871-8529-d23c3fcd7ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def space(string, mer=3):\n",
    "    string = string.replace(\" \", \"\")\n",
    "    new_string=''\n",
    "    k=23//mer if 23%mer==0 else 23//mer +1\n",
    "    for i in range(k):\n",
    "        if type(string)!=type(''): print(string)\n",
    "        else: new_string+=string[i*mer:(i+1)*mer]+' ' if i!=k-1 else string[i*mer:(i+1)*mer]\n",
    "    return new_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d0519d73-83d6-401d-9e56-d05d9d06dd6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GAG AAC GGA AAG GAG AAG GGC GG'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string = 'GAGAACGGAAAGGAGAAGGGCGG'\n",
    "space(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0f3883c8-0cef-4e26-be8b-2755f46f2fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[0]= df[0].apply(space)\n",
    "df.to_csv('examples/sample_data/ft/prom-core/3/train.tsv', sep='\\t',header=None,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bf82bb5c-8192-4162-9810-0c95a50852df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xlrd==1.2.0\n",
      "  Downloading xlrd-1.2.0-py2.py3-none-any.whl (103 kB)\n",
      "\u001b[K     |████████████████████████████████| 103 kB 508 kB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: xlrd\n",
      "  Attempting uninstall: xlrd\n",
      "    Found existing installation: xlrd 2.0.1\n",
      "    Uninstalling xlrd-2.0.1:\n",
      "      Successfully uninstalled xlrd-2.0.1\n",
      "Successfully installed xlrd-1.2.0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f2651d67-7258-42bb-bd74-d02c97cec427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SGRNA</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GAGTCGGGGTTTCGTCATGTTGG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CGCCGCCGCTTTCGGTGATGAGG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GGCAGCGTCGTGCACGGGTCGGG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TGGGCGGATCACTTGACGTCAGG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TTACCATAGTGTACGGGTGCAGG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634846</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634847</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634848</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634849</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634850</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>634851 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          SGRNA  Unnamed: 1  Unnamed: 2  Unnamed: 3  \\\n",
       "0       GAGTCGGGGTTTCGTCATGTTGG         NaN         NaN         NaN   \n",
       "1       CGCCGCCGCTTTCGGTGATGAGG         NaN         NaN         NaN   \n",
       "2       GGCAGCGTCGTGCACGGGTCGGG         NaN         NaN         NaN   \n",
       "3       TGGGCGGATCACTTGACGTCAGG         NaN         NaN         NaN   \n",
       "4       TTACCATAGTGTACGGGTGCAGG         NaN         NaN         NaN   \n",
       "...                         ...         ...         ...         ...   \n",
       "634846                      NaN         NaN         NaN         NaN   \n",
       "634847                      NaN         NaN         NaN         NaN   \n",
       "634848                      NaN         NaN         NaN         NaN   \n",
       "634849                      NaN         NaN         NaN         NaN   \n",
       "634850                      NaN         NaN         NaN         NaN   \n",
       "\n",
       "        Unnamed: 4  Unnamed: 5  \n",
       "0              NaN         NaN  \n",
       "1              NaN         NaN  \n",
       "2              NaN         NaN  \n",
       "3              NaN         NaN  \n",
       "4              NaN         NaN  \n",
       "...            ...         ...  \n",
       "634846         NaN         NaN  \n",
       "634847         NaN         NaN  \n",
       "634848         NaN         NaN  \n",
       "634849         NaN         NaN  \n",
       "634850         NaN         NaN  \n",
       "\n",
       "[634851 rows x 6 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/unlabeled_sgrna.xlsx\", engine='openpyxl')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "751e007b-ddb4-48cb-a25a-24faeffacb69",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_df1(df):\n",
    "    df1=pd.DataFrame(df['SGRNA'].unique())\n",
    "    # fix sgrna with length 21\n",
    "    for i in range(371409):\n",
    "        if len(df1[0][i])==21:\n",
    "            df1[0][i]+=\"GG\"\n",
    "            \n",
    "    #fix sgrna with lenght 20\n",
    "    temp=df1[0].unique()\n",
    "    temp=temp[0:-1] # last one is NaN\n",
    "    missed = [ i for i in range(len(temp)) if len(temp[i])==20]\n",
    "    x=temp[missed]\n",
    "    for i in missed: # fix sequenc inplace with adding A\n",
    "        temp[i]+=\"AGG\"\n",
    "        \n",
    "    temp2 = np.append(temp, [[x+\"CGG\" for i in missed],[x+\"TGG\" for i in missed], [x+\"GGG\" for i in missed]]) # appending the rest\n",
    "    \n",
    "    #check improvements\n",
    "    counter=0 \n",
    "    for i in range(len(temp2)):\n",
    "        if len(temp2[i])!=23:\n",
    "            counter+=1\n",
    "    print(counter)\n",
    "    \n",
    "    # find uniques\n",
    "    df1=pd.DataFrame(temp2)\n",
    "    df1=pd.DataFrame(df1[0].unique())\n",
    "    \n",
    "    #save\n",
    "    df1.to_csv(\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/unlabeled_sgrna0.csv\", index = False, header = False)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e47313-4ed7-4741-88a2-a86d14180071",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c2507f4c-b257-4f8c-b630-161b669ab72a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         GAGTCGGGGTTTCGTCATGTTGG\n",
       "1         CGCCGCCGCTTTCGGTGATGAGG\n",
       "2         GGCAGCGTCGTGCACGGGTCGGG\n",
       "3         TGGGCGGATCACTTGACGTCAGG\n",
       "4         TTACCATAGTGTACGGGTGCAGG\n",
       "                   ...           \n",
       "371405    GTCGAGGAGTCAACTTCTTGTGG\n",
       "371406    GCACCTGTTCGATGTACTTTTGG\n",
       "371407    GCAGAATATTCGGACAAACACGG\n",
       "371408    AGCAACAGAGCCTATCGGCATGG\n",
       "371409                        NaN\n",
       "Name: 0, Length: 371410, dtype: object"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(df['SGRNA'].unique())\n",
    "df1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "b058acc9-660e-4e5c-b04a-a09b2434cce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[0][371409]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "8dcf517d-89ed-4624-8457-478fed510e0f",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59852"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter=0\n",
    "for i in range(371409):\n",
    "    if len(df1[0][i])==21:\n",
    "        df1[0][i]+=\"GG\"\n",
    "        counter+=1\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "12c34b75-ea75-467f-9ddd-240bdf1009f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "334005"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=df1[0].unique()\n",
    "temp=temp[0:-1]\n",
    "len(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "8285ba38-e2c9-4d7b-97b2-5e25ad8216bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame(temp)\n",
    "df1.to_csv(\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/unlabeled_sgrna0.csv\", index = False, header = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "73996efe-e72e-4036-bf57-25761d346480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1040"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter=0\n",
    "for i in range(len(temp)):\n",
    "    if len(temp[i])!=23:\n",
    "        counter+=1\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "aa4dd343-9900-48d3-8327-a89d54de5e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "missed = [ i for i in range(len(temp)) if len(temp[i])==20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "568d81c2-e8b1-4490-8881-15d5772bfd19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GGAATTGCGAATTATAAAAC', 'GGGAACTCCTCAAACACAGC',\n",
       "       'GGAGACACTGCTGTTCTTTT', ..., 'GGCAACTTTTTATGCAAGTT',\n",
       "       'GGTAGCTGGGACTTGCCCTT', 'GGACTTCCAGGACTGCCTTG'], dtype=object)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=temp[missed]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "e015186a-91ad-43f9-9626-a4a243ad4520",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in missed:\n",
    "    temp[i]+=\"AGG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "64ba913c-4d83-4eb8-81fc-50fd80f17af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3578805"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp2 = np.append(temp, [[x+\"CGG\" for i in missed],[x+\"TGG\" for i in missed], [x+\"GGG\" for i in missed]])\n",
    "len(temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "5f38bb7f-6c78-4b8d-8b9b-f2c2b8a3f9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GAGTCGGGGTTTCGTCATGTTGG', 'CGCCGCCGCTTTCGGTGATGAGG',\n",
       "       'GGCAGCGTCGTGCACGGGTCGGG', ..., 'GGCAACTTTTTATGCAAGTTGGG',\n",
       "       'GGTAGCTGGGACTTGCCCTTGGG', 'GGACTTCCAGGACTGCCTTGGGG'], dtype=object)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d8e30234-b9e7-4301-8670-507b2176e617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter=0\n",
    "for i in range(len(temp2)):\n",
    "    if len(temp2[i])!=23:\n",
    "        counter+=1\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "2888bbe8-f26e-411e-a5bb-4b6869db5f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          GAGTCGGGGTTTCGTCATGTTGG\n",
       "1          CGCCGCCGCTTTCGGTGATGAGG\n",
       "2          GGCAGCGTCGTGCACGGGTCGGG\n",
       "3          TGGGCGGATCACTTGACGTCAGG\n",
       "4          TTACCATAGTGTACGGGTGCAGG\n",
       "                    ...           \n",
       "3578800    GGCAGAAATGTGCTCTTGCAGGG\n",
       "3578801    GGGAAGATTTTGGACTATTTGGG\n",
       "3578802    GGCAACTTTTTATGCAAGTTGGG\n",
       "3578803    GGTAGCTGGGACTTGCCCTTGGG\n",
       "3578804    GGACTTCCAGGACTGCCTTGGGG\n",
       "Name: 0, Length: 3578805, dtype: object"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(temp2)\n",
    "df1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "3edc0941-4e3d-4438-a9f9-7698dbc0d440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         GAGTCGGGGTTTCGTCATGTTGG\n",
