├── .github └── workflows │ └── actions.yml ├── .gitignore ├── LICENSE ├── README.md ├── gnomad_api_cli.py ├── gnomad_api_gui.py ├── img ├── main_screen.png ├── results.png └── results_2.png ├── myFavoriteGenes.txt └── requirements.txt /.github/workflows/actions.yml: -------------------------------------------------------------------------------- 1 | name: Actions for gnomad_python_api 2 | on: [push] 3 | jobs: 4 | build_and_run: 5 | runs-on: ubuntu-latest 6 | strategy: 7 | matrix: 8 | python-version: [3.6, 3.7, 3.8] 9 | steps: 10 | - uses: actions/checkout@v2 11 | - name: Set up Python ${{ matrix.python-version }} 12 | uses: actions/setup-python@v2 13 | with: 14 | python-version: ${{ matrix.python-version }} 15 | - name: Install dependencies 16 | run: | 17 | python -m pip install --upgrade pip 18 | if [ -f requirements.txt ]; then pip install -r requirements.txt; fi 19 | - name: Test a single transcript 20 | run: | 21 | # Test the script by retrieving a transcript data 22 | python gnomad_api_cli.py -filter_by=gene_name -search_by="BRCA1" -dataset="gnomad_r2_1" -sv_dataset="gnomad_sv_r2_1" 23 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | outputs/ 3 | outputs/* -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🧬 gnomAD Python API 2 | 3 | ![Actions for gnomad_python_api](https://github.com/furkanmtorun/gnomad_python_api/workflows/Actions%20for%20gnomad_python_api/badge.svg) 4 | ![Python Badges](https://img.shields.io/badge/Tested_with_Python-3.6%20%7C%203.7%20%7C%203.8-blue) 5 | ![gnomAD Python API License](https://img.shields.io/badge/License-%20GPL--3.0-green) 6 | 7 | - [🧬 gnomAD Python API](#-gnomad-python-api) 8 | - [:hash: What is *gnomAD* and the purpose of this script?](#hash-what-is-gnomad-and-the-purpose-of-this-script) 9 | - [:hash: Update on the repository and gnomadR tool](#hash-update-on-the-repository-and-gnomadr-tool) 10 | - [:hash: Requirements and Installation](#hash-requirements-and-installation) 11 | - [:hash: GUI | Usage](#hash-gui--usage) 12 | - [:hash: CLI | Usage & Options](#hash-cli--usage--options) 13 | - [:hash: CLI | Example Usages](#hash-cli--example-usages) 14 | - [:hash: Disclaimer](#hash-disclaimer) 15 | - [:hash: Contributing & Feedback](#hash-contributing--feedback) 16 | - [:hash: Developer](#hash-developer) 17 | - [:hash: References](#hash-references) 18 | 19 | ## :hash: What is *gnomAD* and the purpose of this script? 20 | [gnomAD (The Genome Aggregation Database)](http://gnomad.broadinstitute.org/) [[1]](#hash-references) is aggregation of thousands of exomes and genomes human sequencing studies. Also, gnomAD consortium annotates the variants with allelic frequency in genomes and exomes. 21 | 22 | **Here**, this API with both CLI and GUI versions is able to search the genes or transcripts of your interest and retrieve variant data from the database via [gnomAD backend API](https://gnomad.broadinstitute.org/api) that based on GraphQL query language. 23 | 24 |
25 | 26 | ## :hash: Update on the repository and `gnomadR` tool 27 | 28 | After the last update on the repository, [gnomAD GraphQL API](https://gnomad.broadinstitute.org/api) has been updated, and the query syntax and most of the keywords were deprecated or altered. Hence, the batch script is currently not able to retrieve the data from the gnomAD API and not able to generate outputs and plots. 29 | 30 | **If you are still looking forward a tool that automates fetching the data from gnomAD and you like R lang, you might check `gnomadR`!** 31 | 32 | gnomadR: Query gnomAD API from R by [Dayne Filer (@daynefiler)](https://github.com/daynefiler) 33 | 34 | https://github.com/daynefiler/gnomadR 35 | 36 | `gnomadR` package intends to provide an interface between R and the [gnomAD](https://gnomad.broadinstitute.org/) API, powered by [GraphQL](https://graphql.org). This package utilizes the [qhql](https://docs.ropensci.org/ghql/) R package to send queries to gnomAD. 37 | 38 |
39 | 40 | --- 41 | 42 | ## :hash: Requirements and Installation 43 | - Create a directory and download the "**gnomad_api_cli.py**" and "**requirements.txt**" files or clone the repository via Git using following command: 44 | 45 | `git clone https://github.com/furkanmtorun/gnomad_python_api.git` 46 | 47 | - Install the required packages if you do not already: 48 | 49 | ` pip3 install -r requirements.txt` 50 | 51 | > The `requirements.txt` contains required libraries for both GUI (graphical user interface) and CLI (command-line interface) versions. 52 | 53 | - It's ready to use now! 54 | 55 | > If you did not install **pip** yet, please follow the instruction [here](https://pip.pypa.io/en/stable/installing/). 