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filter_vcfs.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "mount_file_id": "1yyy1oYr8c70ws5KBPQLZ9jxs1Z-QR4aZ", | |
| "authorship_tag": "ABX9TyMoXrKdXAiw9dzvHcxm2D21", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/AstraBert/2d5299582e2087b6de1883ab0cf3a255/filter_vcfs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# ANALYZE AND FILTER YOUR VCF FILE *WITHOUT EXCEL*\n", | |
| "\n", | |
| "In this notebook, we'll be learning how to complete a thorough filtering of a [VCF file](https://en.wikipedia.org/wiki/Variant_Call_Format) (provided in `tsv` format, short for \"tab-separated values\") without employing fancy Excel functions, in less than 80 lines of pure python code!\n", | |
| "\n", | |
| "You **won't be actually coding** (everything has already been done for you), but you'll need to know how to use a notebook: if you don't, no worries! It is really simple and, by clicking on [this link](https://youtu.be/HcUZ5xbdvro?feature=shared), you'll be able to see an awesome 2-mins YouTube tutorial by London Business Analytics Group.\n", | |
| "\n", | |
| "## How do I run a code block?\n", | |
| "If you move your mouse arrow over the square brackets with the numbers in the blocks formatted with a gray background, you will see that the usual \"start\" sign appears which you also click to start videos on YouTube after you have paused them: you just have to clik on it!\n", | |
| "\n", | |
| "Explanatory image:\n", | |
| "#" | |
| ], | |
| "metadata": { | |
| "id": "dPZGEowzcHCL" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 1. Collect data\n", | |
| "\n", | |
| "### 1a. Mount your Google Drive\n", | |
| "By running the next code block, you'll link your Google Drive to this notebook. This will help you while interacting with the files you need to upload." | |
| ], | |
| "metadata": { | |
| "id": "mbVvwZEIgiOl" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ZzMz9Or8bKTo", | |
| "outputId": "cf731489-c43c-4875-a65f-f2637270dfc8" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### 1b. Upload VCF file from Drive\n", | |
| "\n", | |
| "By running the next block, you'll upload your VCF file to this notebook.\n", | |
| "\n", | |
| "**ATTENTION** ⚠! Mind that your file may have a different path (the thing with \"/\"s, like: `/content/drive/MyDrive/VCF/2827-22.tsv`) from the one reported here!" | |
| ], | |
| "metadata": { | |
| "id": "IJNvFwuxhGgM" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "inf = \"/content/drive/MyDrive/VCF/2827-22.tsv\"" | |
| ], | |
| "metadata": { | |
| "id": "qZSmCT37hEEJ" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### 1c. Download the panel file and upload it to Google Drive\n", | |
| "\n", | |
| "Now you can go to [PanelApp](https://panelapp.genomicsengland.co.uk/panels/) and download your panel of interest from there.\n", | |
| "\n", | |
| "Once you downloaded it, run the following block and you'll be able to upload the file directly on Colab." | |
| ], | |
| "metadata": { | |
| "id": "9vXVylI7i77U" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from google.colab import files\n", | |
| "uploaded = files.upload()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 73 | |
| }, | |
| "id": "COsJ0IWwjc55", | |
| "outputId": "ddf59be4-1889-4c5f-d444-d2c259d55cd3" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <input type=\"file\" id=\"files-bb75ab16-bce9-4b18-8ef5-731b0ab10724\" name=\"files[]\" multiple disabled\n", | |
| " style=\"border:none\" />\n", | |
| " <output id=\"result-bb75ab16-bce9-4b18-8ef5-731b0ab10724\">\n", | |
| " Upload widget is only available when the cell has been executed in the\n", | |
| " current browser session. Please rerun this cell to enable.\n", | |
| " </output>\n", | |
| " <script>// Copyright 2017 Google LLC\n", | |
| "//\n", | |
| "// Licensed under the Apache License, Version 2.0 (the \"License\");\n", | |
| "// you may not use this file except in compliance with the License.\n", | |
| "// You may obtain a copy of the License at\n", | |
| "//\n", | |
| "// http://www.apache.org/licenses/LICENSE-2.0\n", | |
| "//\n", | |
| "// Unless required by applicable law or agreed to in writing, software\n", | |
| "// distributed under the License is distributed on an \"AS IS\" BASIS,\n", | |
| "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", | |
| "// See the License for the specific language governing permissions and\n", | |