       "1         CGCCGCCGCTTTCGGTGATGAGG\n",
       "2         GGCAGCGTCGTGCACGGGTCGGG\n",
       "3         TGGGCGGATCACTTGACGTCAGG\n",
       "4         TTACCATAGTGTACGGGTGCAGG\n",
       "                   ...           \n",
       "337120    GGCAGAAATGTGCTCTTGCAGGG\n",
       "337121    GGGAAGATTTTGGACTATTTGGG\n",
       "337122    GGCAACTTTTTATGCAAGTTGGG\n",
       "337123    GGTAGCTGGGACTTGCCCTTGGG\n",
       "337124    GGACTTCCAGGACTGCCTTGGGG\n",
       "Name: 0, Length: 337125, dtype: object"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(df1[0].unique())\n",
    "df1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "d7a034a6-bf51-4964-afc4-6c29dbfd0d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv(\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/unlabeled_sgrna0.csv\", index = False, header = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99f7665-61f3-42da-bc43-a878fb21e7ac",
   "metadata": {},
   "source": [
    "# df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5609d5a2-1d48-409e-bcf9-8eec6b70ef37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sequence</th>\n",
       "      <th>cas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCAGCATCCCAACCAGGTGGAGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GCGGGAGTGAGAGGACTGGGCGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATGACTCTCATACTCCACGAAGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAGTCATCGAGCAGCTGCCATGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGTCACCCTAGCAAAACCAGTGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38473695</th>\n",
       "      <td>CTGAAACATTTAACCAGTTGTGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38473696</th>\n",
       "      <td>GACCAATATACCCATAGCCTTGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38473697</th>\n",
       "      <td>GTTCTTCAACAGTCCACAACTGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38473698</th>\n",
       "      <td>TATTGGTCCCATACGATCTCAGG</td>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38473699</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38473700 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         sequence      cas\n",
       "0         GCAGCATCCCAACCAGGTGGAGG  hSpCas9\n",
       "1         GCGGGAGTGAGAGGACTGGGCGG  hSpCas9\n",
       "2         ATGACTCTCATACTCCACGAAGG  hSpCas9\n",
       "3         GAGTCATCGAGCAGCTGCCATGG  hSpCas9\n",
       "4         AGTCACCCTAGCAAAACCAGTGG  hSpCas9\n",
       "...                           ...      ...\n",
       "38473695  CTGAAACATTTAACCAGTTGTGG  hSpCas9\n",
       "38473696  GACCAATATACCCATAGCCTTGG  hSpCas9\n",
       "38473697  GTTCTTCAACAGTCCACAACTGG  hSpCas9\n",
       "38473698  TATTGGTCCCATACGATCTCAGG  hSpCas9\n",
       "38473699                      NaN      NaN\n",
       "\n",
       "[38473700 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_list  = [\"sequence\", \"cas\"]\n",
    "df = pd.read_csv(\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/GenomeCRISPR_full05112017.csv/GenomeCRISPR_full05112017.csv\", usecols=col_list)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6860d481-0735-4be2-95d2-bf7031c3efd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combination(prefix=[''], allowed=['A','C','T','G'],length=3):\n",
    "    newprefix=[]\n",
    "    if length == 0:\n",
    "        return prefix\n",
    "    for i in allowed:\n",
    "        for j in prefix:\n",
    "            newprefix.append(j+i)\n",
    "    return combination(prefix=newprefix, allowed=allowed, length=length-1)\n",
    "\n",
    "def add(missed_data, missing=1):\n",
    "    allowed = combination(prefix=[''], allowed=['A','C','T','G'],length=missing)\n",
    "    need = []\n",
    "    for i in allowed:\n",
    "        need.append([i+x for x in missed_data])\n",
    "    return need\n",
    "\n",
    "def fix_length(array, length=20):\n",
    "    \n",
    "    if length < 23:\n",
    "        # find missing data\n",
    "        missed_index = [ i for i in range(len(array)) if len(array[i])==length]\n",
    "        missed_data = array[missed_index]\n",
    "\n",
    "        # remove missing data\n",
    "        result = np.delete(array, missed_index)\n",
    "\n",
    "        # fix missing data\n",
    "        need = add(missed_data, missing = 23 - length)\n",
    "\n",
    "#         print(need)\n",
    "        result = np.append(result, need)\n",
    "\n",
    "        # check fix\n",
    "        counter=0\n",
    "        for i in range(len(result)):\n",
    "            if len(result[i])==length:\n",
    "                counter+=1\n",
    "#         print(counter)\n",
    "\n",
    "        return np.unique(result)\n",
    "    \n",
    "    else:\n",
    "        counter=0\n",
    "        for i in range(len(array)):\n",
    "            if len(array[i]) > 23:\n",
    "                array[i] = array[i][-23:]\n",
    "                counter+=1\n",
    "#         print(counter)\n",
    "        \n",
    "        return np.unique(array)\n",
    "        \n",
    "\n",
    "def fix_df2(df):\n",
    "    df2=pd.DataFrame(df['sequence'].unique())\n",
    "    NaN=[]\n",
    "    counter=0\n",
    "    for i in range(747171):\n",
    "        try:\n",
    "            if len(df2[0][i])!=23:\n",
    "                counter+=1\n",
    "        except:\n",
    "            NaN.append(i)\n",
    "            \n",
    "    df2=df2.drop(NaN)\n",
    "    df2.index = range(len(df2))\n",
    "    \n",
    "    temp=df2[0].unique()\n",
    "#     print(len(temp))\n",
    "    temp = fix_length(temp,length=20)\n",
    "    # print(len(temp))\n",
    "    temp = fix_length(temp,length=21)\n",
    "    # print(len(temp))\n",
    "    temp = fix_length(temp,length=22)\n",
    "#     print(len(temp))\n",
    "    temp = fix_length(temp,length=24)\n",
    "#     print(len(temp))\n",
    "    df2=pd.DataFrame(temp)\n",
    "    df2.columns= [\"sgRNA\"]\n",
    "    return df2\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae79361b-6bba-40f3-8882-503f1cdfb19a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCAGCATCCCAACCAGGTGGAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GCGGGAGTGAGAGGACTGGGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATGACTCTCATACTCCACGAAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAGTCATCGAGCAGCTGCCATGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGTCACCCTAGCAAAACCAGTGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747169</th>\n",
       "      <td>GCCAAGCCAGAGAAGACCCGTGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747170</th>\n",
       "      <td>GCTCCTGAGCGAGATGGCGGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747171</th>\n",
       "      <td>hSpCas9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747172</th>\n",
       "      <td>dCas9-VP64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747173</th>\n",
       "      <td>dCas9-KRAB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>747174 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0\n",
       "0       GCAGCATCCCAACCAGGTGGAGG\n",
       "1       GCGGGAGTGAGAGGACTGGGCGG\n",
       "2       ATGACTCTCATACTCCACGAAGG\n",
       "3       GAGTCATCGAGCAGCTGCCATGG\n",
       "4       AGTCACCCTAGCAAAACCAGTGG\n",
       "...                         ...\n",
       "747169  GCCAAGCCAGAGAAGACCCGTGG\n",
       "747170  GCTCCTGAGCGAGATGGCGGCGG\n",
       "747171                  hSpCas9\n",
       "747172               dCas9-VP64\n",
       "747173               dCas9-KRAB\n",
       "\n",
       "[747174 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(pd.unique(df[['sequence', \"cas\"]].values.ravel('K')))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ef69bde-86c2-4d21-8f64-9cc41af04afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCAGCATCCCAACCAGGTGGAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GCGGGAGTGAGAGGACTGGGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATGACTCTCATACTCCACGAAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAGTCATCGAGCAGCTGCCATGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGTCACCCTAGCAAAACCAGTGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747166</th>\n",
       "      <td>GCAGCCGCCCGGCCTCACCGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747167</th>\n",
       "      <td>TGTGAAAGATGCCTCCGCCGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747168</th>\n",
       "      <td>GCATCTTTCACAGAATCCGGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747169</th>\n",
       "      <td>GCCAAGCCAGAGAAGACCCGTGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747170</th>\n",
       "      <td>GCTCCTGAGCGAGATGGCGGCGG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>747171 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0\n",
       "0       GCAGCATCCCAACCAGGTGGAGG\n",
       "1       GCGGGAGTGAGAGGACTGGGCGG\n",
       "2       ATGACTCTCATACTCCACGAAGG\n",
       "3       GAGTCATCGAGCAGCTGCCATGG\n",
       "4       AGTCACCCTAGCAAAACCAGTGG\n",
       "...                         ...\n",
       "747166  GCAGCCGCCCGGCCTCACCGCGG\n",
       "747167  TGTGAAAGATGCCTCCGCCGCGG\n",
       "747168  GCATCTTTCACAGAATCCGGCGG\n",
       "747169  GCCAAGCCAGAGAAGACCCGTGG\n",
       "747170  GCTCCTGAGCGAGATGGCGGCGG\n",
       "\n",
       "[747171 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame(df['sequence'].unique())\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34565715-ee98-4386-aacc-42d535ae1651",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "695153, "
     ]
    },
    {
     "data": {
      "text/plain": [
       "83998"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter=0\n",
    "for i in range(747171):\n",
    "    try:\n",
    "        if len(df2[0][i])!=23:\n",
    "            counter+=1\n",
    "    except:\n",
    "        print(i, end=\", \")\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38bcfac1-8925-4492-98e2-fdb49e0dc9fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[0][695153]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12ce4edc-b14d-44e0-b763-4a81d101ea2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCAGCATCCCAACCAGGTGGAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GCGGGAGTGAGAGGACTGGGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ATGACTCTCATACTCCACGAAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GAGTCATCGAGCAGCTGCCATGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGTCACCCTAGCAAAACCAGTGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747165</th>\n",