56 | 57 | ## :hash: GUI | Usage 58 | 59 | In the GUI version of gnomAD Python API, [Streamlit](https://www.streamlit.io/) has been used. 60 | 61 | > **Note:** In GUI version, it is possible to generate plots from the data retrieved. 62 | > This option is not available in CLI version since it is still under development. 63 | > 64 | > **So, it is recommended to use GUI version.** 65 | 66 | - To use GUI version of gnomAD Python API: 67 | 68 | `streamlit run gnomad_api_gui.py` 69 | 70 | 71 | - Here are the screenshots for the GUI version: 72 | 73 | ![gnomAD Python API GUI](img/main_screen.png) 74 | 75 | _gnomAD Python API GUI - Main Screen_ 76 | 77 | ![gnomAD Python API GUI](img/results.png) 78 | 79 | _gnomAD Python API GUI - Outputs_ 80 | 81 | ![gnomAD Python API GUI](img/results_2.png) 82 | 83 | _gnomAD Python API GUI - Outputs and Plots_ 84 | 85 | > The outputs are also saved into `outputs/` folder in the GUI version. 86 | 87 | ## :hash: CLI | Usage & Options 88 | | Options | Description | Parameters | 89 | |--|--|--| 90 | | -filter_by | *It defines the input type.* |`gene_name`, `gene_id`, `transcript_id`, or `rs_id` | 91 | | -search_by | *It defines the input.* | Type a gene/transcript identifier
*e.g.: TP53, ENSG00000169174, ENST00000544455*
Type the name of file containig your inputs
*e.g: myGenes.txt* 92 | | -dataset | *It defines the dataset.* | `exac`, `gnomad_r2_1`, `gnomad_r3`, `gnomad_r2_1_controls`, `gnomad_r2_1_non_neuro`, `gnomad_r2_1_non_cancer`, or `gnomad_r2_1_non_topmed` 93 | | -sv_dataset | *It defines structural variants dataset.* | `gnomad_sv_r2_1`, `gnomad_sv_r2_1_controls`, or `gnomad_sv_r2_1_non_neuro` 94 | | -reference_genome | *It defines reference genome build.* | `GRCh37` or `GRCh38` 95 | | -h | *It displays the parameters.* | *To get help via script:* `python gnomad_api_cli.py -h` 96 | 97 | 98 | > ❗ Here, for getting variants, `gnomad_r2_1` and `gnomad_sv_r2_1` are defined as default values for these two `-dataset` and `-sv_dataset` options, respectively. 99 | > 100 | > 101 | > ❗ Also, you need to choose `GRCh38` for retrieving variants from the `gnomad_r3` dataset. However, in the `GRCh38` build, structural variants are not available. 102 | 103 | ## :hash: CLI | Example Usages 104 | - **How to list the variants by gene name or gene id?** 105 | 106 | *For gene name:* 107 | 108 | `python gnomad_api_cli.py -filter_by=gene_name -search_by="BRCA1" -dataset="gnomad_r2_1" -sv_dataset="gnomad_sv_r2_1"` 109 | 110 | If you get data from `gnomad_r3`: 111 | 112 | `python gnomad_api_cli.py -filter_by=gene_name -search_by="BRCA1" -dataset="gnomad_r3" -reference_genome="GRCh38"` 113 | 114 | *For Ensembl gene ID* 115 | 116 | `python gnomad_api_cli.py -filter_by=gene_id -search_by="ENSG00000169174" -dataset="gnomad_r2_1" -sv_dataset="gnomad_sv_r2_1"` 117 | 118 | - **How to list the variants by transcript ID?** 119 | 120 | `python gnomad_api_cli.py -filter_by=transcript_id -search_by="ENST00000407236" -dataset="gnomad_r2_1"` 121 | 122 | - **How to get variant info by RS ID (rsId)?** 123 | 124 | `python gnomad_api_cli.py -filter_by=rs_id -search_by="rs201857604" -dataset="gnomad_r2_1"` 125 | 126 | - **How to list the variants using a file containing genes/transcripts?** 127 | 128 | - Prepare your file that contains gene name, Ensembl gene IDs, Ensembl transcript IDs or RS IDs line-by-line. 129 | > ENSG00000169174
ENSG00000171862
ENSG00000170445 130 | 131 | - Then, run the following command: 132 | 133 | `python gnomad_api_cli.py -filter_by="gene_id" -search_by="myFavoriteGenes.txt" -dataset="gnomad_r2_1" -sv_dataset="gnomad_sv_r2_1"` 134 | 135 | > Please, use only one type of identifier in the file. 136 | 137 | - Then, the variants will be listed in "**outputs**" folder in the folders according to their identifier (gene name, gene id, transcript id or rsId). 138 | 139 | - That's all! 140 | 141 | ## :hash: Disclaimer 142 | All the outputs provided by this tool are for informational purposes only. 143 | 144 | The information is not intended to replace any consultation, diagnosis, and/or medical treatment offered by physicians or healthcare providers. 145 | 146 | The author of the app will not be liable for any direct, indirect, consequential, special, exemplary, or other damages arising therefrom. 147 | 148 | ## :hash: Contributing & Feedback 149 | I would be very happy to see any feedback or contributions to the project. 150 | 151 | For problems and enhancement requests, please `open an issue` above. 