| "// limitations under the License.\n", | |
| "\n", | |
| "/**\n", | |
| " * @fileoverview Helpers for google.colab Python module.\n", | |
| " */\n", | |
| "(function(scope) {\n", | |
| "function span(text, styleAttributes = {}) {\n", | |
| " const element = document.createElement('span');\n", | |
| " element.textContent = text;\n", | |
| " for (const key of Object.keys(styleAttributes)) {\n", | |
| " element.style[key] = styleAttributes[key];\n", | |
| " }\n", | |
| " return element;\n", | |
| "}\n", | |
| "\n", | |
| "// Max number of bytes which will be uploaded at a time.\n", | |
| "const MAX_PAYLOAD_SIZE = 100 * 1024;\n", | |
| "\n", | |
| "function _uploadFiles(inputId, outputId) {\n", | |
| " const steps = uploadFilesStep(inputId, outputId);\n", | |
| " const outputElement = document.getElementById(outputId);\n", | |
| " // Cache steps on the outputElement to make it available for the next call\n", | |
| " // to uploadFilesContinue from Python.\n", | |
| " outputElement.steps = steps;\n", | |
| "\n", | |
| " return _uploadFilesContinue(outputId);\n", | |
| "}\n", | |
| "\n", | |
| "// This is roughly an async generator (not supported in the browser yet),\n", | |
| "// where there are multiple asynchronous steps and the Python side is going\n", | |
| "// to poll for completion of each step.\n", | |
| "// This uses a Promise to block the python side on completion of each step,\n", | |
| "// then passes the result of the previous step as the input to the next step.\n", | |
| "function _uploadFilesContinue(outputId) {\n", | |
| " const outputElement = document.getElementById(outputId);\n", | |
| " const steps = outputElement.steps;\n", | |
| "\n", | |
| " const next = steps.next(outputElement.lastPromiseValue);\n", | |
| " return Promise.resolve(next.value.promise).then((value) => {\n", | |
| " // Cache the last promise value to make it available to the next\n", | |
| " // step of the generator.\n", | |
| " outputElement.lastPromiseValue = value;\n", | |
| " return next.value.response;\n", | |
| " });\n", | |
| "}\n", | |
| "\n", | |
| "/**\n", | |
| " * Generator function which is called between each async step of the upload\n", | |
| " * process.\n", | |
| " * @param {string} inputId Element ID of the input file picker element.\n", | |
| " * @param {string} outputId Element ID of the output display.\n", | |
| " * @return {!Iterable<!Object>} Iterable of next steps.\n", | |
| " */\n", | |
| "function* uploadFilesStep(inputId, outputId) {\n", | |
| " const inputElement = document.getElementById(inputId);\n", | |
| " inputElement.disabled = false;\n", | |
| "\n", | |
| " const outputElement = document.getElementById(outputId);\n", | |
| " outputElement.innerHTML = '';\n", | |
| "\n", | |
| " const pickedPromise = new Promise((resolve) => {\n", | |
| " inputElement.addEventListener('change', (e) => {\n", | |
| " resolve(e.target.files);\n", | |
| " });\n", | |
| " });\n", | |
| "\n", | |
| " const cancel = document.createElement('button');\n", | |
| " inputElement.parentElement.appendChild(cancel);\n", | |
| " cancel.textContent = 'Cancel upload';\n", | |
| " const cancelPromise = new Promise((resolve) => {\n", | |
| " cancel.onclick = () => {\n", | |
| " resolve(null);\n", | |
| " };\n", | |
| " });\n", | |
| "\n", | |
| " // Wait for the user to pick the files.\n", | |
| " const files = yield {\n", | |
| " promise: Promise.race([pickedPromise, cancelPromise]),\n", | |
| " response: {\n", | |
| " action: 'starting',\n", | |
| " }\n", | |
| " };\n", | |
| "\n", | |
| " cancel.remove();\n", | |
| "\n", | |
| " // Disable the input element since further picks are not allowed.\n", | |
| " inputElement.disabled = true;\n", | |
| "\n", | |
| " if (!files) {\n", | |
| " return {\n", | |
| " response: {\n", | |
| " action: 'complete',\n", | |
| " }\n", | |
| " };\n", | |
| " }\n", | |
| "\n", | |
| " for (const file of files) {\n", | |
| " const li = document.createElement('li');\n", | |
| " li.append(span(file.name, {fontWeight: 'bold'}));\n", | |
| " li.append(span(\n", | |
| " `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n", | |
| " `last modified: ${\n", | |
| " file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n", | |
| " 'n/a'} - `));\n", | |
| " const percent = span('0% done');\n", | |
| " li.appendChild(percent);\n", | |
| "\n", | |
| " outputElement.appendChild(li);\n", | |
| "\n", | |
| " const fileDataPromise = new Promise((resolve) => {\n", | |
| " const reader = new FileReader();\n", | |