       "      <td>GCAGCCGCCCGGCCTCACCGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747166</th>\n",
       "      <td>TGTGAAAGATGCCTCCGCCGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747167</th>\n",
       "      <td>GCATCTTTCACAGAATCCGGCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747168</th>\n",
       "      <td>GCCAAGCCAGAGAAGACCCGTGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747169</th>\n",
       "      <td>GCTCCTGAGCGAGATGGCGGCGG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>747170 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0\n",
       "0       GCAGCATCCCAACCAGGTGGAGG\n",
       "1       GCGGGAGTGAGAGGACTGGGCGG\n",
       "2       ATGACTCTCATACTCCACGAAGG\n",
       "3       GAGTCATCGAGCAGCTGCCATGG\n",
       "4       AGTCACCCTAGCAAAACCAGTGG\n",
       "...                         ...\n",
       "747165  GCAGCCGCCCGGCCTCACCGCGG\n",
       "747166  TGTGAAAGATGCCTCCGCCGCGG\n",
       "747167  GCATCTTTCACAGAATCCGGCGG\n",
       "747168  GCCAAGCCAGAGAAGACCCGTGG\n",
       "747169  GCTCCTGAGCGAGATGGCGGCGG\n",
       "\n",
       "[747170 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df2.drop([695153])\n",
    "df2.index = range(len(df2))\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5f03f2de-63bb-4dc3-adf3-abebb807143a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "269"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfix=[]\n",
    "counter=0\n",
    "for i in range(747170):\n",
    "    if len(df2[0][i])==25:\n",
    "#         df2[0][i]+=\"GG\"\n",
    "        myfix.append(df2[0][i])\n",
    "        counter+=1\n",
    "# df2=df2.drop(myfix)\n",
    "# df2.index = range(len(df2))\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "92e4a218-afb0-447e-be17-f63cab366754",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CGGCCGCTGCTGCCGCTCCTTGTGG',\n",
       " 'CCGCTGCTGCCGCTCCTTGTGGTGG',\n",
       " 'TGGTGGCCGCGTGCGTCCTGCCCGG',\n",
       " 'CCACGGCGGAGCGGAGAGCCCTCGG',\n",
       " 'CGGAGCGGAGAGCCCTCGGCATCGG',\n",
       " 'GAGCAAGCGGTGGAGACAGAGCCGG',\n",
       " 'GCTGGGGATGCTGCGCTATCGGTGG',\n",
       " 'AATATCACCTACAGGTCACCGTAGG',\n",
       " 'TGCCGAAGCCCAGGACCGTGTTTGG',\n",
       " 'CTTGCCCCGAGGCCCCGGACCTCGG',\n",
       " 'CTGCTGCTGTTGTTGGCGCCGCCGG',\n",
       " 'TAGAGCGAACACGAACCATCCAAGG',\n",
       " 'GAGCCCGGGCGCTGGAGGCTCCTGG',\n",
       " 'CTGCGGGCCCCGGCCGCTCGCCCGG',\n",
       " 'CGGCTTCGGGACCGTACGCAAAAGG',\n",
       " 'ACCACTGCACAGTGCGCGTGTCCGG',\n",
       " 'GAAGTGGCTGAGTACCTGACCCCGG',\n",
       " 'ACCTCAGAGCCCGTTTGGATACTGG',\n",
       " 'CCAAGAGTAAGTTATTTGACGTTGG',\n",
       " 'AAGAAGCTGAACGAGTATCGGCTGG',\n",
       " 'TTTGCCCCTTTTAGTAGTGCCTTGG',\n",
       " 'GAGCAGCTGCTAGCCTCGCCCAAGG',\n",
       " 'GCCTGGGCACGCTGGCGCTGTGCGG',\n",
       " 'AGGAAAGCGGCGCAGCTGCCCTGGG',\n",
       " 'GTTGCTCTCCGGCGGCCTCCCTCGG',\n",
       " 'TGTTCCGTCTTCCACCTGTTCGTGG',\n",
       " 'AGCAGGACACGCAGGGCCTGGACGG',\n",
       " 'GCTGGTGGCTCTGCTCGCTGCATGG',\n",
       " 'CCTGGGAACCGCCTCAAGATCTTGG',\n",
       " 'ATCCTGGACCGTGTCACCATCCAGG',\n",
       " 'CCATTTATTGAAACTCCTCGCCAGG',\n",
       " 'ACGTCCAACAACAGCACCATGCAGG',\n",
       " 'CTCTGTGTGCTGGCCATATCGCTGG',\n",
       " 'CTGGCCATATCGCTGGCCTCTATGG',\n",
       " 'GACAATAGCATATTTGTCGCAAAGG',\n",
       " 'CTTCTGATTATTGATTCAAACCTGG',\n",
       " 'GAGAAAGAGAGGCGAACATGCCTGG',\n",
       " 'GAAACTCTAAAAAAGAACCGAGTGG',\n",
       " 'ATCATACATGGAAGCGAATCAATGG',\n",
       " 'CTCTTCAAGTGCTTCCGAAGCCTGG',\n",
       " 'CTGCTCTTCCGAATTAATAGACTGG',\n",
       " 'AGATGGAGAAGAGGGTCATCCTGGG',\n",
       " 'CTGTTCAGGCCCCATATGATCGAGG',\n",
       " 'TCTGGGGGCAAATACGTAGACTCGG',\n",
       " 'ACATCTCTACACCGTTCCCATCCGG',\n",
       " 'CAAGTGTACGACGCGCACACCAAGG',\n",
       " 'AAGGCGGACGTACAGCTCTTCATGG',\n",
       " 'TACGCCGACCCCGAGAAGTTCGCGG',\n",
       " 'AAGCATCTTTTCTTAATCCAAACGG',\n",
       " 'TGTGACCACGTGCAATATACGCTGG',\n",
       " 'GTGCTGCGGCCCGCAGGGCCCGTGG',\n",
       " 'GAGACTCTGAGCTGCCGCCGCGCGG',\n",
       " 'CTGGGCTCCCGTGAGCGGTTGCTGG',\n",
       " 'GGCTCCCGTGAGCGGTTGCTGGTGG',\n",
       " 'AGGATGAACAGAATCTGCCGGATGG',\n",
       " 'AACAGAATCTGCCGGATGGTGCTGG',\n",
       " 'AAGCTCTCCCGCCACCTCATGAAGG',\n",
       " 'CGCTGCTGCCGGCGGCGCGTGCTGG',\n",
       " 'TTCCCCCGCCCGCTGACCCCGCTGG',\n",
       " 'AGAAGCAAATACGCCCTTTTCTTGG',\n",
       " 'ATAGTTTTTGTCTTCATCGAAAAGG',\n",
       " 'GAGCCAGCCATGGAGGCCGCAGGGG',\n",
       " 'GCCTCGCGGCTCGTCCTGCCCCGGG',\n",
       " 'CCCGTGGATGAGCGTGGCATTAGGG',\n",
       " 'TTGGGCTTCATTCTCGCCTTCCTGG',\n",
       " 'CTTCATTCTCGCCTTCCTGGGATGG',\n",
       " 'GGTGACTCTGCCAAACAGCTACTGG',\n",
       " 'TTTCCGTGCAAAAGTCCAGCTCGGG',\n",
       " 'GGGGCTGCTTGCGTGGATTTCCCGG',\n",
       " 'CTTGCGTGGATTTCCCGGGTGGTGG',\n",
       " 'GGGGCAGCACACCCGACCCTCAAGG',\n",
       " 'CCAGAGCTGACTTACCCATTTCAGG',\n",
       " 'GTCTTTCTGCCCTGCATGTACCTGG',\n",
       " 'GCCTCAATGAGGAGTCCAAACATGG',\n",
       " 'GAGGACTTTTATGACATCGGAGAGG',\n",
       " 'ACCGCGACTTCGAGCCCTCCCTAGG',\n",
       " 'CTCTGCCCGGCGAGTCGGGCTCTGG',\n",
       " 'AGCAGCGCTATGGGCTGTCGTGCGG',\n",
       " 'TGACCTGCTGCAGCTTCGCTGTCGG',\n",
       " 'GGTCGTTTCATCTGCCTGGTCGTGG',\n",
       " 'CTCTGCGTGGCCGTGGCCATCGTGG',\n",
       " 'CCTGGGCCTCCAGTCTTGTCCCTGG',\n",
       " 'AGCAGGAGCTGACAGTAGCCCTTGG',\n",
       " 'CCAGCTTCCTACCTGAGGATGCTGG',\n",
       " 'GTAGTTATTGTCCAACTACCTGTGG',\n",
       " 'GACCCAGGAACCAGCAGAGGCCCGG',\n",
       " 'CTTTCCAGCCACAACGAGCTGCTGG',\n",
       " 'CGCCTAAGAAGTCCCGGGCCGCCGG',\n",
       " 'CCCATCGAGATCCCGATGTGCAAGG',\n",
       " 'AGATCCCGATGTGCAAGGACATCGG',\n",
       " 'ATGGGCCACGAGAACCAGCGCGAGG',\n",
       " 'ACGTGTCACACTTCCCTGTCGTAGG',\n",
       " 'AACTGACTGGCTTGTGCTATGTTGG',\n",
       " 'TTGCTGCTGCTCCTAGCGCAGCTGG',\n",
       " 'GCGGCCGCCGCGTCCAAGGCCCCGG',\n",
       " 'GCGCTGCTGGGCGCACTGTCCGCGG',\n",
       " 'CTGCAGCGCTCTAGCGGCGCTGCGG',\n",
       " 'CTGCTGTGGCAGCTGCTGGCGGCGG',\n",
       " 'CTGGCGGCGGGCGGCGCGGCACTGG',\n",
       " 'CGGGCGGCGCGGCACTGGAGATCGG',\n",
       " 'CGTCCGCCTTCCCGCTGCCCGCCGG',\n",
       " 'CGCCCGCCGAAGACCGCTCCCTCGG',\n",
       " 'CAGCGCTGGTCGCCTGCGCCCGCGG',\n",
       " 'CGTGCAGACCTGCGAAGAAGTTCGG',\n",
       " 'CGGATGTGAAGGCTCGGAGCTGCGG',\n",
       " 'CCAGCCAGACAAAGCCGCCTGATGG',\n",
       " 'ACAAAGCCGCCTGATGGAGCGTTGG',\n",
       " 'CAGCGACTCGGCGCCTGACTGCTGG',\n",
       " 'TCGTGGTGCTGTGTGTGGCCTGCGG',\n",
       " 'AGCCTGACAGACCCTTCGAAGTTGG',\n",
       " 'CCGATGCTGTGGCCGTTACAATGGG',\n",
       " 'ACATGCTGGCTGACAGCATCTCAGG',\n",
       " 'GATTCATCCTGTTACTGGTATTCGG',\n",
       " 'TGAAAAATTCAATGACTTTCAGCGG',\n",
       " 'TGCTTATTGGTTCTCCGTTAGTTGG',\n",
       " 'GAGATGTCTATAAGTGTCCAGTTGG',\n",
       " 'AACATCACCCACGCCTATTCCCAGG',\n",
       " 'GATACAGTGTCTTACAACATGTTGG',\n",
       " 'AAACCAATGGCTACCAGAAGACGGG',\n",
       " 'TGTACAAGTGTCCAGTGATCCACGG',\n",
       " 'ATGCTCTGTGCGCTCGCCTTGATGG',\n",
       " 'CTTGGGAAGCGAGGCGCGAACCCGG',\n",
       " 'GTCGGGGGCTTCAACTTAGACGCGG',\n",
       " 'AATCTCAGGATTGAAGACGATATGG',\n",
       " 'GGCTCGGAGCCGCGACCCTTGGGGG',\n",
       " 'TGGAAAAGCTCACAGTGTACAGCGG',\n",
       " 'CTCACAGTGTACAGCGGCCCCAAGG',\n",
       " 'CCCGGCTGCGCCGAGGGGCGCCGGG',\n",
       " 'CTGCTGCTGGCGCTGGTGGTCGCGG',\n",
       " 'TATACAAGCAGGGCCAAATTGTGGG',\n",
       " 'CCCTGGTGGGGCTGCTCTCCCTCGG',\n",
       " 'GCCTGCGGGTCCGTCTGCGGCCCGG',\n",
       " 'CGGGTCCGTCTGCGGCCCGGTGAGG',\n",
       " 'AAGCCCACGGTCCATCACCTCTCGG',\n",
       " 'CCTGTTCTTTCTATTTCTAGGAAGG',\n",
       " 'GCGCGGCGCTGCGCCCGACGAGAGG',\n",
       " 'CAGGCATAGTGGTGCCCAATGACGG',\n",
       " 'TCTGCCTGCAAAACGACCGGCGAGG',\n",
       " 'TTGTCTTCTGCAGTTGCCCCAAAGG',\n",
       " 'GAACTGTGCGGGCAGACGTGGCAGG',\n",
       " 'CACCTGCACCTGGAGTCTACCCAGG',\n",
       " 'CCCCTGCTCTCAGCTCTGGTCGCGG',\n",
       " 'CTGGAGCACAAAAGAGTCTCCGCGG',\n",
       " 'GAGCACAAAAGAGTCTCCGCGGTGG',\n",
       " 'CCGATTGCCAGGGTGCTGACCGTGG',\n",
       " 'ACTGCCCAGATGGGTATGACGAAGG',\n",
       " 'GCTCTCGTCGCCTGCCTAGCGCCGG',\n",
       " 'TCGCCTGCCTAGCGCCGGCCAGTGG',\n",
       " 'CAGATGAGTTCCGCTGTGACAACGG',\n",
       " 'CCAGCAGCCCCGAGTGTGCTTGTGG',\n",
       " 'CAGCCCCGAGTGTGCTTGTGGTCGG',\n",
       " 'CTGCTGCAGCTCCAGCATCTTGCGG',\n",
       " 'CGGCAGCGGCTGATCCGCTGCTCGG',\n",
       " 'CAGCGGCTGATCCGCTGCTCGGCGG',\n",
       " 'CATGTTTCCTTACTGTCGTTATTGG',\n",
       " 'ATAACCATCGTTATAAGTGGAATGG',\n",
       " 'CCATACACCTCGAAGCATTCCTGGG',\n",
       " 'CGCGGGACACGACGCCCCCCGCAGG',\n",
       " 'CCATGTACATCACAGCCCTGTTAGG',\n",
       " 'GACATTGTTATGGCCTCCTCCGTGG',\n",
       " 'CTTTTCTGGACCCCGAGCGGGGAGG',\n",
       " 'AAGACCCAATGTTGAAGTTCTCAGG',\n",
       " 'TTACTTGTCAAGTTTTTGCAGAAGG',\n",
       " 'ACTTTGAATATGATAAAAGCCTGGG',\n",
       " 'TTGCTCGAGCCAAGCACCGAGAAGG',\n",
       " 'ATGGGCTGCCAAAGGGACCGTGCGG',\n",
       " 'GCGAGGTTCCCGAAGGCCGACCTGG',\n",
       " 'CTGCTGCTGCTGCCGGGCATGTGGG',\n",
       " 'CGGCTGGATGACCTCTTCATCCTGG',\n",
       " 'GATGGCAAGCTCTTCATCGGCACGG',\n",
       " 'CCCGCCGAGGGTTTCAATCACCTGG',\n",
       " 'CCCAGGGGCCCTGGTGGTATGCGGG',\n",
       " 'TGGTATGCGGGAGCGTGCACCAGGG',\n",
       " 'CCGGGGCGCGGACGAGCCCGTGTGG',\n",
       " 'GGGCGCGGACGAGCCCGTGTGGCGG',\n",
       " 'GGGGCGGCGCGGGCCGGCGCCCTGG',\n",
       " 'CCTTCATGGAGAAGCCGCCAGCCGG',\n",
       " 'TGTAGGTAAATCGAGTATTGTGTGG',\n",
       " 'GGACTTCCTCTTTCCTCACCTGCGG',\n",
       " 'AGAAAATTCAAGTTGGTGTTCTTGG',\n",
       " 'ATTGCTATTCAACACCAGGTGCTGG',\n",
       " 'TCTTCTGATCTTGCGGATGAAAAGG',\n",
       " 'GCTGCCGGGGCGCTGGGCGTCGCGG',\n",
       " 'GGCCGATGCTGCTTCTACACGGCGG',\n",
       " 'CTGGTGACCAGCGTCACGCTGCTGG',\n",
       " 'CGCTGGCACTTGTGCGTGCTGCTGG',\n",
       " 'TGCTGCTGGCCCTGGGCACACGCGG',\n",
       " 'CTCTGGCTCTGGCTGTGCGCGCTGG',\n",
       " 'CGCGCGTGGATCCTGCTCACCTTGG',\n",
       " 'ATCCTGCTCACCTTGGGCTTGGTGG',\n",
       " 'CGCTTCAGCCCAGATGTAGCCCTGG',\n",
       " 'GATCACATATTTTCATTCGACCTGG',\n",
       " 'CCGGGGCTGCCGCCGTGATCCCGGG',\n",
       " 'GCTGCCGCCGTGATCCCGGGCCTGG',\n",
       " 'CCTTGCTGGTGGATGAGGAGCGTGG',\n",
       " 'GCATTCCGGACAATTTGCGTGTTGG',\n",
       " 'GGCTTCCCTACTGACCGGGGCCTGG',\n",
       " 'CCTGGACGCCCTCTACTCTCTGCGG',\n",
       " 'CTGCTGCTGCCGACGACGACCGCGG',\n",
       " 'GTGATGGAAATACTCTCTACGTGGG',\n",
       " 'CACTTAGCTTCTTCCACCAGAAGGG',\n",
       " 'TAGTACGGCGGTTCTCCCAGACCGG',\n",