152 | 153 | ⭐ If you like it, please do not forget give a star! 154 | 155 | ## :hash: Developer 156 | **Furkan M. Torun ([@furkanmtorun](http://github.com/furkanmtorun)) | [furkanmtorun@gmail.com](mailto:furkanmtorun@gmail.com) | 157 | Academia: [Google Scholar Profile](https://scholar.google.com/citations?user=d5ZyOZ4AAAAJ)** 158 | 159 | ## :hash: References 160 | 1. Karczewski, K.J., Francioli, L.C., Tiao, G. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020). https://doi.org/10.1038/s41586-020-2308-7 161 | 162 | 163 | -------------------------------------------------------------------------------- /gnomad_api_cli.py: -------------------------------------------------------------------------------- 1 | # gnomAD Python API by @furkanmtorun 2 | # E-Mail: furkanmtorun@gmail.com 3 | # GitHub: https://github.com/furkanmtorun 4 | # Google Scholar: https://scholar.google.com/citations?user=d5ZyOZ4AAAAJ 5 | # Personal Website : https://furkanmtorun.github.io/ 6 | 7 | # Import required libraries and packages 8 | import warnings 9 | warnings.simplefilter(action='ignore', category=FutureWarning) 10 | from pandas.io.json import json_normalize as json_normalize 11 | from tqdm import tqdm 12 | import pandas as pd 13 | import requests 14 | import argparse 15 | import json 16 | import os 17 | import shutil 18 | import sys 19 | 20 | # Create folders for outputs in the current directory 21 | if not os.path.exists('outputs/'): 22 | os.mkdir('outputs/') 23 | 24 | # Argument Parsing 25 | def arg_parser(): 26 | global filter_by 27 | global search_by 28 | global dataset 29 | global sv_dataset 30 | parser = argparse.ArgumentParser() 31 | parser.add_argument("-filter_by", type=str, required=True, default="gene_name", help="Get your variants according to: `gene_name`, `gene_id, `transcript_id` or `rs_id`.") 32 | parser.add_argument("-search_by", type=str, required=True, default="TP53", help="Type your input for searching or type the file name (e.g: myGenes.txt) containing your inputs") 33 | parser.add_argument("-dataset", type=str, required=True, default="gnomad_r2_1", help="Select your dataset: exac, gnomad_r2_1, gnomad_r3, gnomad_r2_1_controls, gnomad_r2_1_non_neuro, gnomad_r2_1_non_cancer, gnomad_r2_1_non_topmed") 34 | parser.add_argument("-reference_genome", type=str, required=False, default="GRCh37", help="Select a proper reference genome build : `GRCh37` or `GRCh38`") 35 | parser.add_argument("-sv_dataset", type=str, required=False, default="gnomad_sv_r2_1", help="Select your structural variants dataset : `gnomad_sv_r2_1`, `gnomad_sv_r2_1_controls` or `gnomad_sv_r2_1_non_neuro`") 36 | args = parser.parse_args() 37 | 38 | # Control the given arguments 39 | if args.dataset not in ["exac", "gnomad_r2_1", "gnomad_r3", "gnomad_r2_1_controls", "gnomad_r2_1_non_neuro", "gnomad_r2_1_non_cancer", "gnomad_r2_1_non_topmed"]: 40 | sys.exit("! Select a proper gnomAD data set:\n\texac, gnomad_r2_1, gnomad_r3, gnomad_r2_1_controls, gnomad_r2_1_non_neuro, gnomad_r2_1_non_cancer, gnomad_r2_1_non_topmed") 41 | 42 | if args.sv_dataset not in ["gnomad_sv_r2_1", "gnomad_sv_r2_1_controls", "gnomad_sv_r2_1_non_neuro"]: 43 | sys.exit("! Select a proper gnomAD data set:\n\t`gnomad_sv_r2_1`, `gnomad_sv_r2_1_controls` or `gnomad_sv_r2_1_non_neuro`") 44 | 45 | if args.filter_by not in ["gene_name", "gene_id", "transcript_id", "rs_id"]: 46 | sys.exit("! Select a proper filter type :\n\t `gene_name`, `gene_id, `transcript_id` or `rs_id`") 47 | 48 | if args.reference_genome not in ["GRCh37", "GRCh38"]: 49 | sys.exit("! Select a proper reference genome build :\n\t `GRCh37` or `GRCh38`") 50 | 51 | if (args.dataset == "gnomad_r3") and (args.reference_genome == "GRCh37"): 52 | sys.exit("! You need to select `GRCh38` reference genome build for getting data from `gnomad_r3`.") 53 | 54 | # Define variables 55 | filter_by = args.filter_by 56 | search_by = args.search_by 57 | dataset = args.dataset 58 | sv_dataset = args.sv_dataset 59 | reference_genome = args.reference_genome 60 | 61 | return filter_by, search_by, dataset, sv_dataset, reference_genome 62 | 63 | # gnomAD API 64 | end_point = "https://gnomad.broadinstitute.org/api/" 65 | 66 | # Main Function 67 | def get_variants_by(filter_by, search_term, dataset, reference_genome, sv_dataset, timeout=None): 68 | 69 | query_for_transcripts = """ 70 | { 71 | transcript(transcript_id: "%s", reference_genome: %s) { 72 | transcript_id, 73 | transcript_version, 74 | gene { 75 | gene_id, 76 | symbol, 77 | start, 78 | stop, 79 | strand, 80 | chrom, 81 | hgnc_id, 82 | gene_name, 83 | full_gene_name, 84 | omim_id 85 | } 86 | variants(dataset: %s) { 87 | pos 88 | rsid 89 | ref 90 | alt 91 | consequence 92 | genome { 93 | genome_af:af 94 | genome_ac:ac 95 | genome_an:an 96 | genome_ac_hemi:ac_hemi 97 | genome_ac_hom:ac_hom 98 | } 99 | exome { 100 | exome_af:af 101 | exome_ac:ac 