| " reader.onload = (e) => {\n", | |
| " resolve(e.target.result);\n", | |
| " };\n", | |
| " reader.readAsArrayBuffer(file);\n", | |
| " });\n", | |
| " // Wait for the data to be ready.\n", | |
| " let fileData = yield {\n", | |
| " promise: fileDataPromise,\n", | |
| " response: {\n", | |
| " action: 'continue',\n", | |
| " }\n", | |
| " };\n", | |
| "\n", | |
| " // Use a chunked sending to avoid message size limits. See b/62115660.\n", | |
| " let position = 0;\n", | |
| " do {\n", | |
| " const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n", | |
| " const chunk = new Uint8Array(fileData, position, length);\n", | |
| " position += length;\n", | |
| "\n", | |
| " const base64 = btoa(String.fromCharCode.apply(null, chunk));\n", | |
| " yield {\n", | |
| " response: {\n", | |
| " action: 'append',\n", | |
| " file: file.name,\n", | |
| " data: base64,\n", | |
| " },\n", | |
| " };\n", | |
| "\n", | |
| " let percentDone = fileData.byteLength === 0 ?\n", | |
| " 100 :\n", | |
| " Math.round((position / fileData.byteLength) * 100);\n", | |
| " percent.textContent = `${percentDone}% done`;\n", | |
| "\n", | |
| " } while (position < fileData.byteLength);\n", | |
| " }\n", | |
| "\n", | |
| " // All done.\n", | |
| " yield {\n", | |
| " response: {\n", | |
| " action: 'complete',\n", | |
| " }\n", | |
| " };\n", | |
| "}\n", | |
| "\n", | |
| "scope.google = scope.google || {};\n", | |
| "scope.google.colab = scope.google.colab || {};\n", | |
| "scope.google.colab._files = {\n", | |
| " _uploadFiles,\n", | |
| " _uploadFilesContinue,\n", | |
| "};\n", | |
| "})(self);\n", | |
| "</script> " | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Saving Congenital myopathy.tsv to Congenital myopathy.tsv\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Now run this block to allow this notebook to actually *use* this file:" | |
| ], | |
| "metadata": { | |
| "id": "pnY4yannjtKU" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "panel = \"Congenital myopathy.tsv\"" | |
| ], | |
| "metadata": { | |
| "id": "cH9CBTDwj26K" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 2. Preprocess the data\n", | |
| "### 2a. Define constants\n", | |
| "\n", | |
| "We'll be defining constants that we'll be using to filter our VCF file, specifically we'll set the columns we want to retain and the variant effects we want to keep." | |
| ], | |
| "metadata": { | |
| "id": "5cw0u8Rsj-5h" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "EFFECTS = {'start_lost', 'stop_lost', 'stop_gained', 'missense_variant', 'frameshift_variant&stop_lost', 'disruptive_inframe_deletion', 'initiator_codon_variant','frameshift_variant', 'disruptive_inframe_insertion', 'frameshift_variant&start_lost', 'frameshift_variant&stop_gained', 'stop_retained_variant'}\n", | |
| "TO_RETAIN = ['CHR', 'START', 'END', 'REF', 'ALT', 'EFFECT', 'GENE', 'TRANSCRIPT_ID', 'SELECT_CANONICAL', 'EXON_INTRON_NUM', 'HGVS_C', 'HGVS_P', 'CDS_DISTANCE', 'AA_LEN', 'OTHER_TRANSCRIPTS', 'gnomAD_AF_ALL', 'gnomAD_Hom_ALL', 'gnomAD_AF_NFE', 'gnomAD_Hom_NFE', 'CADD_score', 'PolyPhen-2_pred', 'SIFT_pred', 'PseeAC-RF_pred', 'PseeAC-RF_score', 'ClinVar_hotSpot', 'ClinVar_RCV', 'ClinVar_clinical_significance', 'ClinVar_rev_status', 'ClinVar_traits', 'ClinVar_PMIDS', 'Diseases', 'Disease_IDs']\n", | |
| "TO_RETAIN_SPEC = ['.GENO', '.GENO_QUAL', '.AF', '.AO', '.RO', '.COV']" | |
| ], | |
| "metadata": { | |
| "id": "91B_J-1QkWLh" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "### 2b. Define functions\n", | |
| "\n", | |
| "**What the hell is a python function??🤯**\n", | |
| "\n", | |
| "A python function is like an oven: it takes some inputs (a raw cake) and spits out some outputs (a baked cake)🎂... This is everything we need to know!\n", | |
| "\n", | |
| "**Why are function useful for us?**\n", | |
| "\n", | |
| "We'll be using functions to:\n", | |
| "\n", | |
| "- preprocess our data (`do_preprocess`, that takes our raw VCF as input and returns only the columns we want as output)\n", | |
| "- filter variants according to effect, quality, coverage, gnomAD frequency, gnomAD homozygous count (`do_filter`, that takes the selected columns as input and returns a table with gene, effect, predicted pathogenicity, HGVS_C and HGVS_P for each filtered variant)\n", | |
| "- compare filtered variants to panel genes (`summarize_out`, which takes the table from `do_filter` and turns it into a final, nicely summarized, table)" | |
| ], | |
| "metadata": { | |