       " 'GACTTCCTGACACTGACGCTGACGG',\n",
       " 'CCCTTCCCGCTACTGCTGCTGGCGG',\n",
       " 'CTGCTGGCGGTGCTGAGCGGCCCGG',\n",
       " 'CGGTGCTGAGCGGCCCGGTATCCGG',\n",
       " 'GAGTTCAGAGCGAAGAATGCTGTGG',\n",
       " 'TACTTCCCCATTATCCTGGTTTCGG',\n",
       " 'ACCTTGAACCGGACCTTGCTAGTGG',\n",
       " 'CGCGCCCGCGTCCCTGGCCCGCCGG',\n",
       " 'GGGGAGGAGGACGACGACTATCTGG',\n",
       " 'TTGTTCTTCATCATGCTCTCGCTGG',\n",
       " 'CCCCGGCCCTGGGCTCAGCCTCGGG',\n",
       " 'GGCGGGGCGTGTCGGCCCGGGGCGG',\n",
       " 'AAGCCCTGACCCAGAGTCCCGCCGG',\n",
       " 'CTGCGGGCATTGCAGATCGCGCTGG',\n",
       " 'CGGGCATTGCAGATCGCGCTGGTGG',\n",
       " 'AAGAAAAAGCCATCGACGTCACAGG',\n",
       " 'ATTAAAGACCAGGGAGCCCAAATGG',\n",
       " 'GAGAGAGGGGGAGAGGCCCGCTCGG',\n",
       " 'CCAACGGAGTGATCCCGCCGCCCGG',\n",
       " 'CCCAGCCGCAGCTACAGCGAGCGGG',\n",
       " 'CCCCCTCTAAGTCCAGACGTGGAGG',\n",
       " 'GATGAAGCTGTCCTGGACAGAGGGG',\n",
       " 'AGTCCATGTGCCGAAGAGTTACAGG',\n",
       " 'ACCAGCCACAGCAGGAACTGACTGG',\n",
       " 'AGATGGATACTCTGCAGCATGACGG',\n",
       " 'AAGCTCCGCAGCCTGCTGGCCGAGG',\n",
       " 'TCAATCCCGACGATAGAGAGCTCGG',\n",
       " 'ATGGAGAAGATTGTCAGGAAAACGG',\n",
       " 'TTGCTGCTGCTCCTGCAGGTGGCGG',\n",
       " 'GTGGCGGGGCCCGTGGGCGCCCTGG',\n",
       " 'GCGGGGCCCGTGGGCGCCCTGGCGG',\n",
       " 'AGAACATGACCACTGCCCCAAACGG',\n",
       " 'AACTTTGGCTTGCCACATGGCTCGG',\n",
       " 'TTGAAGATGAGGATATCCAAGCTGG',\n",
       " 'CTTCTCTGTCTTGTGACCGCAATGG',\n",
       " 'ATTACCAGCCGCCAACTTCACAAGG',\n",
       " 'GCATAAAAAGCAATATGTTCACAGG',\n",
       " 'TGCTCATGGCCGGTCCTGTGTTTGG',\n",
       " 'AACAGCTGCAGCTGCTGGAGCTCGG',\n",
       " 'AACATGAGCTCGAGCCCCTACAAGG',\n",
       " 'GAACCAGAATCCAGTTCTCCGACGG',\n",
       " 'ACGAGGCGTCTTGTTGCCGCTGGGG',\n",
       " 'AGGACCTGGAGAAGATCGGCATGGG',\n",
       " 'CCGTGGGAGGGAACCGACGAGCCGG',\n",
       " 'CCCAGGCTGGTCTTGAACTGCTAGG',\n",
       " 'ACTGGTAAGACCATCACTCTCGAAG',\n",
       " 'ACTGGTAAGACCATCACTCTCGAGG',\n",
       " 'CACTTGGTCCTGCGCTTGAGGGGGG',\n",
       " 'ATCCACCTGCTCACTGTTCAGCTGG',\n",
       " 'TTCCACTGGCAAGCCACTATTATGG',\n",
       " 'CACAGTGTTCAGCAGGTCCTGTTGG',\n",
       " 'TTCCATTGGCAAGCTACAATAATGG',\n",
       " 'TCTTGATATTCTACGATCACAGTGG',\n",
       " 'CGAATCAAGCGGGAGTTCAAGGAGG',\n",
       " 'TTCAAGGAGGTGCTGAAGAGCGAGG',\n",
       " 'ATGAGAATTTTACAGAATTAAGAGG',\n",
       " 'TCTTGGGGGGATGGTTCCATCATGG',\n",
       " 'GCGGAGAGTCCGACTGAGGAGGCGG',\n",
       " 'AGTCCGACTGAGGAGGCGGCAACGG',\n",
       " 'TCTACAGCCTCCGCTTCTCTCCTGG',\n",
       " 'CTCCTGCTGTCCGGCTGGTCCCGGG',\n",
       " 'GATCTTGGCAGTTCAGTTTCGATGG',\n",
       " 'GCCGCGGCGGCACAGAGACCGTGGG',\n",
       " 'GGCAAGTTCGAGTTCTCCCGCAAGG',\n",
       " 'GCCCCTATGGGGGGCGCCCCTCCGG',\n",
       " 'AGGCCGCGGACGCTCCCCCGGGCGG',\n",
       " 'TGGAGCAGCTGGATCAAGTCCACGG']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff38cba4-55d1-4215-a827-0da8d0ef779d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "747170"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=df2[0].unique()\n",
    "len(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "69d8d271-fa8e-43e8-94e4-d5d92a693edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=\"CGGCCGCTGCTGCCGCTCCTTGTGG\"\n",
    "len(a)\n",
    "len(a[-23:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ad5ae4b-5d6a-4f07-990c-5a38991bacd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "470"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfix=[]\n",
    "counter=0\n",
    "for i in range(len(df2)):\n",
    "    if len(df2[0][i])>23:\n",
    "#         df2[0][i]+=\"GG\"\n",
    "        myfix.append(df2[0][i])\n",
    "        counter+=1\n",
    "\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2f6b084b-20e9-41d0-904e-e08b6ec49e08",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ATCAGCCAGAGCCCACACGGGG',\n",
       " 'AGCGGAGAGCCCTCGGCATCGG',\n",
       " 'CTGGCTGTTCCCCGGGCTGTGG',\n",
       " 'TTCCCCGGGCTGTGGCTGTTGG',\n",
       " 'GCCAAGGTGGAGCAAGCGGTGG',\n",
       " 'AAGAATGATGACCCTTCAAAGG',\n",
       " 'TTTCTGGTGATCCAGCCCCTGG',\n",
       " 'CCCCTGGTGATCTGGATGCCGG',\n",
       " 'ATAGGTGATAGTTGGGAATGG',\n",
       " 'GGTCTGTGATGTCACATCTAGG',\n",
       " 'GACAGACATGTCTTCCCATGG',\n",
       " 'TGATTGTCCAGGCTCGGCTTGG',\n",
       " 'AATAACCAAATGCAGCGGAAGG',\n",
       " 'CCACATCGTGCGGCAGCTGCGG',\n",
       " 'ATTAATACTGTGAAGGGAAAGG',\n",
       " 'GGGAAGAATTGAAAGTGAAGG',\n",
       " 'TGCTTCGAGATGTGTGGTTTGG',\n",
       " 'TGAAAAAGCACTTTCAGAAGG',\n",
       " 'TGATTCACCCCCAGGGGAGG',\n",
       " 'TCCAACAACAGCACCATGCAGG',\n",
       " 'CTGGCCTCTATGGTGACCGAGG',\n",
       " 'ACCTCTGACAGCACGTTCCTGG',\n",
       " 'GATGAATTATTACAGACAAGGG',\n",
       " 'GGGCTCCGCAGGGGGCACCCGG',\n",
       " 'GGCTGTATTGAAGAGCAGGGGG',\n",
       " 'CGATCCATCAAGACCACCCGGG',\n",
       " 'GCGGCTCCTGCGGCGGTGCCGG',\n",
       " 'GGCTGCTACGGGGCCATCCAGG',\n",
       " 'CAGTTGGAGCAGTTTATTTTGG',\n",
       " 'ACGGGCCCGCTCAGACCAATGG',\n",
       " 'AAGCTGGAGCAGCTGCTCCTGG',\n",
       " 'TACACCGAGTGCAGCCACTCGG',\n",
       " 'GAGCGGCGGCTGCGCGCGCTGG',\n",
       " 'GAGCAGCTGGCGCGGGGCGAGG',\n",
       " 'GCGGCTGCCCGGGGCTCGACGG',\n",
       " 'CTCAGCGTCACCTGGGTGGAGG',\n",
       " 'GCGAGTGTCCACTGTGCACGGG',\n",
       " 'TGCAAAAGTCCAGCTCGGGGGG',\n",
       " 'GCCCCCACTTCCAGCCCCCGG',\n",
       " 'GCTTTGACCCTGACTATGTTGG',\n",
       " 'TTGGCCAGACTTGCATCCGCGG',\n",
       " 'GTCATGTGCAAGAGCCTACTGG',\n",
       " 'AAGATGAGGCTTCTGGGATAGG',\n",
       " 'CATGTGAAGCTCACCGAGACGG',\n",
       " 'GTGAATCTGGATTGTTCTCGGG',\n",
       " 'AGTATTGCCGCGGTGGAAATGG',\n",
       " 'GTTGGGTCCGCCCGCAGCGAGG',\n",
       " 'AATACAGTGCATTTCGAGTAGG',\n",
       " 'ACACCCCACATCGACAATTTGG',\n",
       " 'ACCCGCTGGAAACACCCCTCGG',\n",
       " 'CACACTCCTCGCCCAGCCCGGG',\n",
       " 'GCGCCTGACTGCTGGGACCAGG',\n",
       " 'GTGGTGCTGAAGCTCTTTCTGG',\n",
       " 'TTCTCTCGGCACTGGTGACTGG',\n",
       " 'GAGATCCTAAGAAGCCGAGAGG',\n",
       " 'TCTGGTACTAAAGCATTCATGG',\n",
       " 'CACCATGGAGAGGAGGAGGTGG',\n",
       " 'AAACACTGGCCGTTCCGGGTGG',\n",
       " 'GCGGCGGGCGCGCTGCTGCTGG',\n",
       " 'AACATGAGGTGGCTGTGCTGGG',\n",
       " 'TGCAGCAGCACGACATCAGTGG',\n",
       " 'ACCGGCCGCCCCTACAACGTGG',\n",
       " 'TGCTCACCGATATGAAAAAAGG',\n",
       " 'TGGAGAATGTATACAAGCAGGG',\n",
       " 'ATGACATAGAAAATCCCAGAGG',\n",
       " 'TCTGCGGCCCGGTGAGGAGCGG',\n",
       " 'CCCCTCAGCAGCAAGGGTTCGG',\n",
       " 'GTGCATGTCCGCCAGACTGAGG',\n",
       " 'AGGTCCCGCCTCGTTCCTCTGG',\n",
       " 'GGTTTGAGGACTGTCCCGATGG',\n",
       " 'CCCGCTACCACTGCAAGAACGG',\n",
       " 'ACAAGAGCTTCATCTGCGATGG',\n",
       " 'CTCTCAGCTCTGGTCGCGGCGG',\n",
       " 'CAGTGCTCTTAGAAGTGCAGGG',\n",
       " 'CGCTCTGGCCCATCCAGACCGG',\n",
       " 'GGCACCTTACAGAGGCCCCTGG',\n",
       " 'GGATTATTGCCGATTGCCAGGG',\n",
       " 'TTGCTTTATGCAAACAGACGGG',\n",
       " 'AGAATGCTACGATTGTAGTTGG',\n",
       " 'TGTTGTTGAAGCTATTTCTGGG',\n",
       " 'AAGAAATGCTCTTTTGCTTGG',\n",
       " 'GGAATCTGGAAGCACTTACAGG',\n",
       " 'AAAAGCAATCGGGAACTTCTGG',\n",
       " 'TGTGCGGGGACAAGGTGTCCGG',\n",
       " 'CAGGACCCGCCCATCAGCCCGG',\n",
       " 'ATGGTGAGCATCTTCTGCTCGG',\n",
       " 'ATCTCGGGAAAGATCACGTCGG',\n",
       " 'GCTAAGGGCAGGAGCTACCTGG',\n",
       " 'GCCAAGTGGCCATCGTCACCGG',\n",
       " 'GCGTCCAACCCCGAACGGGGGG',\n",
       " 'CAGAAATTGGATGATATCAAGG',\n",
       " 'CCCCCCTTCCGCACCTTCTCGG',\n",
       " 'GGACGGGGGCCGTCTATGTGGG',\n",
       " 'AAGTGCACGCCGCCCATCGAGG',\n",
       " 'CCCCTGCTCGCCTATCTGCTGG',\n",
       " 'CACCGCCGCCGCCGCCGCCGGG',\n",
       " 'ACTCACCTGGGTACTGGTATGG',\n",
       " 'GAACGTTCTCCTGGTGCAATGG',\n",
       " 'GAGAAGCCGCCAGCCGGCAAGG',\n",
       " 'CTGTACAGCATAACCCGCCAGG',\n",
       " 'TGGTGATAGTGGACTGGATGGG',\n",
       " 'GAGGACCAGGTGTACAACTAGG',\n",
       " 'AGTATTGTGTGGCGGTTTGTGG',\n",
       " 'ATGACTCAAATCATACAATAGG',\n",
       " 'TCAAAAACCATGTACTTGGAGG',\n",
       " 'AGGGCGGTATGCGACACACGGG',\n",
       " 'CGTCGAGCGGCGGGGAGCTAGG',\n",
       " 'GCTGCCATCCGGAAGAAACTGG',\n",
       " 'GGCTTGGTGGCCTGCGTGTCGG',\n",
       " 'GCGGTGTCCACCACACCTGCGG',\n",
       " 'CGCTGCTGCTGTTCTTCGTAGG',\n",
       " 'CTCCTGTGGGGTTACTTACTGG',\n",
       " 'GGCCATTTGCTGGCTGCTAGGG',\n",
       " 'AGGGAGGAGTGTGTTCGAAAGG',\n",
       " 'TTGCCCACCACCTCGTCCCTGG',\n",
       " 'AACCACTATCCCGTGTTTGTGG',\n",
       " 'GCAATATCCGGTTTTTAGAGG',\n",
       " 'CGTCCCTGGCCCGCCGGCTCGG',\n",
       " 'CTGCGGGACAACTACACCCTGG',\n",
       " 'TTCAAGCTACGAAAATAAACGG',\n",
       " 'AGAAGGGAAAGGAGTATGATGG',\n",
       " 'ATGGAGGAGAATCTGGAGCAGG',\n",
       " 'ACATCCCCGAGTCCCAGATGG',\n",
       " 'AAGCAGTTGTGGATAGAGGTGG',\n",
       " 'CAGCATGGATCTTATCAGCAGG',\n",
       " 'TTTTTGTTATTGGGTCCAGCGG',\n",
       " 'AAGAAGCTGTTGTGGATCTTGG',\n",
       " 'CTCCTAGAAATATTCCTTCAGG',\n",
       " 'AAGGAAGACCGTGTGGACGAGG',\n",
       " 'CACAGAAAAATGGCTCCGCTGG',\n",
       " 'ACCCCAGCCACAACGACACTGG',\n",
       " 'CGGTCCGTGGCGCCAGCTTTGG',\n",
       " 'AGCAGCACCCCTGGCCCAGTGG',\n",
       " 'GACAGGATTAACGAATATGTGG',\n",
       " 'ACGGAGTTTATCATCACCGCGG',\n",
       " 'TGGCCTCAACAATCGCATCCGG',\n",
       " 'TATTTGTCTCGATATTCTGAGG',\n",
       " 'AATGGAGATCAACCATTCTAGG',\n",
       " 'AATGGAGGTCAACTATATTGGG',\n",
       " 'GATTACGACTATCATTTTTGGG',\n",
       " 'ATCGGCCACGGCGCCTTCGCGG',\n",
       " 'TGAATTTCTCCGCATCAAATGG',\n",
       " 'CTGTTTTCTCTGAGTTCTGTGG',\n",
       " 'ACATGGTATTGCAGTAGACTGG',\n",
       " 'ATCTTATCGGAGATGAAAAAGG',\n",
       " 'AATCGGCAGTTGTCCACACTGG',\n",
       " 'TATAAGCTCATCCTCAAAAAGG',\n",
       " 'AATATGGTGGCCCTCCACCTGG',\n",
       " 'AACATGCACCAGGCGTGCATGG',\n",
       " 'GAATCCCCAATCACGTCCCCGG',\n",
       " 'AGATGAGCAACCGAGCGACAGG',\n",
       " 'ACCCACTGGTAGCCGTAACAGG',\n",
       " 'TGCTCATCCGTGGTAGCATTGG',\n",
       " 'CGGATGTCTCCAAGGTAAAGGG',\n",
       " 'CAAGGGGCCGTTTAGCCGGTGG',\n",
       " 'GTGGATTATTACATCGACCCGG',\n",
       " 'CAATCCGGGTGTCGGGAGTTGG',\n",
       " 'GCTGTACGCATATCCAATGAGG',\n",
       " 'TGGAGGCGTCGGAGAGCACGGG',\n",
       " 'GACTGGTACGCGGCCGTGCAGG',\n",
       " 'AATGTCCAGATGCTCCCGCTGG',\n",
       " 'ACGTGCTGGTCCTGATGAAAGG',\n",
       " 'ACGTGCCAGTAGATCATGATGG',\n",
       " 'CCACCGTATTTCCAGATGATGG',\n",
       " 'GCAGCCTTTTCATATCCAAAGG',\n",
       " 'AACGTTGTCTATTGCTGACAGG',\n",
       " 'GCAGCAGACTCTACGGAACAGG',\n",
       " 'TTCTTTCAAGTCCCACCAGGGG',\n",
       " 'GCCCATCGACAGAGTGCCAGGG',\n",
       " 'GTGAAGACGGCATCCGGCTGGG',\n",
       " 'AGGATCCCCTAAAGACCCCTGG',\n",