102 | exome_an:an 103 | exome_ac_hemi:ac_hemi 104 | exome_ac_hom:ac_hom 105 | } 106 | flags 107 | lof 108 | consequence_in_canonical_transcript 109 | gene_symbol 110 | hgvsc 111 | lof_filter 112 | lof_flags 113 | hgvsc 114 | hgvsp 115 | reference_genome 116 | variant_id: variantId 117 | } 118 | gtex_tissue_expression{ 119 | adipose_subcutaneous, 120 | adipose_visceral_omentum, 121 | adrenal_gland, 122 | artery_aorta, 123 | artery_coronary, 124 | artery_tibial, 125 | bladder, 126 | brain_amygdala, 127 | brain_anterior_cingulate_cortex_ba24, 128 | brain_caudate_basal_ganglia, 129 | brain_cerebellar_hemisphere, 130 | brain_cerebellum, 131 | brain_cortex, 132 | brain_frontal_cortex_ba9, 133 | brain_hippocampus, 134 | brain_hypothalamus, 135 | brain_nucleus_accumbens_basal_ganglia, 136 | brain_putamen_basal_ganglia, 137 | brain_spinal_cord_cervical_c_1, 138 | brain_substantia_nigra, 139 | breast_mammary_tissue, 140 | cells_ebv_transformed_lymphocytes, 141 | cells_transformed_fibroblasts, 142 | cervix_ectocervix, 143 | cervix_endocervix, 144 | colon_sigmoid, 145 | colon_transverse, 146 | esophagus_gastroesophageal_junction, 147 | esophagus_mucosa, 148 | esophagus_muscularis, 149 | fallopian_tube, 150 | heart_atrial_appendage, 151 | heart_left_ventricle, 152 | kidney_cortex, 153 | liver, 154 | lung, 155 | minor_salivary_gland, 156 | muscle_skeletal, 157 | nerve_tibial, 158 | ovary, 159 | pancreas, 160 | pituitary, 161 | prostate, 162 | skin_not_sun_exposed_suprapubic, 163 | skin_sun_exposed_lower_leg, 164 | small_intestine_terminal_ileum, 165 | spleen, 166 | stomach, 167 | testis, 168 | thyroid, 169 | uterus, 170 | vagina, 171 | whole_blood 172 | } 173 | clinvar_variants{ 174 | variant_id, 175 | clinvar_variation_id, 176 | reference_genome, 177 | chrom, 178 | pos, 179 | ref, 180 | alt, 181 | clinical_significance, 182 | gold_stars, 183 | major_consequence, 184 | review_status 185 | } 186 | coverage(dataset: %s){ 187 | genome{ 188 | pos, 189 | mean, 190 | median, 191 | over_1, 192 | over_5, 193 | over_10, 194 | over_15, 195 | over_20, 196 | over_25, 197 | over_30, 198 | over_50, 199 | over_100 200 | } 201 | 202 | exome{ 203 | pos, 204 | mean, 205 | median, 206 | over_1, 207 | over_5, 208 | over_10, 209 | over_15, 210 | over_20, 211 | over_25, 212 | over_30, 213 | over_50, 214 | over_100 215 | } 216 | } 217 | gnomad_constraint{ 218 | exp_lof, 219 | exp_mis, 220 | exp_syn, 221 | obs_lof, 222 | obs_mis, 223 | obs_syn, 224 | oe_lof, 225 | oe_lof_lower, 226 | oe_lof_upper, 227 | oe_mis, 228 | oe_mis_lower, 229 | oe_mis_upper, 230 | oe_syn, 231 | oe_syn_lower, 232 | oe_syn_upper, 233 | lof_z, 234 | mis_z, 235 | syn_z, 236 | pLI, 237 | flags 238 | } 239 | exac_constraint{ 240 | exp_syn, 241 | exp_mis, 242 | exp_lof, 243 | obs_syn, 244 | obs_mis, 245 | obs_lof, 246 | mu_syn, 247 | mu_mis, 248 | mu_lof, 249 | syn_z, 250 | mis_z, 251 | lof_z, 252 | pLI 253 | } 254 | } 255 | } 256 | """ 257 | 258 | query_for_variants = """ 259 | { 260 | variant(%s: "%s", dataset: %s) { 261 | variantId 262 | reference_genome 263 | chrom 264 | pos 265 | ref 266 | alt 267 | colocatedVariants 268 | multiNucleotideVariants { 269 | combined_variant_id 270 | changes_amino_acids 271 | n_individuals 272 | other_constituent_snvs 273 | } 274 | exome { 275 | ac 276 | an 277 | ac_hemi 278 | ac_hom 279 | faf95 { 280 | popmax 281 | popmax_population 282 | } 283 | filters 284 | populations { 285 | id 286 | ac 287 | an 288 | ac_hemi 289 | ac_hom 290 | } 291 | age_distribution { 292 | het { 293 | bin_edges 294 | bin_freq 295 | n_smaller 296 | n_larger 297 | } 298 | hom { 299 | bin_edges 300 | bin_freq 301 | n_smaller 302 | n_larger 303 | } 304 | } 305 | qualityMetrics { 306 | alleleBalance { 307 | alt { 308 | bin_edges 309 | bin_freq 310 | n_smaller 311 | n_larger 312 | } 313 | } 314 | genotypeDepth { 315 | all { 316 | bin_edges 317 | bin_freq 318 | n_smaller 319 | n_larger 320 | } 321 | alt { 322 | bin_edges 323 | bin_freq 324 | n_smaller 325 | n_larger 326 | } 327 | } 328 | genotypeQuality { 329 | all { 330 | bin_edges 331 | bin_freq 332 | n_smaller 333 | n_larger 334 | } 335 | alt { 336 | bin_edges 337 | bin_freq 338 | n_smaller 339 | n_larger 340 | } 341 | } 342 | } 343 | } 344 | genome { 345 | ac 346 | an 347 | ac_hemi 348 | ac_hom 349 | faf95 { 350 | popmax 351 | popmax_population 352 | } 353 | filters 354 | populations { 355 | id 356 | ac 357 | an 358 | ac_hemi 359 | ac_hom 360 | } 361 | age_distribution { 362 | het { 363 | bin_edges 364 | bin_freq 365 | n_smaller 366 | n_larger 367 | } 368 | hom { 369 | bin_edges 370 | bin_freq 371 | n_smaller 372 | n_larger 373 | } 374 | } 375 | qualityMetrics { 376 | alleleBalance { 377 | alt { 378 | bin_edges 379 | bin_freq 380 | n_smaller 381 | n_larger 382 | } 383 | } 384 | genotypeDepth { 385 | all { 386 | bin_edges 387 | bin_freq 388 | n_smaller 389 | n_larger 390 | } 391 | alt { 392 | bin_edges 393 | bin_freq 394 | n_smaller 395 | n_larger 396 | } 397 | } 398 | genotypeQuality { 399 | all { 400 | bin_edges 401 | bin_freq 402 | n_smaller 403 | n_larger 404 | } 405 | alt { 406 | bin_edges 407 | bin_freq 408 | n_smaller 409 | n_larger 410 | } 411 | } 412 | } 413 | } 414 | flags 415 | rsid 416 | sortedTranscriptConsequences { 417 | canonical 418 | gene_id 419 | gene_version 420 | gene_symbol 421 | hgvs 422 | hgvsc 423 | hgvsp 424 | lof 425 | lof_flags 426 | lof_filter 427 | major_consequence 428 | polyphen_prediction 429 | sift_prediction 430 | transcript_id 431 | transcript_version 432 | } 433 | } 434 | 435 | } 436 | """ 437 | 438 | query_for_genes = """ 439 | { 440 | gene(%s: "%s", reference_genome: %s) { 441 | gene_id 442 | symbol 443 | start 444 | stop 445 | strand 446 | chrom 447 | hgnc_id 448 | gene_name 449 | symbol 450 | full_gene_name 451 | reference_genome 452 | omim_id 453 | canonical_transcript_id 454 | 455 | structural_variants(dataset: %s){ 456 | ac, 457 | ac_hom, 458 | an, 459 | af, 460 | reference_genome, 461 | chrom, 462 | chrom2, 463 | end, 464 | end2, 465 | consequence, 466 | filters, 467 | length, 468 | pos, 469 | pos2, 470 | type, 471 | variant_id 472 | } 473 | 474 | variants(dataset: %s) { 475 | pos 476 | rsid 477 | ref 478 | alt 479 | consequence 480 | genome { 481 | genome_af:af 482 | genome_ac:ac 483 | genome_an:an 484 | genome_ac_hemi:ac_hemi 485 | genome_ac_hom:ac_hom 486 | } 487 | exome { 488 | exome_af:af 489 | exome_ac:ac 490 | exome_an:an 491 | exome_ac_hemi:ac_hemi 492 | exome_ac_hom:ac_hom 493 | } 494 | flags 495 | lof 496 | consequence_in_canonical_transcript 497 | gene_symbol 498 | hgvsc 499 | lof_filter 500 | lof_flags 501 | hgvsc 502 | hgvsp 503 | reference_genome 504 | variant_id: variantId 505 | } 506 | 507 | mane_select_transcript{ 508 | ensembl_id 509 | ensembl_version 510 | refseq_id 511 | refseq_version 512 | } 513 | 514 | transcripts{ 515 | reference_genome 516 | gene_id 517 | transcript_id 518 | strand 519 | start 520 | stop 521 | chrom 522 | } 523 | 524 | exac_regional_missense_constraint_regions { 525 | start 526 | stop 527 | obs_mis 528 | exp_mis 529 | obs_exp 530 | chisq_diff_null 531 | } 532 | 533 | clinvar_variants { 534 | variant_id 535 | clinvar_variation_id 536 | reference_genome 537 | chrom 538 | pos 539 | ref 540 | alt 541 | clinical_significance 542 | gold_stars 543 | major_consequence 544 | review_status 545 | } 546 | 547 | coverage(dataset: %s) { 548 | exome { 549 | pos 550 | mean 551 | median 552 | over_1 553 | over_5 554 | over_10 555 | over_15 556 | over_20 557 | over_25 558 | over_30 559 | over_50 560 | over_100 561 | } 562 | genome { 563 | pos 564 | mean 565 | median 566 | over_1 567 | over_5 568 | over_10 569 | over_15 570 | over_20 571 | over_25 572 | over_30 573 | over_50 574 | over_100 575 | } 576 | } 577 | 578 | 579 | gnomad_constraint { 580 | exp_lof 581 | exp_mis 582 | exp_syn 583 | obs_lof 584 | obs_mis 585 | obs_syn 586 | oe_lof 587 | oe_lof_lower 588 | oe_lof_upper 589 | oe_mis 590 | oe_mis_lower 591 | oe_mis_upper 592 | oe_syn 593 | oe_syn_lower 594 | oe_syn_upper 595 | lof_z 596 | mis_z 597 | syn_z 598 | pLI 599 | flags 600 | } 601 | 602 | exac_constraint { 603 | exp_syn 604 | exp_mis 605 | exp_lof 606 | obs_syn 607 | obs_mis 608 | obs_lof 609 | mu_syn 610 | mu_mis 611 | mu_lof 612 | syn_z 613 | mis_z 614 | lof_z 615 | pLI 616 | } 617 | } 618 | } 619 | """ 620 | 621 | if filter_by == "transcript_id": 622 | query = query_for_transcripts % (search_term.upper(), reference_genome, dataset, dataset) 623 | 624 | elif filter_by == "rs_id": 625 | query = query_for_variants % ("rsid", search_term.lower(), dataset) 626 | 627 | elif filter_by == "gene_id": 628 | query = query_for_genes % ("gene_id", search_term.upper(), reference_genome, sv_dataset, dataset, dataset) 629 | 630 | elif filter_by == "gene_name": 631 | query = query_for_genes % ("gene_name", search_term.upper(), reference_genome, sv_dataset, dataset, dataset) 632 | 633 | else: 634 | print("Unknown `filter_by` type!") 635 | 636 | # Get repsonse 637 | response = requests.post(end_point, data={'query': query}, timeout=timeout) 638 | 639 | # Parse response 640 | if response.status_code == 200: 641 | try: 642 | 643 | if filter_by == "transcript_id": 644 | if not os.path.exists('outputs/' + search_term + "/"): 645 | os.mkdir('outputs/'+ search_term + "/") 646 | else: 647 | shutil.rmtree('outputs/'+ search_term + "/") 648 | os.mkdir('outputs/'+ search_term + "/") 649 | json_keys = list(response.json()["data"]["transcript"].keys()) 650 | for json_key in json_keys: 651 | if response.json()["data"]["transcript"][json_key] is not None and type(response.json()["data"]["transcript"][json_key]) not in [str, int]: 652 | data = json_normalize(response.json()["data"]["transcript"][json_key]) 653 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 654 | data.to_csv("outputs/" + search_term + "/" + json_key + ".tsv", sep="\t", index=False) 655 | 656 | elif filter_by == "rs_id": 657 | if not os.path.exists('outputs/' + search_term + "/"): 658 | os.mkdir('outputs/'+ search_term + "/") 659 | else: 660 | shutil.rmtree('outputs/'+ search_term + "/") 661 | os.mkdir('outputs/'+ search_term + "/") 662 | json_keys = list(response.json()["data"]["variant"].keys()) 663 | for json_key in json_keys: 664 | # print(json_key, type(response.json()["data"]["variant"][json_key])) 665 | 666 | # Basic info in `variant` part 667 | if response.json()["data"]["variant"][json_key] is not None and type(response.json()["data"]["variant"][json_key]) in [str, int]: 668 | with open("outputs/" + search_term + "/" + search_term + ".txt", "a") as f: 669 | f.write("\n" + json_key + ":" + str(response.json()["data"]["variant"][json_key])) 670 | 671 | # Other parts rather than `genome` and `exome` 672 | if response.json()["data"]["variant"][json_key] is not None and type(response.json()["data"]["variant"][json_key]) not in [str, int] and json_key not in ["genome", "exome"]: 673 | data = json_normalize(response.json()["data"]["variant"][json_key]) 674 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 675 | data.to_csv("outputs/" + search_term + "/" + json_key + ".tsv", sep="\t", index=False) 676 | 677 | # Deep parsing for nested things in `genome` and `exome` 678 | if json_key in ["genome", "exome"]: 679 | for sub_json_key in list(response.json()["data"]["variant"][json_key].keys()): 680 | # print(json_key, sub_json_key, type(response.json()["data"]["variant"][json_key][sub_json_key])) 681 | 682 | if response.json()["data"]["variant"][json_key][sub_json_key] is not None and type(response.json()["data"]["variant"][json_key][sub_json_key]) in [str, int]: 683 | with open("outputs/" + search_term + "/" + search_term + ".txt", "a") as f: 684 | f.write("\n" + json_key + "_" + sub_json_key + ":" + str(response.json()["data"]["variant"][json_key][sub_json_key])) 685 | 686 | if response.json()["data"]["variant"][json_key][sub_json_key] is not None and type(response.json()["data"]["variant"][json_key][sub_json_key]) not in [str, int]: 687 | data = json_normalize(response.json()["data"]["variant"][json_key][sub_json_key]) 688 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 689 | data.to_csv("outputs/" + search_term + "/" + json_key + "_" + sub_json_key + ".tsv", sep="\t", index=False) 690 | 691 | elif filter_by == "gene_id": 692 | if not os.path.exists('outputs/' + search_term + "/"): 693 | os.mkdir('outputs/'+ search_term + "/") 694 | else: 695 | shutil.rmtree('outputs/'+ search_term + "/") 696 | os.mkdir('outputs/'+ search_term + "/") 697 | 698 | json_keys = list(response.json()["data"]["gene"].keys()) 699 | for json_key in json_keys: 700 | # print(json_key, type(response.json()["data"]["gene"][json_key]), response.json()["data"]["gene"][json_key] is None, type(response.json()["data"]["gene"][json_key]) not in [str, int]) 701 | if response.json()["data"]["gene"][json_key] is not None and type(response.json()["data"]["gene"][json_key]) in [str, int]: 702 | with open("outputs/" + search_term + "/" + search_term + ".txt", "a") as f: 703 | f.write("\n" + json_key + ":" + str(response.json()["data"]["gene"][json_key])) 704 | 705 | if response.json()["data"]["gene"][json_key] is not None and type(response.json()["data"]["gene"][json_key]) not in [str, int]: 706 | data = json_normalize(response.json()["data"]["gene"][json_key]) 707 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 708 | data.to_csv("outputs/" + search_term + "/" + json_key + ".tsv", sep="\t", index=False) 709 | 710 | elif filter_by == "gene_name": 711 | if not os.path.exists('outputs/' + search_term + "/"): 712 | os.mkdir('outputs/'+ search_term + "/") 713 | else: 714 | shutil.rmtree('outputs/'+ search_term + "/") 715 | os.mkdir('outputs/'+ search_term + "/") 716 | 717 | json_keys = list(response.json()["data"]["gene"].keys()) 718 | for json_key in json_keys: 719 | # print(json_key, type(response.json()["data"]["gene"][json_key]), response.json()["data"]["gene"][json_key] is None, type(response.json()["data"]["gene"][json_key]) not in [str, int]) 720 | if response.json()["data"]["gene"][json_key] is not