| "id": "TAOB3guPkdeG" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import os\n", | |
| "\n", | |
| "def eliminate_first_line(infile):\n", | |
| " fp = open(infile, \"r+\")\n", | |
| " lines = fp.readlines()\n", | |
| " if lines[0].startswith(\"#\"):\n", | |
| " fp.seek(0)\n", | |
| " fp.truncate()\n", | |
| " lines = [line for line in lines if not line.startswith(\"#\")]\n", | |
| " fp.writelines(lines)\n", | |
| " fp.close()\n", | |
| " return infile\n", | |
| " return infile\n", | |
| "\n", | |
| "\n", | |
| "def do_preprocess(infile):\n", | |
| " global TO_RETAIN_SPEC\n", | |
| " indf = pd.read_csv(eliminate_first_line(infile), delimiter=\"\\t\")\n", | |
| " keys = list(indf.keys())\n", | |
| " to_retain = TO_RETAIN\n", | |
| " for i in TO_RETAIN_SPEC:\n", | |
| " for key in keys:\n", | |
| " if key.endswith(i):\n", | |
| " to_retain.append(key)\n", | |
| " else:\n", | |
| " continue\n", | |
| " newdf = indf[to_retain]\n", | |
| " return newdf\n", | |
| "\n", | |
| "def do_filter(df):\n", | |
| " newdict = {key: df[key] for key in list(df.keys())}\n", | |
| " newdf = {\"GENE\": [], \"EFFECT\": [], \"Polyphen\": [], \"SIFT\": [], \"HGVS_CP\": []}\n", | |
| " print(\"Variants before filtering:\", len(newdict[list(newdict.keys())[0]]))\n", | |
| " for i in range(len(newdict[list(newdict.keys())[0]])):\n", | |
| " if (newdict[\"EFFECT\"][i] not in EFFECTS) or (float(newdict[\"gnomAD_AF_ALL\"][i]) > 0.01) or (int(newdict[\"2827-22.GENO_QUAL\"][i]) != 99) or (int(newdict[\"2827-22.COV\"][i]) < 15) or (int(newdict[\"gnomAD_Hom_ALL\"][i]) > 35):\n", | |
| " continue\n", | |
| " else:\n", | |
| " newdf[\"GENE\"].append(newdict[\"GENE\"][i])\n", | |
| " newdf[\"Polyphen\"].append(newdict[\"PolyPhen-2_pred\"][i])\n", | |
| " newdf[\"SIFT\"].append(newdict[\"SIFT_pred\"][i])\n", | |
| " newdf[\"HGVS_CP\"].append(\":\".join([newdict[\"HGVS_C\"][i], newdict[\"HGVS_P\"][i]]))\n", | |
| " newdf[\"EFFECT\"].append(newdict[\"EFFECT\"][i])\n", | |
| " print(\"Variants after filtering:\", len(newdf[list(newdf.keys())[0]]))\n", | |
| " return pd.DataFrame.from_dict(newdf)\n", | |
| "\n", | |
| "def summarize_out(panel_df, target_df):\n", | |
| " genes = list(panel_df[\"Gene Symbol\"])\n", | |
| " gens = list(target_df[\"GENE\"])\n", | |
| " rows = []\n", | |
| " for i in range(len(gens)):\n", | |
| " if gens[i] in genes:\n", | |
| " rows.append(target_df.iloc[i])\n", | |
| " else:\n", | |
| " continue\n", | |
| " return pd.DataFrame(rows)" | |
| ], | |
| "metadata": { | |
| "id": "_LATx913nIHL" | |
| }, | |
| "execution_count": 13, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 3. Finally get your results\n", | |
| "\n", | |
| "Now that we have defined everything, it's time to get our results!\n", | |
| "\n", | |
| "Let's first **select our columns of interest**:😎" | |
| ], | |
| "metadata": { | |
| "id": "AJ00PQOQnaM8" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df = do_preprocess(inf)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "IzOFbV2enZUS", | |
| "outputId": "75b14500-b53a-421a-d156-c5d1e3057f30" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "<ipython-input-9-7f2991e6aecd>:19: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n", | |
| " indf = pd.read_csv(eliminate_first_line(infile), delimiter=\"\\t\")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "(Let's see how the file looks like now🧐)" | |
| ], | |
| "metadata": { | |
| "id": "W8c8peT5oCph" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df" | |
| ], | |
| "metadata": { | |
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| "0 1 69270 69270 A G \n", | |
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| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-a1198fdb-ff6a-4151-a8a0-1a4be79042a3 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "df" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Now we filter out all the variants that do not match the following criteria:\n", | |
| "\n", | |
| "- Less than 1% frequency on gnomAD\n", | |
| "- Less than 35 homozygous individuals registered\n", | |
| "- 99.0 quality\n", | |
| "- More than 15 reads coverage\n", | |
| "- Exonic variant" | |
| ], | |
| "metadata": { | |
| "id": "c7TWiYO0qFBV" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "newdf = do_filter(df)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "w8xx0uz8qfDg", | |
| "outputId": "0c5de803-5485-4356-f1d7-30891b736df8" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Variants before filtering: 133838\n", | |