       " 'CCTTTGTCAGTTGTAGGACAGG',\n",
       " 'CATACACCTGTTACCAGCTTGG',\n",
       " 'TTATGATGACTTGTACCATTGG',\n",
       " 'AATCGCAGATGTCCCCGAGTGG',\n",
       " 'TCACATTCACACCGAAGTCCGG',\n",
       " 'ATGCACTTGGGTGCGCCCGTGG',\n",
       " 'CCTTACCGTGGTGTAGCACAGG',\n",
       " 'GCTACATTTGTGGAGCTCCTGG',\n",
       " 'AAAGTCTGAAGCACTAAGAAGG',\n",
       " 'GGGGGTTGTTTTACATCCATGG',\n",
       " 'TTTCTATCCAAATCACTCATGG',\n",
       " 'ACATGTTGTCTGGAAAGCATGG',\n",
       " 'AACTTGTACATTTACGGTAAGG',\n",
       " 'TTGGCAAGTACAATCGTACTGG',\n",
       " 'GACTTCCTGAATAGATGGACGG',\n",
       " 'CCGCAATGCAGATTCGGGTGGG',\n",
       " 'GCAACAGCGGTCACTCAACAGG',\n",
       " 'TGAAGTCCCTGGATGCATATGG',\n",
       " 'AGTTGTTGACAAACCACTGCGG',\n",
       " 'GGACAGGCCAGATCTGCCCCGG',\n",
       " 'CGAAGGGGTTTGTAAATCCAGG',\n",
       " 'CCTGAGAGGTTTAAGGAGAGGG',\n",
       " 'CACCAAAGTGACCAGGTAGAGG',\n",
       " 'TTTTGGGACGATACCCGTGAGG',\n",
       " 'AAATACTGTGGCCCCTCCATGG',\n",
       " 'GTCTTGCAGATTGTTACCATGG',\n",
       " 'CCGGGCCAGATAATTGCACAGG',\n",
       " 'TGGATCTATGTGTCACATCTGG',\n",
       " 'TTCAGTATTCTCCGAGTGCTGG',\n",
       " 'CCGTAATCACTGTAGAAAATGG',\n",
       " 'GTATGTCTCTTAGGGAATCTGG',\n",
       " 'AATAATGTTGCAGCCCAGTGGG',\n",
       " 'ATCCAGTCAGCTTGCAAAAAGG',\n",
       " 'GACTGTCCAATCACTATAAGGG',\n",
       " 'ATCTGCAAGGAATACGTAGAGG',\n",
       " 'CACGAACACCACCACGGCGCGG',\n",
       " 'CCCGGAACAGCGCGCCGAACGG',\n",
       " 'ACCACCCCGATGATCAGAGAGG',\n",
       " 'CTGGAACAAACCATAGCATTGG',\n",
       " 'ACTACATTGGACCACACATTGG',\n",
       " 'ATGACTTCCCAGAATCTACAGG',\n",
       " 'AGTCCAGAGGAGTTGCCTGAGG',\n",
       " 'ACGCCTTCTACTCAGGCCCAGG',\n",
       " 'TTCAATTGCACCCCGCCAGAGG',\n",
       " 'CCGGCCTTTTGTCAGGGTCTGG',\n",
       " 'GGCAAAATCATCACTACGAAGG',\n",
       " 'TTCGGCATCGGGCGACAGCAGG',\n",
       " 'GAACTTGATCCCGGTCCGCCGG',\n",
       " 'TTCGGGACTTTCCACATCCAGG',\n",
       " 'TCGCTGAGCCGCCATACGAAGG',\n",
       " 'ATACCTTGGCAGTGACCGGGGG',\n",
       " 'GGTGACCTCGCTATCGTCGGGG',\n",
       " 'GCAGCGAAACTCACTGGCAGGG',\n",
       " 'CCTTCACGCTCGATCTCAAAGG',\n",
       " 'ACATAGGGTCAGGAAGTGCCGG',\n",
       " 'AGGGACAGCTCTGGACATAGGG',\n",
       " 'CTTCATCATCGGCTCGCTCTGG',\n",
       " 'ATTGACTATAACTCCTGGGAGG',\n",
       " 'ATCTTCACGCCCCGGAACTTGG',\n",
       " 'GTGGACAAGGCTAATCCGAAGG',\n",
       " 'AAGGAAGAAACCCATACGAGGG',\n",
       " 'ACAGGTAGCACATCGGAAAAGG',\n",
       " 'GTCGCACGCGGGACGAGGTGGG',\n",
       " 'CGTGCGACTTCTTCGGGGACGG',\n",
       " 'GCTGGAGGACCTCAGACGACGG',\n",
       " 'CTGACGCGCGCCTTCCAGTGGG',\n",
       " 'GCGATCTCGAGGCGCGTGCAGG',\n",
       " 'TACGCTATCGAAGAGCTCCGGG',\n",
       " 'GGCGTATCGGACAGCTCGGCGG',\n",
       " 'GCCGCACATCTTGTCAACCAGG',\n",
       " 'ACCACCAATCCGGTCGTAGTGG',\n",
       " 'GGGGGCAAACATAATGACCTGG',\n",
       " 'ATCCGCCTACCCCGCTCCAGGG',\n",
       " 'CGAGCTGTGCGTTTCTCCATGG',\n",
       " 'CGACGAAGAACCAGCCTAAAGG',\n",
       " 'CCTTACTTGGGCCGAGTAGGGG',\n",
       " 'ATCCACGAAGCGAGATGGCAGG',\n",
       " 'GGAGCAACTTGACGGAAGTTGG',\n",
       " 'TCCACAACAGAAGGATCGAAGG',\n",
       " 'TAGAACTTACCGAAAATGCTGG',\n",
       " 'TAGCTCGCATTGTGCCAAGTGG',\n",
       " 'CTCTTCGAGTATTAGCCCTTGG',\n",
       " 'GTGCGATCGATTCAGCCCGAGG',\n",
       " 'GTCCCGGGCAAAGACGAACTGG',\n",
       " 'TAAAGTTGAAATTCGGGTAAGG',\n",
       " 'CAATTCCAACTTGCAGCTCAGG',\n",
       " 'TTATCAGGTAATGTACCGTTGG',\n",
       " 'TGTGCCGGCAAATTGTCAATGG',\n",
       " 'CTTACTTGTGTGGTAATGCAGG',\n",
       " 'AACGACTTATGTAGCGCTCCGG',\n",
       " 'GATACAGCCTTCGAATCGGCGG',\n",
       " 'GTGGGTAGCATTAGAAAACTGG',\n",
       " 'CCTTTTGTCCGTGTCCAAGAGG',\n",
       " 'TCTACTCACCAGACGATCCTGG',\n",
       " 'GCCACTCTGCAAAAACACCGGG',\n",
       " 'GAGCCGTGAAGAACCAGATAGG',\n",
       " 'CCGCGCCAAGCTTCTGCGAGGG',\n",
       " 'CCACCCGTCGACGTCCCCTGGG',\n",
       " 'GCTGACCCTGTCGGACCGCTGG',\n",
       " 'CGCTGGATCGCGCCAGAGCTGG',\n",
       " 'TCGCAACAATTTCGTGCACAGG',\n",
       " 'GCAATTACGAAGAGAGCCGAGG',\n",
       " 'GTGGACGGCAACCGAATGCTGG',\n",
       " 'ACCCGGTACCACATGAAGATGG',\n",
       " 'CATGGGCGCGTTCCATGGGAGG',\n",
       " 'GCCGTGGGAACGGTGCGATGGG',\n",
       " 'ATATTCCCCTGCGGGAGTAAGG',\n",
       " 'ACCTTAGATCCCCCGACAAAGG',\n",
       " 'GTTCTATGCCCGCTTGAAAGGG',\n",
       " 'TGAGGACATGCGGTACGTCTGG',\n",
       " 'CGAGTACTTCGTTCCAACATGG',\n",
       " 'AGACGACAAATCTATGCAGTGG',\n",
       " 'GTGTTCTGACCAGGGCATAAGG',\n",
       " 'TTGTTTCTGAAGCGAAAGTGGG',\n",
       " 'ATGAATCCCCAGAGGGATCAGG',\n",
       " 'TTGTTACTTGGCAAAGTACTGG',\n",
       " 'CTGCCATACCGTATGTCCCTGG',\n",
       " 'ATTGCACGATACGAAGAACAGG',\n",
       " 'GCTGTAGTCCGAAAAATACAGG',\n",
       " 'GTCTTGAGACTCATCCGGATGG',\n",
       " 'CGGTCATATAATACACAAGAGG',\n",
       " 'GTCATGCGTACCCTTAAGAAGG',\n",
       " 'TAGACCCCTGCAATGTCCATGG',\n",
       " 'CTTCAATGACGGTGAATCCAGG',\n",
       " 'GATCTCAATAAGACCGAGGAGG',\n",
       " 'TGGCTGCGAGTCTACCAACAGG',\n",
       " 'CGGATGTCGTGCCCGTAGATGG',\n",
       " 'ATGTACGTGGCGATCCGAGAGG',\n",
       " 'GATCCAGACGAAGCCGTAGAGG',\n",
       " 'CGAGATCCGCCGCGCCTACTGG',\n",
       " 'TGACAACGCGGCCTGCGGCTGG',\n",
       " 'GTGGACGGGCATACATCAGCGG',\n",
       " 'GTCCACGTTGACGGGACTCAGG',\n",
       " 'AGTCATCCAGTTGTACCGAGGG',\n",
       " 'TGCGATGAACGGCGGGTACGGG',\n",
       " 'TCGGCATGGTACTCCATGATGG',\n",
       " 'AGATTGAGCGTCTCGAGTAAGG',\n",
       " 'CACGAATCGTCCACGAAGCAGG',\n",
       " 'CGAATGGACATAGACGTGGTGG',\n",
       " 'CGGATCTTATACTTCACGTGGG',\n",
       " 'CTCGGCCGATGGATAAACTAGG',\n",
       " 'GTTGTAATAAAGTAGCTCCAGG',\n",
       " 'CTGAACACTGTCACCCGATTGG',\n",
       " 'CGCAATGACTGCCATAAGAAGG',\n",
       " 'AAAATTACTCCTCGGGGAAAGG',\n",
       " 'TTGCTCGGCTTAGAGAGACTGG',\n",
       " 'CTTGTATATACTTTGCCACTGG',\n",
       " 'GGTCAAATCACGGCAATCCTGG',\n",
       " 'GAATCTTAGATTATCAAACTGG',\n",
       " 'CCCAAATTATAGTCATGACTGG',\n",
       " 'AAGTGGACTGTTATATCCCTGG',\n",
       " 'ATTTTGATGCGCACGTGGCCGG',\n",
       " 'ACGTAGCGCAGGTCGGTCAGGG',\n",
       " 'GACCGTGGACGAGCACGTCTGG',\n",
       " 'TCCGGCTACTACCTGACGCTGG',\n",
       " 'AAGCTGATCGCCACGCTGAGGG',\n",
       " 'TTGATAGCTATCCTGACCTAGG',\n",
       " 'AAACCAGTGATTACCCGCCAGG',\n",
       " 'AGCGCCTCCAGAATTCCAAGGG',\n",
       " 'AATCGCCAATGCCAGATAGAGG',\n",
       " 'TGAGATTTGAGCCGTCCGATGG',\n",
       " 'GAAGGTGTTCATATCCAATGGG',\n",
       " 'GAAGACGTGTATACAATGCTGG',\n",
       " 'GATCCCCCAAAAGACAATAAGG',\n",
       " 'AAACACAACCAAGCCCGTAAGG',\n",
       " 'CACAAATTGTCGTCCTGACTGG',\n",
       " 'TCGTGGTGGCAAACAATACAGG',\n",
       " 'TGACAGCTATTCGAAGAGTGGG',\n",
       " 'TCTTAGCGTATGCAAAAGCTGG',\n",
       " 'CTATAATGCGACAGGAGATAGG',\n",
       " 'GGTATGCCTATTATTACAGTGG',\n",
       " 'GTGAACGGCGATAGGGACCCGG',\n",
       " 'AATACGTTCCTCAGCTAGCTGG',\n",
       " 'GCCACCTTTGTTTTGAGCGTGG',\n",
       " 'TACAAGTTGGATCGAATCAAGG',\n",
       " 'TCTTGTGTTCGCCCAGCGTTGG',\n",
       " 'GGTCATGCGCTGAATGAAAAGG',\n",
       " 'CTGTCGCAGTACTTATCACAGG',\n",
       " 'AATGAGTAGCACGCCTGGCTGG',\n",
       " 'ATTTCGGACCGTAGACAGTCGG',\n",
       " 'CTTGACGTTAGCTCGAGAAGGG',\n",
       " 'AATACGCGGCTAACAGAGTAGG',\n",
       " 'CCTTGTCGCTGCTAAATCCAGG',\n",
       " 'TTAGTTTCGATACTCCGATTGG',\n",
       " 'TTGATATTGTTACTGATGGTGG',\n",
       " 'GATGTGACTGATGAAGAGATGG',\n",
       " 'AATGCGTATGATTTTATCATGG',\n",
       " 'AAATTTAATACATTGGTAGGGG',\n",
       " 'AGGAGCTCAAATGAGTGGAGGG',\n",
       " 'AAAGGTCGGACTACAATCGTGG',\n",
       " 'TATCTAAGGCTATTGACAGAGG',\n",
       " 'TAGGCTCGAGCGGTTCCCAAGG',\n",
       " 'TAGTCTCTGTTAGCGGCTCAGG',\n",
       " 'CACAGTTGCAGTCACACGGTGG',\n",
       " 'TGGTAACCCTTGGAACACTTGG',\n",
       " 'GTACACAAGGGACCACGTAGGG',\n",
       " 'AGCCATGCGCTCGCCAACGTGG',\n",
       " 'TCTTTGACGCCAATAAGACAGG',\n",
       " 'GCGTGTCGACAGCATGGACAGG',\n",
       " 'TCCCGTTGCTCCATGGCGAAGG',\n",
       " 'CGAAGGCGAATGTTCAGTCTGG',\n",
       " 'CAATTAGCTCATTTGCCGCAGG',\n",
       " 'TCCTACACCAAGCCCGACGTGG',\n",
       " 'ATGTACGTCCACACGAACAGGG',\n",
       " 'CCCACGAAGAGGCACACGAGGG',\n",
       " 'TATTACAGGTCCGTGATACAGG',\n",
       " 'CCAGAAGGTGATCCTCGACAGG',\n",
       " 'TAGCCCCGGACAATCAACCTGG',\n",
       " 'ATCGTCAAGTCCCCACAGAAGG',\n",
       " 'ACAATCTCCCCGACCGTGGAGG',\n",
       " 'GTCACGCTTTTCCTATGCCTGG',\n",
       " 'GACGGTGCTATCTGGCACTTGG',\n",
       " 'GTAACACTTCAGGCACGGGGGG',\n",
       " 'AACCGGTAGGGCAGCCCACAGG',\n",
       " 'GCACGCTGTCCTCCGCTAAGGG',\n",
       " 'ATCTGACGGTCGGAATAGACGG',\n",
       " 'GATCGTCAGCGGGAACATCAGG',\n",
       " 'TGTCCCGGGATCGTCAATGGGG',\n",
       " 'CAGATGACCAGCGATGCGCAGG',\n",
       " 'CGTTGGTTGATGATCCAACTGG',\n",
       " 'CGCAAACTCACCGCACCCGTGG',\n",
       " 'ACGGCCATCCGGTTGAAGGTGG',\n",
       " 'AGTCATATGTCGACAATGGGGG',\n",
       " 'GGTGAAAGGCTACCGGCAAAGG',\n",
       " 'AGGTTAGCACCCAACCCGGTGG',\n",
       " 'GCAACTCACTCTCGGACTGTGG',\n",
       " 'GATTGGTATATCGAACAGCAGG',\n",
       " 'TATCGAGAGAATCCAGAATAGG',\n",
       " 'GAGTAGCATTACCTACAGCTGG',\n",
       " 'AGACATCCTCGAGTGTCAGAGG',\n",
       " 'CGACCCACAGGTAGATGCAGGG',\n",
       " 'GCCACGACAATGGTGCCGCAGG',\n",
       " 'ATACAGTATGAGCGGCTGCAGG',\n",
       " 'GGATGCCGGTAGCCATAGATGG',\n",
       " 'AATGCCTCCGGCGAGGACGAGG',\n",
       " 'GCATCCGAAGAATCCAGGTTGG',\n",
       " 'CGTCACTGCCACGAACACGCGG',\n",
       " 'TGCAACTTTCACGGATGCCAGG',\n",
       " 'GCACTTCGTCTTAGTAACATGG',\n",
       " 'TGCCATATGATTTATCGGAAGG',\n",
       " 'CCCGTGTGGCCCACAAGAAGGG',\n",
       " 'AAGCCCAGCATTGTCCACTGGG',\n",
       " 'ACTTGGAAGTAGTCCGGATGGG',\n",
       " 'ATTCTGAGCTGAGAATGCATGG',\n",
       " 'AAGTTCAGGAGAACTCGACCGG',\n",
       " 'GGCTGTTGTCGGTCCAGTGGGG',\n",
       " 'CGGGTGTCCTTTGACCGTCTGG',\n",
       " 'ACCCCCCAGGTCGCACTGGTGG',\n",
       " 'CTGCCTCTCGTCTGGATCGTGG',\n",
       " 'TCTTGCCGTAGGAAGGGCTCGG',\n",
       " 'CCGGAGGTAGCCCCGGCCATGG',\n",
       " 'TCCTCTTCATCCACCACCATGG',\n",
       " 'GAGGAGGTAGATGGGACCAAGG',\n",
       " 'GGTACCCCCCATGTACCTCTGG',\n",
       " 'TGTGCTTCCTGAGAACAGCAGG',\n",
       " 'TCACCATTCGTATCCCCCGAGG',\n",
       " 'ACGGTAGATCGGACCACACTGG',\n",
       " 'GACTCCGAGTATGTAGTAGAGG',\n",
       " 'CCGATGGCTCGCAAGTCGATGG',\n",
       " 'CGTAGCTGCGAGAAGCCGATGG',\n",
       " 'GTTAGACGTAGAGAGCCTAAGG',\n",
       " 'GATCAATGTCATTCGAGTCAGG',\n",
       " 'CAACGAATCCCATCACGGCTGG',\n",
       " 'TCCGGGACATAACCTGAGATGG',\n",
       " 'GCGGGCCACATACACCGACAGG',\n",
       " 'ATCGTCCGCAAGGACCCGCGGG',\n",
       " 'CAGACGGCTGCGGAACGACAGG',\n",