None and type(response.json()["data"]["gene"][json_key]) in [str, int]: 721 | with open("outputs/" + search_term + "/" + search_term + ".txt", "a") as f: 722 | f.write("\n" + json_key + ":" + str(response.json()["data"]["gene"][json_key])) 723 | 724 | if response.json()["data"]["gene"][json_key] is not None and type(response.json()["data"]["gene"][json_key]) not in [str, int]: 725 | data = json_normalize(response.json()["data"]["gene"][json_key]) 726 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 727 | data.to_csv("outputs/" + search_term + "/" + json_key + ".tsv", sep="\t", index=False) 728 | 729 | except (ConnectionError, ConnectionAbortedError, ConnectionRefusedError, ConnectionResetError): 730 | sys.exit("An unknown error occured regarding the internet connection!") 731 | 732 | except AttributeError as ae: 733 | # Error Message from gnomAD 734 | try: 735 | for msg in response.json()["errors"]: 736 | sys.exit("Errors from gnomAD for your process:\n\t" + msg["message"]) 737 | except Exception as anyOtherException: 738 | pass 739 | 740 | if filter_by != "rs_id": 741 | # General Error Message 742 | print(""" 743 | It might be caused since the search did not find a result from the database. 744 | Try to check the `input` for `{}` or other `options`. 745 | """.format(filter_by)) 746 | 747 | # Technical Error Message 748 | print(""" 749 | > As a note, technical reason is `{}`. 750 | > 751 | > If you think this should not occur, you can contact with developer to issue this problem on Github page. 752 | """.format(ae)) 753 | 754 | except (TypeError, KeyError): 755 | try: 756 | for msg in response.json()["errors"]: 757 | print("Errors from gnomAD for your process:\n\t" + msg["message"]) 758 | except Exception as anyOtherException: 759 | pass 760 | 761 | else: 762 | print(" ! DONE: Check out the 'outputs/' folder") 763 | 764 | elif response.status_code == 404: 765 | sys.exit('API is not accessible right now. Check the end point out!') 766 | 767 | # Action 768 | if __name__ == "__main__": 769 | filter_by, search_by, dataset, sv_dataset, reference_genome = arg_parser() 770 | if "." in search_by: 771 | try: 772 | with open(search_by, "r") as f: 773 | search_list = [line.rstrip() for line in f] 774 | for search_item in tqdm(search_list): 775 | get_variants_by(filter_by, search_item, dataset, reference_genome, sv_dataset) 776 | except: 777 | print("A problem occured while reading the file namely `{}` or the filter type `{}` is wrong!"\ 778 | .format(search_by, filter_by)) 779 | finally: 780 | f.close() 781 | elif "." not in search_by: 782 | get_variants_by(filter_by, search_by, dataset, reference_genome, sv_dataset) 783 | -------------------------------------------------------------------------------- /gnomad_api_gui.py: -------------------------------------------------------------------------------- 1 | # gnomAD Python API by @furkanmtorun 2 | # E-Mail: furkanmtorun@gmail.com 3 | # GitHub: https://github.com/furkanmtorun 4 | # Google Scholar: https://scholar.google.com/citations?user=d5ZyOZ4AAAAJ 5 | # Personal Website : https://furkanmtorun.github.io/ 6 | 7 | # Import required libraries and packages 8 | import warnings 9 | warnings.simplefilter(action='ignore', category=FutureWarning) 10 | from pandas.io.json import json_normalize as json_normalize 11 | import plotly.express as px 12 | import streamlit as st 13 | import pandas as pd 14 | import requests 15 | import json 16 | import os 17 | import shutil 18 | 19 | # Create folders for outputs in the current directory 20 | if not os.path.exists('outputs/'): 21 | os.mkdir('outputs/') 22 | 23 | # gnomAD API 24 | end_point = "https://gnomad.broadinstitute.org/api/" 25 | 26 | # Welcome 27 | main_external_css = """ 28 | 31 | """ 32 | version = "v1.0" 33 | 34 | st.markdown(main_external_css, unsafe_allow_html=True) 35 | st.title("🧬 gnomAD Python API {}".format(version)) 36 | 37 | st.markdown(""" 38 | 39 | > - gnomAD Python API {} by **Furkan M. Torun** 40 | > - E-Mail: [furkanmtorun@gmail.com](mailto:furkanmtorun@gmail.com) 41 | > - GitHub: https://github.com/furkanmtorun 42 | > - Google Scholar: https://scholar.google.com/citations?user=d5ZyOZ4AAAAJ 43 | > - Personal Website : https://furkanmtorun.github.io/ 44 | > - ⭐ If you like it, please do not forget give a star on [GitHub](https://github.com/furkanmtorun/gnomad_python_api)! 45 | 46 | --- 47 | """.format(version)) 48 | 49 | st.info(""" 50 | - This API tool uses [gnomAD GraphQL backend service](https://gnomad.broadinstitute.org/api). 51 | - Upload your .csv/tsv/txt file containing the single type of identifiers as one column. 52 | - Each row should correspond to single record (i.e. gene name, gene ID, transcript ID or rsID). 