| "Variants after filtering: 1730\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "(Let's see how the file looks like now🧐)" | |
| ], | |
| "metadata": { | |
| "id": "0X2pbPo6q5Ti" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "newdf" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 423 | |
| }, | |
| "id": "O_Vtih6bq2q4", | |
| "outputId": "2bbc9125-260c-426f-c1c7-e48e6d357d18" | |
| }, | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " GENE EFFECT Polyphen SIFT \\\n", | |
| "0 CDK11A disruptive_inframe_deletion . . \n", | |
| "1 CCDC27 missense_variant D B \n", | |
| "2 RPL22 missense_variant B B \n", | |
| "3 ZBTB48 missense_variant B B \n", | |
| "4 FBXO6 missense_variant B B \n", | |
| "... ... ... ... ... \n", | |
| "1725 CNGA2 missense_variant B B \n", | |
| "1726 PNMA3 missense_variant . . \n", | |
| "1727 RPL10 missense_variant . . \n", | |
| "1728 RPL10 missense_variant B D \n", | |
| "1729 PLXNA3 missense_variant . . \n", | |
| "\n", | |
| " HGVS_CP \n", | |
| "0 c.933_941delGGAGGAGGA:p.Glu312_Glu314del \n", | |
| "1 c.1513A>C:p.Asn505His \n", | |
| "2 c.20T>C:p.Leu7Pro \n", | |
| "3 c.679G>A:p.Gly227Ser \n", | |
| "4 c.820C>A:p.Gln274Lys \n", | |
| "... ... \n", | |
| "1725 c.338G>T:p.Gly113Val \n", | |
| "1726 c.1130C>T:p.Ala377Val \n", | |
| "1727 c.605G>A:p.Ser202Asn \n", | |
| "1728 c.628C>T:p.Arg210Trp \n", | |
| "1729 c.2589G>C:p.Glu863Asp \n", | |
| "\n", | |
| "[1730 rows x 5 columns]" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-0a3fbd4d-d322-4c72-9c0b-25d1e4dee587\" class=\"colab-df-container\">\n", | |
| " <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>GENE</th>\n", | |
| " <th>EFFECT</th>\n", | |
| " <th>Polyphen</th>\n", | |
| " <th>SIFT</th>\n", | |
| " <th>HGVS_CP</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>CDK11A</td>\n", | |
| " <td>disruptive_inframe_deletion</td>\n", | |
| " <td>.</td>\n", | |
| " <td>.</td>\n", | |
| " <td>c.933_941delGGAGGAGGA:p.Glu312_Glu314del</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>CCDC27</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>D</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.1513A>C:p.Asn505His</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>RPL22</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>B</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.20T>C:p.Leu7Pro</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>ZBTB48</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>B</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.679G>A:p.Gly227Ser</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>FBXO6</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>B</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.820C>A:p.Gln274Lys</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1725</th>\n", | |
| " <td>CNGA2</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>B</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.338G>T:p.Gly113Val</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1726</th>\n", | |
| " <td>PNMA3</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>.</td>\n", | |
| " <td>.</td>\n", | |
| " <td>c.1130C>T:p.Ala377Val</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1727</th>\n", | |
| " <td>RPL10</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>.</td>\n", | |
| " <td>.</td>\n", | |
| " <td>c.605G>A:p.Ser202Asn</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1728</th>\n", | |
| " <td>RPL10</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>B</td>\n", | |
| " <td>D</td>\n", | |
| " <td>c.628C>T:p.Arg210Trp</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1729</th>\n", | |
| " <td>PLXNA3</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>.</td>\n", | |
| " <td>.</td>\n", | |
| " <td>c.2589G>C:p.Glu863Asp</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1730 rows × 5 columns</p>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a3fbd4d-d322-4c72-9c0b-25d1e4dee587')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", | |
| " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| "\n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
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| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
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| " .colab-df-buttons div {\n", | |
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| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-0a3fbd4d-d322-4c72-9c0b-25d1e4dee587');\n", | |