       " 'TACTACCGGGTCAGCAACATGG',\n",
       " 'CGCTACATGCACTCGCGTGTGG',\n",
       " 'GCTTTTAACACTTTAGCAGTGG',\n",
       " 'CTAGTAGGGTGGGCCCAATTGG',\n",
       " 'AGTGATCAATATGGATGGGAGG',\n",
       " 'ATTTGATGATGAAAGTCCGAGG',\n",
       " 'GCAAGCGTTTAAAGCAATCTGG',\n",
       " 'CCTCATTGTTCTGTAATGGTGG',\n",
       " 'TGGTATCATTGGTCGTAGCAGG',\n",
       " 'ATTAAACTTACAAGAGGCATGG',\n",
       " 'TAAACTGTGCTTCCGAGTAAGG',\n",
       " 'CTGAAGATACTCCTCATAGAGG',\n",
       " 'TAGTAGACGAGGGTGAACGGGG',\n",
       " 'GATGACATCGATAACCCGTAGG',\n",
       " 'AGGACCCCATAGTACTGACTGG',\n",
       " 'TCACTCACCCAGTAGGGTCAGG',\n",
       " 'CCAGGAAAATTATGCATAGAGG',\n",
       " 'ACAATTGATAAGGCGCCAAAGG',\n",
       " 'GGTTGCCTATCCCTCGTCCAGG',\n",
       " 'AGCATCTCCGCTCATGGCAAGG',\n",
       " 'TTGTAGCCGGCCGCCGCTACGG',\n",
       " 'GCCAAAGCACGGCGGATCCTGG',\n",
       " 'GAGAAGAATCGGATCAATAAGG',\n",
       " 'CGGGAGGACATCTCCGCCTGGG',\n",
       " 'GGACCTAGATGCTTGCGTGTGG',\n",
       " 'TCGCGGATCTTGCATGGACTGG',\n",
       " 'AGCTTCTCACACTCCGCTGTGG',\n",
       " 'GGGCATCTCTCGAGTCAGATGG',\n",
       " 'ACCAATGAGGCCATAACGACGG',\n",
       " 'TTTGCCTTAAGTCGAGCCTCGG',\n",
       " 'GTCTTTTGGCGATAGGTGAGGG',\n",
       " 'AGCGGATGTGAACCTGGCATGG',\n",
       " 'CGAGAGGATTTGCTACGGAGGG',\n",
       " 'GGGCATCAGTCATTCTCGCTGG',\n",
       " 'CCCGGAAATTCCTTTCAGGAGG',\n",
       " 'TTGCGCCAACACGCGGACCGGG',\n",
       " 'GGTGGTCAGCGCAAGCGCCTGG',\n",
       " 'ATGAAAGGGCTCGCTCAAGGGG',\n",
       " 'GCTCGAAGAGTTTGGCGCTGGG',\n",
       " 'TCTTGGATGAGCCTACAACTGG',\n",
       " 'GTCATTCAAGAGTTAGGTCTGG',\n",
       " 'ATTAGATGTCTTAGCTGCAAGG',\n",
       " 'ACAGTGGGATCATGAAACCTGG',\n",
       " 'AACTTAGGATGTCTAAGCAGGG',\n",
       " 'GGCCCCTCCCGCACGGAATAGG',\n",
       " 'TACCCCTTTTGCGCCAGCAGGG',\n",
       " 'TCAGGTAAATTCTGCCGCCGGG',\n",
       " 'CGTGTGGGAGCACGACATCAGG',\n",
       " 'GCCGAGTCCCTTTAGATAGGGG',\n",
       " 'AAAGATGTCTCCTTGTACGTGG',\n",
       " 'CCTTTAAAGCCACCGCGTGAGG',\n",
       " 'TTAGTGGACCTGACGTCAGTGG',\n",
       " 'ATTAGCAGTCATGCAGTCCAGG',\n",
       " 'AATCAGTCGGTCTGCCACATGG',\n",
       " 'GTCTCTCGCACAGTCAAGTTGG',\n",
       " 'GTTACCCTTTTGTCACGCTGGG',\n",
       " 'GGGCAACATGTACGTGCGGGGG',\n",
       " 'GGTGCCAGACGTCATCAGGAGG',\n",
       " 'GGCCGCCCCTAAGTAGATGGGG',\n",
       " 'GACTTTCTACCCAACGCTGTGG',\n",
       " 'GCCCCTAGTCCTTTATCAGAGG',\n",
       " 'TGAGGGATGGCTACCAGTAAGG',\n",
       " 'CTGTCGTGTTTAACAACCGGGG',\n",
       " 'GGTTTGCCCTCAGACCCACAGG',\n",
       " 'GATACCTCAGCGGGCGCCACGG',\n",
       " 'TTTTAGCTTCTTATAAGCCAGG',\n",
       " 'CATATAAGAAAGATAGCCAGGG',\n",
       " 'ATGAATGGTACAAAAGCGTTGG',\n",
       " 'TCTAGGTCACGCCTGCATAAGG',\n",
       " 'ATGCTGCTGGCACTACAGAGGG',\n",
       " 'TGTGGCCAACGACGACGCAGGG',\n",
       " 'AGCTACGAGATCATGAGCCAGG',\n",
       " 'CGTGCAAAAAGACGACGGCCGG',\n",
       " 'CACGGCCCTAGCTTCGCGAGGG',\n",
       " 'GACATAGTTTGATGAGAAATGG',\n",
       " 'TTGCAGGCGAAAGGGCGTGTGG',\n",
       " 'GCCCGCTTCATTCCCGCGTCGG',\n",
       " 'AAGCAGAACCTACGCCTGACGG',\n",
       " 'GGTACCTGCGTTCCCATGCAGG',\n",
       " 'TTATTTTGATACGATACACTGG',\n",
       " 'AAATTATAGTTGGGGAATAGGG',\n",
       " 'ATGGGAATGCAGGCAACATAGG',\n",
       " 'TGGTTGATTATCGAGGATATGG',\n",
       " 'ATAAAGGAAGGTAACGCATCGG',\n",
       " 'TGATTTTGTCAGTGCAGATGGG',\n",
       " 'GGCACAAAGCCCGGGAGCTGGG',\n",
       " 'ATGGACTGATCACAACCGGGGG',\n",
       " 'CCTGCCCCTATTGGGGAGCTGG',\n",
       " 'GGCGCGCACGTCATCCCAGCGG',\n",
       " 'TACGCGCTGGCCCACTCGCTGG',\n",
       " 'GCCTCGTGGAGCGCTACCACGG',\n",
       " 'ACGCGGGCTTCTACGAGATGGG',\n",
       " 'GGCGTTGGCGTCGTGCTGAGGG',\n",
       " 'TCGGCCTGTCCCAGGTACACGG',\n",
       " 'CAGGAGACGCGGTTGCCGCGGG',\n",
       " 'GAAGTCGGCACGCTCCGTCAGG',\n",
       " 'TCTGTGGGTTTCCCGGAACTGG',\n",
       " 'CATATGATTATTCTGGATATGG',\n",
       " 'TTACATAGGACTAGGATCACGG',\n",
       " 'AGTAAAGTGTATTTCGCATTGG',\n",
       " 'TCATGACTAGAACCAGTAAAGG',\n",
       " 'AGCACCGTGTAGGTGGGCTCGG',\n",
       " 'CCTCAGCGAGCGCGCCGACTGG',\n",
       " 'TCGGGACTCACGCCATACCTGG',\n",
       " 'TCGAGGCCAAGTCTACCGTGGG',\n",
       " 'GCTGCGCATTCATACCTCGAGG',\n",
       " 'AAGAGGTCGAATGTAGAAGTGG',\n",
       " 'TTGACTACGCCCAGAAAAATGG',\n",
       " 'TGCGCCGTGCTGAACCACCTGG',\n",
       " 'GGTCAATTCAATGTTGGGCAGG',\n",
       " 'TACCATAGGTGAACATGCGTGG',\n",
       " 'ACTTCCTGTACTGCACCGGGGG',\n",
       " 'AGTGATGAAAGGTCTAAGCAGG',\n",
       " 'AACGTACTACCCGACGGTCTGG',\n",
       " 'TTCTGTAACCACGGGACAGTGG',\n",
       " 'AACCCCAGTTAGTGACCGGGGG',\n",
       " 'CAAAACGACCGCACCCCCTTGG',\n",
       " 'CCGAAGCCAAGCAGATCGAAGG',\n",
       " 'CATTCCCAAGGGACCCGGAGGG',\n",
       " 'GACATCGATAGAGACATGGCGG',\n",
       " 'TCGGAGGGGAAAGCCCCATGGG',\n",
       " 'AAGACCTCCTCCAAAACCATGG',\n",
       " 'TGGACGAAGTAGTAGACCCAGG',\n",
       " 'CTGCATCACTGTCAAACCTGGG',\n",
       " 'AAGAGTGGAGATGTGCCCTAGG',\n",
       " 'TGCTTGGGCACAACCTAGGTGG',\n",
       " 'AGGTACTGGCGGAGGACATTGG',\n",
       " 'AGAGAATCCGTGGAGCATGAGG',\n",
       " 'AACCTGCACTTGGTCTGCGTGG',\n",
       " 'TGGATGACCTGTCCATAGATGG',\n",
       " 'CAGGCTGGAGACATCCGATGGG',\n",
       " 'CTTCCAAGCCTAGCAGGACTGG',\n",
       " 'TTCCCCGGCCTGACATGGGAGG',\n",
       " 'TCCATCAATCATATCTCACAGG',\n",
       " 'AGCAACACTAGCTAGAGATTGG',\n",
       " 'GTAGTCTGCCACCCGAGTCAGG',\n",
       " 'CGAACTGGTCGTTCAAGGTTGG',\n",
       " 'CCTGACATAGGGGGACTAGGGG',\n",
       " 'TATGGAGAAGGTGGCCCGAAGG',\n",
       " 'CGATGACCTTCTGGCCGGGGGG',\n",
       " 'CTGCAGTAGGGCGTGAGAGGGG',\n",
       " 'TTGGCTGCCTGGACGACATTGG',\n",
       " 'CTGTCTCGAAAGAGCATCAAGG',\n",
       " 'TCTTGGCGGTTTGGGCCGGGGG',\n",
       " 'GGCGTCGTTTAGGTGTAGAAGG',\n",
       " 'TCTAATGCACTAGACGTAGTGG',\n",
       " 'CGAGAACGAGTTTTACCTGTGG',\n",
       " 'TGGACGCAAGAACTTCAAGAGG',\n",
       " 'GCCGCTCGTTGGAACTCCAAGG',\n",
       " 'CATCTCGCTGAGATACGAAGGG',\n",
       " 'ATCATTCAACGGTGGCCGACGG',\n",
       " 'GTCCCCCAACTACGACAAGTGG',\n",
       " 'AGGTCAATTCCCGGGTAAGGGG',\n",
       " 'TCCCCCATCCACGACAAATGGG',\n",
       " 'TCCATCTCGCTCAGGTACGAGG',\n",
       " 'GTTCGCTCTAAGAATGGGCAGG',\n",
       " 'GATAGCCTCATTTAGTGCCTGG',\n",
       " 'AAGCTTTGCATCGTCCCTATGG',\n",
       " 'TCCATCGGTCCACGAGCCAGGG',\n",
       " 'CATCCGATCCCGAACATCCTGG',\n",
       " 'TGCTAAATCCTTGTAATCCAGG',\n",
       " 'TTACATAGATAGTATGACCTGG',\n",
       " 'GTTTATTTTACAGCCTACCAGG',\n",
       " 'TGAGTAGGAGATCATGTCGGGG',\n",
       " 'GGGCGGTCGATGTCATAGATGG',\n",
       " 'GCAGCCCTTCCTAAAAGTAAGG',\n",
       " 'ATCCTGGACTACAGGGACTTGG',\n",
       " 'TTACATAAATCACACGGCTCGG',\n",
       " 'CTCGTAGTCCGAGGACAGTAGG',\n",
       " 'ACACTCACGTGTAGGACTAGGG',\n",
       " 'CTCCGAGTGACCCGGTCAATGG',\n",
       " 'GATAACTATAAAGTCGCTCTGG',\n",
       " 'CGCTTTGTACACACCGAGCTGG',\n",
       " 'GACTCATCGATGTACGGCATGG',\n",
       " 'ATCTACAGGATATCGGGCGTGG',\n",
       " 'ATCTTCACACCGAAGACGCCGG',\n",
       " 'CCCCGTCCAAAAAGCAGACCGG',\n",
       " 'TTCAGGCTCATATCTCGGCTGG',\n",
       " 'CGGACGTGATTGAGATGAACGG',\n",
       " 'CACATTTTCTCCTCCGCGTTGG',\n",
       " 'AGCATCCCTCTCGTACCTGTGG',\n",
       " 'AAAGAGGTGTCCAAAACGGTGG',\n",
       " 'TTGATGTGAGAAACCTCAGGGG',\n",
       " 'AATGAGAACAGCATCAGGCAGG',\n",
       " 'GAAAGTTAAGCGTGAAATTTGG',\n",
       " 'GTTGGCACATTTATCATCACGG',\n",
       " 'TCCTACCAATGCTTCAAAGAGG',\n",
       " 'TCTTAAAGCTGCAAAACGAAGG',\n",
       " 'AGAAGGTTGCGGACGTGCAGGG',\n",
       " 'ACGCGTCCGACCTCGCCATAGG',\n",
       " 'CTTCTTGCGTCTCACGAACCGG',\n",
       " 'AAGGACTACACCGAGGGATGGG',\n",
       " 'ATAGGCGTGTTGTGTAGACTGG',\n",
       " 'GCACCTTCACACACGTGCCTGG',\n",
       " 'GTTGAAGCCGTCAGGACAAGGG',\n",
       " 'GACTACTTCCGAGCCCTGCTGG',\n",
       " 'GTCGGGTGAGATGCCATGCAGG',\n",
       " 'GCACGTGAGTGAAGACGTCGGG',\n",
       " 'CGTGGACAGCGGCGACATCCGG',\n",
       " 'ACGCGCATCCACTCCATGAGGG',\n",
       " 'GCTCGCTCGTCATGGCCCAGGG',\n",
       " 'GCGTACTCGTGGATGCGCACGG',\n",
       " 'AGCGCCTGCGAGTTGGACTTGG',\n",
       " 'TGCCGAGAGAAGCTCGTCGGGG',\n",
       " 'CTTGGTGCCCTTGTCATCATGG',\n",
       " 'GGCATTTCACGGGAGAAGCAGG',\n",
       " 'GAAATGGGTCTTATGACATTGG',\n",
       " 'CAATAGACAGTGTTCGTCGGGG',\n",
       " 'CATCTCTGCTGTGACTGCAGGG',\n",
       " 'TTGATATCTGATCCAAGCTTGG',\n",
       " 'AGCCCTTCGAACATACACCTGG',\n",
       " 'CGAAGTGCTACTTTGGCATTGG',\n",
       " 'TCCATTACGTCAAGCGACCTGG',\n",
       " 'CTAGATATCGCATCACAATTGG',\n",
       " 'GTTAGAGACGCTATTCCGCAGG',\n",
       " 'GATGACTACTCCTCCGACGAGG',\n",
       " 'TCTCGTGAGTCTACCCGGAAGG',\n",
       " 'GGCGACTTTAGGACTACCGTGG',\n",
       " 'CGTGGAGCCACATACCGGGTGG',\n",
       " 'AGCGTCTGGGCGACAACGGTGG',\n",
       " 'GAGCGGTGATTTTCGACATGGG',\n",
       " 'CCAACTTACTACATCAGGCTGG',\n",
       " 'GATCAAGAACGTTAGGGACAGG',\n",
       " 'TATATGGCTCGTCTGTGGCTGG',\n",
       " 'TCCACGCCAGGTACGAACCTGG',\n",
       " 'AGATCCTTACGGTCGATGCAGG',\n",
       " 'AGCCCAGCTAGGCTTCGGAGGG',\n",
       " 'CCGAAGCTATCAATCATCCAGG',\n",
       " 'CTCCATGGTACAGAGCGGTGGG',\n",
       " 'TGGTCATGTGCCGAACAGGAGG',\n",
       " 'CTTTGGCGAAAGCTCGTACAGG',\n",
       " 'AATCAGTTCTTGGGACCCGTGG',\n",
       " 'GACTCCCGAAAAATTGACCAGG',\n",
       " 'TTGCCTAAACTGGCGTCCGGGG',\n",
       " 'GAGCGATGCAGCCTCAATCCGG',\n",
       " 'CCCCCTAATTAGAAGAGCCTGG',\n",
       " 'CTTATTGTGTAACAGAACCTGG',\n",
       " 'GAGCAGGCTCTGATGTAGCTGG',\n",
       " 'AGATGTGGATAACCAACGGAGG',\n",
       " 'AGCGATGTTCAGATACTAGAGG',\n",
       " 'TATCCACCTGGGCTGCAATGGG',\n",
       " 'GGCGTAGGCCAGGTAATCGAGG',\n",
       " 'ACACTCATGATGACTCCGGTGG',\n",
       " 'AGAACCCATGAGTCGCCCTCGG',\n",
       " 'GCCGTGGTCTTTGCCAGCACGG',\n",
       " 'GCACCTTTGGTTTCAACCATGG',\n",
       " 'TACAAGGATTATTTCAACAAGG',\n",
       " 'TCCACTGTGCTCGTGATAGAGG',\n",
       " 'TAGTAGATCGTGATACTCCGGG',\n",
       " 'TTTTCGAATGTTAACGGGCAGG',\n",
       " 'CCCGGCGGCCCTATGCCGGGGG',\n",
       " 'TTCCCTTACCGGACGGGTATGG',\n",
       " 'CCTCGAAGAAACGGGACACAGG',\n",
       " 'CTCCACCAAACGGGCGTACTGG',\n",
       " 'GTCTAACCGAGCCCTCAAGCGG',\n",
       " 'GATGTGTACTGCTTCGCCGAGG',\n",
       " 'GGCGTCGTAGCGGGATGAGGGG',\n",
       " 'AATGACCGCCCCCGTGCGCAGG',\n",
       " 'ATGGCTTCACCTGACGGTCTGG',\n",
       " 'GGTAGACCGTTCTCACGCCTGG',\n",
       " 'AATGCCGACAACGAAGGCGCGG',\n",
       " 'CCTCCGCATACCGCCATCATGG',\n",
       " 'CTCGCCGGGATTTCTGGCGGGG',\n",
       " 'CTTTGAGGACGGCTCGAGCTGG',\n",
       " 'ATAATCCAGTGCCCGTCCCCGG',\n",
       " 'CTATAAGCAGTAGCAATACTGG',\n",
       " 'GATTCCGAAAAGCTGCGCCAGG',\n",
       " 'CTATAGGATAATCCGACTGTGG',\n",
       " 'ATTACTAGAATACTGAGCCAGG',\n",
       " 'GTATTGCAATGATTGATACTGG',\n",
       " 'CTGTCAGGGCTCTCGCGGTAGG',\n",
       " 'TTGACGTGGACGCGCCCAGTGG',\n",
       " 'AGGTTCGCTGACAGCCTACAGG',\n",
       " 'CCACGCTCCCGAGTGTCGGTGG',\n",
       " 'GTCGATGCTGCTCGTGGACGGG',\n",
       " 'CTGCCTAAAAAAGATCCAATGG',\n",
       " 'TGGTACATGGAGTAGAAATAGG',\n",
       " 'AGTCTTATGTGTGGACATCAGG',\n",
       " 'TTCATTACATATGGAACATTGG',\n",
       " 'AATATTGCACGAAATGAACAGG',\n",
       " 'CAGTCCAGTCAATAGGGATAGG',\n",
       " 'CATATGGGTATTACAGGTAAGG',\n",
       " 'CAAGTGAGTAGAGAAGATCAGG',\n",
       " 'TACAATCTCTTTGTCAAAATGG',\n",
       " 'TTCTAGTTGACACCAAAACTGG',\n",
       " 'GAGGAAAGGCTTCGACGGGAGG',\n",
       " 'GCGTCCCACAAAATAGAGAAGG',\n",
       " 'ATGTCTAAAGAGGATGCCATGG',\n",
       " 'CAGACACTTGTCCTGAGGTTGG',\n",
       " 'TCGCGGCCCGGGCGCCTCAGGG',\n",
       " 'CGCAGCATCTCTTCATAGGAGG',\n",
       " 'ACTACAAGCAGGCCACCATGGG',\n",
       " 'CCACTTATATCGTCCAATGGGG',\n",
       " 'GTGATCGACACAGTGCCCCTGG',\n",
       " 'TACTTCGAGCCCCTGTACCAGG',\n",
       " 'TTTTATAGCTTCTATAAGCAGG',\n",
       " 'ACAGCTTTACGTCCAACAATGG',\n",
       " 'AAGCCCATTGATGAAAACTTGG',\n",
       " 'ATGCCAGTTCTTCCCTGAATGG',\n",
       " 'GGAGTTCTGATATAACCTCAGG',\n",
       " 'TAAAAAACTAGATCCAGGTTGG',\n",
       " 'TCCATATTGTAGGGAAGCTTGG',\n",
       " 'CTGTATGCCAGGTACAAACAGG',\n",
       " 'CTGATTCAGGTGGCCAGCAGGG',\n",
       " 'CCCCATAGCCCTGAGATCGAGG',\n",
       " 'TTAAAAGGCAAAGCCAAATGGG',\n",
       " 'CAGGAATGCTAGATTTAAAAGG',\n",
       " 'TTCTTTTGCCAGCGTGTCCAGG',\n",
       " 'TTTACAAACAAGCAATAGTTGG',\n",
       " 'GAGAACTGAAAGAACTCTATGG',\n",
       " 'AGTAGCCATGGGGAAGATCTGG',\n",
       " 'CTCATCATACTCATCCATGTGG',\n",
       " 'AGTCAGCGCGACATGCAGAGGG',\n",
       " 'GTGATAGCGCCATAGTAAGGGG',\n",
       " 'GGGAGTATACATCACCCAGAGG',\n",
       " 'ATAACGCTGCACTTGCAGCAGG',\n",
       " 'CATATTAAGCGACTCAGAAGGG',\n",
       " 'ACCGCGACCTATCCATCCGTGG',\n",
       " 'TTGGTAGGCATTGTAGTCCTGG',\n",
       " 'AGCCCACTGACTTACCGCCTGG',\n",
       " 'GTCCGATGCCTGGTGACGCGGG',\n",
       " 'CCCTGATACGTCCGACGTCGGG',\n",
       " 'CGGGCCCGTTGCATCCGCTGGG',\n",
       " 'ACGCCCTCGTGCGCAGGCGCGG',\n",
       " 'TCGGCTTCCGCGGGACAGAGGG',\n",
       " 'ATTCCATGCTAACGGACCCAGG',\n",
       " 'ACTGATGCAATGGTGGACCAGG',\n",
       " 'CTTCCTATATCAGTCCAATTGG',\n",
       " 'ATCTTCAATCAACTGGCCGCGG',\n",
       " 'TGAGCACCATCTACAGTACTGG',\n",
       " 'GGCGGATGAACTGGGACCTGGG',\n",
       " 'TATGCTATAGCCACTGCCCAGG',\n",
       " 'AGCGTCATGTGGAAATACATGG',\n",
       " 'CCTACCTATGATCATGAAGAGG',\n",
       " 'CAGACAACGTAAATGTAGCGGG',\n",
       " 'CGAGTTCTACAACACGGTGCGG',\n",
       " 'ATCTGACCCAAGAAACTAAGGG',\n",
       " 'AGGGGTAGGTTCAAGGTGGTGG',\n",
       " 'GCACGTCGTAACCGCAGACAGG',\n",
       " 'GCTCATCGCAGAGCTAAAGAGG',\n",
       " 'TCGGCGATGTACCAGGTCACGG',\n",
       " 'TCTGAACTGCACCGAACATTGG',\n",
       " 'CAGAGTGTTAGAGACGGTCTGG',\n",
       " 'ATTAAACTGAATAGAACTAAGG',\n",
       " 'AAAGAGCCGATATTCCATCAGG',\n",
       " 'GATGTCGGGTCACCCCCGAAGG',\n",
       " 'CCTCCTTGGACGTGTACGATGG',\n",
       " 'TGTCCACACGTTGAGGTACAGG',\n",
       " 'TCCGGTGGCAGAAAGCGACGGG',\n",
       " 'ATTCGCCTGAAGACCCCCGGGG',\n",
       " 'TTCAACACTGCCCTCCGGGCGG',\n",
       " 'GTCAGCAATGATGACCGCCCGG',\n",
       " 'ATATGCCGGACAGTCACTCAGG',\n",
       " 'CAGAGCGTAATGGCGATGATGG',\n",
       " 'CCTGGTCGGAAACGACGCTGGG',\n",
       " 'GAAGGAGAGCGTGTAGAGCAGG',\n",
       " 'GGCATGATTGCCAACTCCGTGG',\n",
       " 'CAGTGCGTGTCATAGCCTGTGG',\n",
       " 'TGCACGTGAGCTCGCCCATGGG',\n",
       " 'TTGTTGGACGCAGACGTGACGG',\n",
       " 'GCATAAATTGCCACTACCATGG',\n",
       " 'CTGCATTAACAGCCCAAAAAGG',\n",
       " 'CTAAAGTCCCCAGCCAATCAGG',\n",
       " 'TACTGTATAAAAAACCAGCTGG',\n",
       " 'TTGATGTTCCTAGGTAGCGGGG',\n",
       " 'CCTACCGATGTGCTCCCGCTGG',\n",
       " 'CTAGGGGGTGTCAACGAGCTGG',\n",
       " 'GGATATGCCTCCCGCCCGAAGG',\n",
       " 'ACGTAGTCCCCTTCTCGGGTGG',\n",
       " 'ACCAGCTGATCAAACGTCGTGG',\n",
       " 'CTAAACATGACAGGAACTGTGG',\n",
       " 'GGGTAGCTCACAACGGTGCTGG',\n",
       " 'GCTTTCCCGGGGTCCAAATTGG',\n",
       " 'GGCACCCACGTCAACGAGTGGG',\n",
       " 'GAGTGGGACCTGAACGCGCAGG',\n",
       " 'AACTCATCACAATTCCGAATGG',\n",
       " 'GGCTCACGTAAAGTCCTGCAGG',\n",
       " 'ACATGCGGATTATTCCCCCTGG',\n",
       " 'ATCGTAGCTCGGTCAGCAATGG',\n",
       " 'GAAGTTAGTATCACGTACCTGG',\n",
       " 'ACATTGTTCGCAAACGGTAAGG',\n",
       " 'GCGAGGCAAGTCGTACCTGCGG',\n",
       " 'GACGGGCTCTCTCTCCGGTTGG',\n",
       " 'GTGCAATCGTGGAATACCACGG',\n",
       " 'CGTGTTCCCTTACAACCACAGG',\n",
       " 'GCGCCCTTCTCGTCGCGCAGGG',\n",
       " 'GCTACCGCGCCGACACCCTTGG',\n",
       " 'GTAGCGCTCGTGCCGCACCCGG',\n",
       " 'GAGTATCGGGCTAGTCTGCTGG',\n",
       " 'TATCCCTCGCCGTCTGCCATGG',\n",
       " 'ACGGCTTGCCTGTCCGGTAAGG',\n",
       " 'TTGAAGGCCCGGTTCACCTTGG',\n",
       " 'TTCAATGGCCTTCAGCGTAGGG',\n",
       " 'ACCAGACGGTCGCCGACCTGGG',\n",
       " 'GTTGAAGTTGGAGTTCGCGTGG',\n",
       " 'TCCGGGGACCATCTACAGTTGG',\n",
       " 'ATATCTACGGACTTCAGAAAGG',\n",
       " 'CGTGATGGTTTGCATGGGGTGG',\n",
       " 'ACCAGTTCAATGCTCTGAACGG',\n",
       " 'TGGAGAGTGCCTATTGCATTGG',\n",
       " 'CGTGGCTGTCAAACCGCCGTGG',\n",
       " 'GTAGATAACATATCAACAATGG',\n",
       " 'CTCTGCTATGCACGGAAAGGGG',\n",
       " 'TTCATATCGACACTGACTCCGG',\n",
       " 'GTACAGCCGGGACGCTCGAAGG',\n",
       " 'AAGAGTTCGGCGCAGTTCCTGG',\n",
       " 'CGCGCACGGCGATTCGCACCGG',\n",
       " 'GTAGCGCTCGTGCCGCGTCTGG',\n",
       " 'CGGCGCCAGTCGGTGCGAGCGG',\n",
       " 'TTCTTTTCTTCCAGGTAAAAGG',\n",
       " 'GATCCATTGATGGAAACTGTGG',\n",
       " 'CTACTGTGCTTATAGTTGATGG',\n",
       " 'CGTGGACATTGTGGATATAAGG',\n",
       " 'TTCTCGCAGTATTCCCGGCAGG',\n",
       " 'AAGGGCACCTTACCACAACAGG',\n",
       " 'GGTGCGCTCGACACGAGCCAGG',\n",
       " 'CGGCCACACAGCGCTCCTGTGG',\n",
       " 'CATTGCAACAGCCAGAACGGGG',\n",
       " 'CCTTAAATACTGGTCAATGAGG',\n",
       " 'CTGTGAAGGCCGTGCAACATGG',\n",
       " 'AAGTGGAGCGGACACGAACAGG',\n",
       " 'CCGAACAAAGTAGCAGTGCAGG',\n",
       " 'GGTGGTCAGATCGTGGGCCAGG',\n",
       " 'AGTAACGACGTGGAGCTGAAGG',\n",
       " 'AAGAGGAGCTCTTTAGACAAGG',\n",
       " 'TGAGTGGATTTACAAATGCCGG',\n",
       " 'GGGCTGTAACTATCGATAAAGG',\n",
       " 'TGTAACAAATAAGAACTGCAGG',\n",
       " 'TGTGGTTTCCAAGCCTCGAAGG',\n",
       " 'CGCAGCCAGCGTTATGAGGTGG',\n",
       " 'GCGCTGTATCGGATGGCAATGG',\n",
       " 'GAATTACCGTTGGTGACATCGG',\n",
       " 'TCACGAATAGGTACGATAAAGG',\n",
       " 'CGGTTGATAGCTCGGCGCCTGG',\n",
       " 'TTATAAGGCTGCCATGCGGAGG',\n",
       " 'CGGATGACCGAGTAGCGCATGG',\n",
       " 'GCTGTACCGAGTTTGACGTAGG',\n",
       " 'GAACCATGTGCGGGTCCCCAGG',\n",
       " 'AAACTCGGGCAGAACGGTCTGG',\n",
       " 'GGCCTTGGTATTACTGCCGTGG',\n",
       " 'GAACTTCCTTCGATGCAAGCGG',\n",
       " 'CGCACGCCTCACCTTAAAGCGG',\n",
       " 'GTCCCTCCCGTAGGACGTCAGG',\n",
       " 'GATGTTTGCAATGACCGAGAGG',\n",
       " 'GACACGCCGTGTGAAAATGCGG',\n",
       " 'GCTGCATAATTCCCGTACATGG',\n",
       " 'GACAAGGTTCGGATACCCAGGG',\n",
       " 'GCGAATGGTACTGTCCATCTGG',\n",
       " 'TTCTCTGAGATGTTTTGATCGG',\n",
       " 'CAACTTCCGGGTATGAGATAGG',\n",
       " 'GAACCCCCCTGACTACCGAGGG',\n",
       " 'CTTCATGCAGCTCTGCCCTCGG',\n",
       " 'TCCCATGAGCCACGTTGCCCGG',\n",
       " 'TCCAAGGACGGTACCTGCCGGG',\n",
       " 'TTTGATTATATACTATGTATGG',\n",
       " 'TATGGATGAAAGCAATCTGAGG',\n",
       " 'CTGTTTCTGACTGGAACGTGGG',\n",
       " 'TGAGCTGCCTAAGAAATCATGG',\n",
       " 'GTCTACCTGTCTTGCTTTATGG',\n",
       " 'ACCTCTTGCCGGTGATAAGAGG',\n",
       " 'CCGTGATAGCGCTGCCGCAGGG',\n",
       " 'GGTTAACTGACCAAACCCCTGG',\n",
       " 'CCAGTGAAGACATATCCCAAGG',\n",
       " 'CACTGGTGAACGGTCTCCATGG',\n",
       " 'GATCTTAGAGTATGCAATCTGG',\n",
       " 'TTTCCGGCTAGCACGTACCAGG',\n",
       " 'CTCCCTTCGCAAAGTGCCCTGG',\n",
       " 'TCAGAGAATACGTCCTCAAAGG',\n",
       " 'TTCCCGGGCCGTGTGGAATGGG',\n",
       " 'ACACAGTCACCAATCTAGCTGG',\n",
       " 'GGGTGGGACCTCTAATAGCTGG',\n",
       " 'TTTGTACTAACAGGTAGAGTGG',\n",
       " 'CGTGTTTCGACACGGGGCTCGG',\n",
       " 'TCGACCGGACACTGGCCATCGG',\n",
       " 'ACTTTCAGGTGTTTCGGCATGG',\n",
       " 'CCAAATCCTTGTGGCCATGAGG',\n",
       " 'TGGAGCAGCATTATGAACTTGG',\n",
       " 'CTCAAGTTCTTGAAAACGAGGG',\n",
       " 'CTTTCAGGATTACATGGCCAGG',\n",
       " 'CGGTACTCCGGACTCAGGAAGG',\n",
       " 'ACGGACTTTGACCGCACGCTGG',\n",
       " 'CTCGGGAAACAGCCCGGCAAGG',\n",
       " 'ATCGGGGACAGCTGCGCATGGG',\n",
       " 'AACTTCACGGGACTGTCGCTGG',\n",
       " 'GTCCGCATCGTCGGCGGGAAGG',\n",
       " 'AGCTTGCTGAATTCACGATGGG',\n",
       " 'GCGCCCACACGAAATGGGAGGG',\n",
       " 'CGTGTGGATCTCATCGACCTGG',\n",
       " 'GGTCGAGTTACACAAGTCCAGG',\n",
       " 'ACTCTACTTCTCGTACCCGGGG',\n",
       " 'GCAACGGTAGTGAGTGAACTGG',\n",
       " 'ACCATCGGGCACTAAGCCGTGG',\n",
       " 'GTGGGATTCCGGCAGCCGTGGG',\n",
       " 'TGGGTTGCACGGAGACGGCAGG',\n",
       " 'CATCCTTAATGTCCAGTCAAGG',\n",
       " 'TATTGGAAATAAACAGCGACGG',\n",
       " 'CAGAAGATTTGTGTAATGAAGG',\n",
       " 'AAACTTCAGGGTTAGCCTCAGG',\n",
       " 'TCCCTGATTGTTCCACCACAGG',\n",
       " 'GAACAGCCTTCTGGTGAGCAGG',\n",
       " 'CTTTGAGTGAGCTTGAGTCAGG',\n",
       " 'CAAAGGCTGTTCTCCGGAGAGG',\n",
       " 'CTTCAGGTGAACACCTCTCCGG',\n",
       " 'CTTCAGTTCAGCAACTTCCAGG',\n",
       " 'GTGCATCGGATGTTCTACGAGG',\n",
       " 'GACGACTCGGGCCGATGGGCGG',\n",
       " 'AGATCTACGACCTGTGGACAGG',\n",
       " 'GCCCATACCGTGTACACATTGG',\n",
       " 'TCATAAGCGATGTACTGGCAGG',\n",
       " 'CCGACAAGCCTCATAGTACTGG',\n",
       " 'CCAATGACCTTACCCGGCTAGG',\n",
       " 'TGACAAACCTCGGCAGAGTTGG',\n",
       " 'TGGGTAGGGGTTTACCGAAAGG',\n",
       " 'CCTTATGTTACAGATCACGTGG',\n",
       " 'AAGCGGACACCCTGCAACCTGG',\n",
       " 'GCATGGTCTCCACGCCCATCGG',\n",
       " 'GCACCCCTGAACGTAGCTGAGG',\n",
       " 'GGGACCTAACTCCTATGCATGG',\n",
       " 'CATCCAGTTCACCTCGGTAGGG',\n",
       " 'CGGCCGTGCTGGTCAACCAGGG',\n",
       " 'ATAAAGCTCCCTGTACGTGTGG',\n",
       " 'AACGATGCCTCCTACGTCGTGG',\n",
       " 'ACTCATCCATGACGCCCACTGG',\n",
       " 'CTTAAGTTCTGAAACGCCGCGG',\n",
       " 'GATGCCTACGGCAGTGAACTGG',\n",
       " 'ACCAGACCAACCCTATGAATGG',\n",
       " 'TTTCCGAGAGCCTAAACAAGGG',\n",
       " 'TATACGAAGGTTTCCAGAGGGG',\n",
       " 'GGTCTCGTGGCGTACCAGAAGG',\n",
       " 'TGGCTTCAGTATTATACCCTGG',\n",
       " 'TTTCTCACGGATGCCGCATTGG',\n",
       " 'TCAACACTGGTAATCAGATAGG',\n",
       " 'TAGGATTTCCCTGGTCCCAAGG',\n",
       " 'TCCAGGTATTCTTTCACACTGG',\n",