| " const dataTable =\n", | |
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| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
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| " .colab-df-quickchart {\n", | |
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| " border-bottom-color: var(--fill-color);\n", | |
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| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
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| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
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| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
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| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
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| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
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| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
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| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "newdf", | |
| "summary": "{\n \"name\": \"newdf\",\n \"rows\": 1730,\n \"fields\": [\n {\n \"column\": \"GENE\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 879,\n \"samples\": [\n \"TRBV13\",\n \"DSP\",\n \"LENG1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"EFFECT\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 8,\n \"samples\": [\n \"missense_variant\",\n \"stop_lost\",\n \"disruptive_inframe_deletion\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Polyphen\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \".\",\n \"D\",\n \"B\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SIFT\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \".\",\n \"B\",\n \"D\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"HGVS_CP\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1701,\n \"samples\": [\n \"c.1141G>T:p.Glu381*\",\n \"c.646G>A:p.Glu216Lys\",\n \"c.1256C>A:p.Ala419Glu\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Now we finally compare genes from the panel to genes from our filtered variants and retain only the shared ones:🚀" | |
| ], | |
| "metadata": { | |
| "id": "pOIcExt8rGJP" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "dff = pd.read_csv(panel, delimiter=\"\\t\")\n", | |
| "newdff = summarize_out(dff, newdf)" | |
| ], | |
| "metadata": { | |
| "id": "5eKCIBW5rSaU" | |
| }, | |
| "execution_count": 16, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Let's see the output! Are you thrilled?😁" | |
| ], | |
| "metadata": { | |
| "id": "zUhPlSUwrcrC" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "newdff" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 237 | |
| }, | |
| "id": "QhNGmh9Auzmj", | |
| "outputId": "254066bc-af4f-4c01-926a-b74ca745dd71" | |
| }, | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " GENE EFFECT Polyphen SIFT HGVS_CP\n", | |
| "391 COL12A1 missense_variant B B c.6590C>T:p.Thr2197Ile\n", | |
| "828 VCP missense_variant D B c.469G>A:p.Gly157Arg\n", | |
| "1141 ISCU missense_variant . . c.19_20delTTinsGG:p.Phe7Gly\n", | |
| "1279 RYR3 missense_variant D B c.11545A>C:p.Asn3849His\n", | |
| "1412 PIEZO2 missense_variant . B c.147A>C:p.Lys49Asn\n", | |
| "1419 EPG5 missense_variant B B c.1511A>C:p.His504Pro" | |
| ], | |
| "text/html": [ | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>GENE</th>\n", | |
| " <th>EFFECT</th>\n", | |
| " <th>Polyphen</th>\n", | |
| " <th>SIFT</th>\n", | |
| " <th>HGVS_CP</th>\n", | |
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| " <td>COL12A1</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>B</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.6590C>T:p.Thr2197Ile</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>828</th>\n", | |
| " <td>VCP</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>D</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.469G>A:p.Gly157Arg</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1141</th>\n", | |
| " <td>ISCU</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>.</td>\n", | |
| " <td>.</td>\n", | |
| " <td>c.19_20delTTinsGG:p.Phe7Gly</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1279</th>\n", | |
| " <td>RYR3</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>D</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.11545A>C:p.Asn3849His</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1412</th>\n", | |