       " 'ATTTGATAGCTGGAATGACAGG',\n",
       " 'GTTCTACTGGCCGACCTAAGGG',\n",
       " 'GTTTCACAAGCGGCACGACAGG',\n",
       " 'GATGTAGCGGATAGCCCCAGGG',\n",
       " 'TAGAGCGGGACCACCACCATGG',\n",
       " 'CCGGGCATCATCATAGTAGTGG',\n",
       " 'GGCGATCTGTGATTGGGTCTGG',\n",
       " 'GACTCACTTAACCAGCGATCGG',\n",
       " 'AAGACGTTGGCTACACGGCGGG',\n",
       " 'AAGTTATGTACTGGACTACTGG',\n",
       " 'AATGACTATGAAGTACCGGAGG',\n",
       " 'AATCCATCCTGAGTCCGACAGG',\n",
       " 'CACACGATCCCCACGCTGCAGG',\n",
       " 'GTGCTCACTTATGGAACCTTGG',\n",
       " 'AGCAACATCCGGTAAACAATGG',\n",
       " 'CAGAACTGCGTCACTGTAGGGG',\n",
       " 'ACCGTTTCTGTCTGGCCATAGG',\n",
       " 'AAACTTGGCCGGCACTTCCTGG',\n",
       " 'ATCTACAGAACTGCCTCGCTGG',\n",
       " 'GAACATTCCTACTCGAGCCTGG',\n",
       " 'CTGCCATCTACTTCACTAGTGG',\n",
       " 'AACTATGAATCCATGAAACAGG',\n",
       " 'TGAAACAGGACTTCAAACTGGG',\n",
       " 'CTTATCAGTCCATTGGTCCAGG',\n",
       " 'CGATGGAGTTTTGAGGAACTGG',\n",
       " 'AGCCATAAATCGCTGTAACAGG',\n",
       " 'ATGTAGCTTGCATACGAACAGG',\n",
       " 'TACAATGGACTCCACCGCTGGG',\n",
       " ...]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "72ee0fcc-2726-4b1b-89fe-f7294a0ce2b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "747169"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp=df2[0].unique()\n",
    "temp=temp[0:-1]\n",
    "len(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8730caf4-ea04-4c05-adf4-8ed8d39e424c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "747170\n",
      "965615\n",
      "965459\n"
     ]
    }
   ],
   "source": [
    "temp=df2[0].unique()\n",
    "print(len(temp))\n",
    "temp = fix_length(temp,length=20)\n",
    "# print(len(temp))\n",
    "temp = fix_length(temp,length=21)\n",
    "# print(len(temp))\n",
    "temp = fix_length(temp,length=22)\n",
    "print(len(temp))\n",
    "temp = fix_length(temp,length=24)\n",
    "print(len(temp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac05809f-18d6-4a5f-bcae-426f990a7838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfix=[]\n",
    "counter=0\n",
    "for i in range(len(temp)):\n",
    "    if len(temp[i])!=23:\n",
    "#         df2[0][i]+=\"GG\"\n",
    "        myfix.append(temp[i])\n",
    "        counter+=1\n",
    "\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a7003387-c467-40ee-97c4-d3b04165ba9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sgRNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAAAAAAAAAAGGGGGGTTCTGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAAAAAAAAAAGTCGTACTTGGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAAAAAAAAAGAGCACGATGAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAAAAAAAAATTCCACCCCCTGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAAAAAAAAATTGCCGGGCGAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965454</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTAAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965455</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTCAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965456</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965457</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTTAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965458</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTTGG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>965459 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          sgRNA\n",
       "0       AAAAAAAAAAAGGGGGGTTCTGG\n",
       "1       AAAAAAAAAAAGTCGTACTTGGG\n",
       "2       AAAAAAAAAAGAGCACGATGAGG\n",
       "3       AAAAAAAAAATTCCACCCCCTGG\n",
       "4       AAAAAAAAAATTGCCGGGCGAGG\n",
       "...                         ...\n",
       "965454  TTTTTTTTTGAGACGGAGTTAAG\n",
       "965455  TTTTTTTTTGAGACGGAGTTCAG\n",
       "965456  TTTTTTTTTGAGACGGAGTTCGG\n",
       "965457  TTTTTTTTTGAGACGGAGTTTAG\n",
       "965458  TTTTTTTTTGAGACGGAGTTTGG\n",
       "\n",
       "[965459 rows x 1 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame(temp)\n",
    "df2.columns= [\"sgRNA\"]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "772d44f1-a340-430d-814e-c56ee6b37abc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sgRNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CGCCGCCGCTTTCGGTGATGAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GGCAGCGTCGTGCACGGGTCGGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TGGGCGGATCACTTGACGTCAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TTACCATAGTGTACGGGTGCAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCTACTGAAGTGGTAGCAACAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337119</th>\n",
       "      <td>GGCAGAAATGTGCTCTTGCAGGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337120</th>\n",
       "      <td>GGGAAGATTTTGGACTATTTGGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337121</th>\n",
       "      <td>GGCAACTTTTTATGCAAGTTGGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337122</th>\n",
       "      <td>GGTAGCTGGGACTTGCCCTTGGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337123</th>\n",
       "      <td>GGACTTCCAGGACTGCCTTGGGG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>337124 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          sgRNA\n",
       "0       CGCCGCCGCTTTCGGTGATGAGG\n",
       "1       GGCAGCGTCGTGCACGGGTCGGG\n",
       "2       TGGGCGGATCACTTGACGTCAGG\n",
       "3       TTACCATAGTGTACGGGTGCAGG\n",
       "4       TCTACTGAAGTGGTAGCAACAGG\n",
       "...                         ...\n",
       "337119  GGCAGAAATGTGCTCTTGCAGGG\n",
       "337120  GGGAAGATTTTGGACTATTTGGG\n",
       "337121  GGCAACTTTTTATGCAAGTTGGG\n",
       "337122  GGTAGCTGGGACTTGCCCTTGGG\n",
       "337123  GGACTTCCAGGACTGCCTTGGGG\n",
       "\n",
       "[337124 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/unlabeled_sgrna0.csv\")\n",
    "df1.columns= [\"sgRNA\"]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4e537f39-ef3f-46c1-b5be-93650084f0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sgRNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CGCCGCCGCTTTCGGTGATGAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GGCAGCGTCGTGCACGGGTCGGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TGGGCGGATCACTTGACGTCAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TTACCATAGTGTACGGGTGCAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCTACTGAAGTGGTAGCAACAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302578</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTAAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302579</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTCAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302580</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302581</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTTAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302582</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTTGG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302583 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           sgRNA\n",
       "0        CGCCGCCGCTTTCGGTGATGAGG\n",
       "1        GGCAGCGTCGTGCACGGGTCGGG\n",
       "2        TGGGCGGATCACTTGACGTCAGG\n",
       "3        TTACCATAGTGTACGGGTGCAGG\n",
       "4        TCTACTGAAGTGGTAGCAACAGG\n",
       "...                          ...\n",
       "1302578  TTTTTTTTTGAGACGGAGTTAAG\n",
       "1302579  TTTTTTTTTGAGACGGAGTTCAG\n",
       "1302580  TTTTTTTTTGAGACGGAGTTCGG\n",
       "1302581  TTTTTTTTTGAGACGGAGTTTAG\n",
       "1302582  TTTTTTTTTGAGACGGAGTTTGG\n",
       "\n",
       "[1302583 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.concat([df1,df2], ignore_index=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "21526c11-7417-470c-b93c-f9cbb31eef2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CGCCGCCGCTTTCGGTGATGAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GGCAGCGTCGTGCACGGGTCGGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TGGGCGGATCACTTGACGTCAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TTACCATAGTGTACGGGTGCAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCTACTGAAGTGGTAGCAACAGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066619</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTAAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066620</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTCAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066621</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTCGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066622</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTTAG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066623</th>\n",
       "      <td>TTTTTTTTTGAGACGGAGTTTGG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1066624 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0\n",
       "0        CGCCGCCGCTTTCGGTGATGAGG\n",
       "1        GGCAGCGTCGTGCACGGGTCGGG\n",
       "2        TGGGCGGATCACTTGACGTCAGG\n",
       "3        TTACCATAGTGTACGGGTGCAGG\n",
       "4        TCTACTGAAGTGGTAGCAACAGG\n",
       "...                          ...\n",
       "1066619  TTTTTTTTTGAGACGGAGTTAAG\n",
       "1066620  TTTTTTTTTGAGACGGAGTTCAG\n",
       "1066621  TTTTTTTTTGAGACGGAGTTCGG\n",
       "1066622  TTTTTTTTTGAGACGGAGTTTAG\n",
       "1066623  TTTTTTTTTGAGACGGAGTTTGG\n",
       "\n",
       "[1066624 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=pd.DataFrame(df3['sgRNA'].unique())\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4e9ba465-48ca-4ea8-8799-8bece51b3f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.to_csv(\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/unlabeled_sgrna.csv\", index = False, header = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "423dbbf1-1cf9-4806-82d9-b04d066622c5",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df4' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-d67f66e6b3a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/unlabeled_sgrna.txt\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"w\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df4' is not defined"
     ]
    }
   ],
   "source": [
    "with open(\"/mnt/d/M3/Projects/BCB/Crispr/Dataset/unlabeled_sgrna.txt\",\"w\") as f:\n",
    "    for i in df4[0]:\n",
    "        f.write(space(i, mer=3)+\"\\n\")"
   ]
  }
 ],
 "metadata": {
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