| " <td>PIEZO2</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>.</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.147A>C:p.Lys49Asn</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1419</th>\n", | |
| " <td>EPG5</td>\n", | |
| " <td>missense_variant</td>\n", | |
| " <td>B</td>\n", | |
| " <td>B</td>\n", | |
| " <td>c.1511A>C:p.His504Pro</td>\n", | |
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| " const element = document.querySelector('#df-85d1dd1b-fd77-419c-8a38-2f5a0f7bc2ee');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
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| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
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| " + ' to learn more about interactive tables.';\n", | |
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| " async function quickchart(key) {\n", | |
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| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
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| " quickchartButtonEl.style.display =\n", | |
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| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "variable_name": "newdff", | |
| "summary": "{\n \"name\": \"newdff\",\n \"rows\": 6,\n \"fields\": [\n {\n \"column\": \"GENE\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"COL12A1\",\n \"VCP\",\n \"EPG5\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"EFFECT\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"missense_variant\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Polyphen\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"B\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SIFT\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \".\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"HGVS_CP\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"c.6590C>T:p.Thr2197Ile\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 19 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Let's generate links to copy-paste in your browser and query ClinVar for these candidate variants!🤠" | |
| ], | |
| "metadata": { | |
| "id": "lc6U08TBu_t8" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "newdict = {key: list(newdff[key]) for key in list(newdff.keys())}\n", | |
| "for i in range(len(newdict[list(newdict.keys())[0]])):\n", | |
| " print(f\"https://www.ncbi.nlm.nih.gov/clinvar/?term={newdict['GENE'][i]}+{newdict['HGVS_CP'][i].replace(':','%3A')}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "WJ8umyHjrcPN", | |
| "outputId": "598951c7-05a7-418c-a75a-7d8ccc7f5213" | |
| }, | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "https://www.ncbi.nlm.nih.gov/clinvar/?term=COL12A1+c.6590C>T%3Ap.Thr2197Ile\n", | |
| "https://www.ncbi.nlm.nih.gov/clinvar/?term=VCP+c.469G>A%3Ap.Gly157Arg\n", | |
| "https://www.ncbi.nlm.nih.gov/clinvar/?term=ISCU+c.19_20delTTinsGG%3Ap.Phe7Gly\n", | |
| "https://www.ncbi.nlm.nih.gov/clinvar/?term=RYR3+c.11545A>C%3Ap.Asn3849His\n", | |
| "https://www.ncbi.nlm.nih.gov/clinvar/?term=PIEZO2+c.147A>C%3Ap.Lys49Asn\n", | |
| "https://www.ncbi.nlm.nih.gov/clinvar/?term=EPG5+c.1511A>C%3Ap.His504Pro\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "From here, you can directly go to OMIM and VarSome! Also, if you are interested in searching [Franklin](https://franklin.genoox.com/clinical-db/home), you can run the block down here and use the printed queries:😶🌫️" | |
| ], | |
| "metadata": { | |
| "id": "0NGoVY3v1CR9" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "newdict = {key: list(newdff[key]) for key in list(newdff.keys())}\n", | |
| "for i in range(len(newdict[list(newdict.keys())[0]])):\n", | |
| " print(f\"{newdict['GENE'][i]}:{newdict['HGVS_CP'][i].split(':')[0]}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Tamt7XRA1PYw", | |
| "outputId": "860e66c4-17f9-4f79-da53-098c307969b3" | |
| }, | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "COL12A1:c.6590C>T\n", | |
| "VCP:c.469G>A\n", | |
| "ISCU:c.19_20delTTinsGG\n", | |
| "RYR3:c.11545A>C\n", | |
| "PIEZO2:c.147A>C\n", | |
| "EPG5:c.1511A>C\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# HAVE FUN!❤️\n", | |
| "\n", | |
| "\n", | |
| "And give a little star to this [Gist on GitHub](#)🥰" | |
| ], | |
| "metadata": { | |
| "id": "7kZtIHvr2Pp_" | |
| } | |
| } | |
| ] | |
| } |
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