53 | - By using the app, you agree that you accepting [the disclaimer](https://github.com/furkanmtorun/gnomad_python_api#hash-disclaimer). 54 | """) 55 | 56 | # File - content uploading 57 | @st.cache(persist=True) 58 | def upload_file(file_buffer): 59 | df = pd.DataFrame() 60 | if file_buffer is not None: 61 | try: 62 | df = pd.read_csv(file_buffer, sep=",", header=None, names=["search_term"]) 63 | except: 64 | df = pd.read_csv(file_buffer, sep='\t', header=None, names=["search_term"]) 65 | return df 66 | 67 | # Input 68 | st.subheader("Set the input") 69 | file_buffer = st.file_uploader("Upload your search list below as *.csv, *.tsv or *.txt (without header):", type=["csv", "tsv", "txt"]) 70 | 71 | if file_buffer is not None: 72 | search_df = upload_file(file_buffer) 73 | search_by = search_df 74 | st.text("Here is your file:") 75 | st.dataframe(search_df) 76 | if file_buffer is None: 77 | single_search = st.text_input("Or write a single ID here:", value="TP53") 78 | search_by = single_search 79 | 80 | # Input type 81 | st.subheader("Select the input type") 82 | filter_by = st.selectbox("Select a proper input type", ["gene_name", "gene_id", "transcript_id", "rs_id"]) 83 | 84 | # Datasets 85 | st.subheader("Choose the source for dataset") 86 | dataset = st.selectbox("Select a proper gnomAD data set:", ["gnomad_r2_1", "gnomad_r3", "gnomad_r2_1_controls", "gnomad_r2_1_non_neuro", "gnomad_r2_1_non_cancer", "gnomad_r2_1_non_topmed", "exac"]) 87 | 88 | # Reference Genome 89 | st.subheader("Choose the reference genome") 90 | reference_genome = st.selectbox("Select a proper reference genome build:", ["GRCh37", "GRCh38"]) 91 | 92 | if reference_genome == "GRCh38": 93 | st.warning("gnomAD structural variant (SV) data might not available on reference genome `GRCh38`.") 94 | 95 | # SV Dataset 96 | if filter_by in ["gene_id", "gene_name"]: 97 | st.subheader("Choose the source for structural variant (SV) dataset") 98 | sv_dataset = st.selectbox("Select a proper SV gnomAD data set:", ["gnomad_sv_r2_1", "gnomad_sv_r2_1_controls", "gnomad_sv_r2_1_non_neuro"]) 99 | 100 | # Main Function for Getting Data and Saving them 101 | def get_variants_by(filter_by, search_term, dataset, mode, timeout=None): 102 | 103 | query_for_transcripts = """ 104 | { 105 | transcript(transcript_id: "%s", reference_genome: %s) { 106 | transcript_id, 107 | transcript_version, 108 | gene { 109 | gene_id, 110 | symbol, 111 | start, 112 | stop, 113 | strand, 114 | chrom, 115 | hgnc_id, 116 | gene_name, 117 | full_gene_name, 118 | omim_id 119 | } 120 | variants(dataset: %s) { 121 | pos 122 | rsid 123 | ref 124 | alt 125 | consequence 126 | genome { 127 | genome_af:af 128 | genome_ac:ac 129 | genome_an:an 130 | genome_ac_hemi:ac_hemi 131 | genome_ac_hom:ac_hom 132 | } 133 | exome { 134 | exome_af:af 135 | exome_ac:ac 136 | exome_an:an 137 | exome_ac_hemi:ac_hemi 138 | exome_ac_hom:ac_hom 139 | } 140 | flags 141 | lof 142 | consequence_in_canonical_transcript 143 | gene_symbol 144 | hgvsc 145 | lof_filter 146 | lof_flags 147 | hgvsc 148 | hgvsp 149 | reference_genome 150 | variant_id: variantId 151 | } 152 | gtex_tissue_expression{ 153 | adipose_subcutaneous, 154 | adipose_visceral_omentum, 155 | adrenal_gland, 156 | artery_aorta, 157 | artery_coronary, 158 | artery_tibial, 159 | bladder, 160 | brain_amygdala, 161 | brain_anterior_cingulate_cortex_ba24, 162 | brain_caudate_basal_ganglia, 163 | brain_cerebellar_hemisphere, 164 | brain_cerebellum, 165 | brain_cortex, 166 | brain_frontal_cortex_ba9, 167 | brain_hippocampus, 168 | brain_hypothalamus, 169 | brain_nucleus_accumbens_basal_ganglia, 170 | brain_putamen_basal_ganglia, 171 | brain_spinal_cord_cervical_c_1, 172 | brain_substantia_nigra, 173 | breast_mammary_tissue, 174 | cells_ebv_transformed_lymphocytes, 175 | cells_transformed_fibroblasts, 176 | cervix_ectocervix, 177 | cervix_endocervix, 178 | colon_sigmoid, 179 | colon_transverse, 180 | esophagus_gastroesophageal_junction, 181 | esophagus_mucosa, 182 | esophagus_muscularis, 183 | fallopian_tube, 184 | heart_atrial_appendage, 185 | heart_left_ventricle, 186 | kidney_cortex, 187 | liver, 188 | lung, 189 | minor_salivary_gland, 190 | muscle_skeletal, 191 | nerve_tibial, 192 | ovary, 193 | pancreas, 194 | pituitary, 195 | prostate, 196 | skin_not_sun_exposed_suprapubic, 197 | skin_sun_exposed_lower_leg, 198 | small_intestine_terminal_ileum, 199 | spleen, 200 | stomach, 201 | testis, 202 | thyroid, 203 | uterus, 204 | vagina, 205 | whole_blood 206 | } 207 | clinvar_variants{ 208 | variant_id, 209 | clinvar_variation_id, 210 | reference_genome, 211 | chrom, 212 | pos, 213 | ref, 214 | alt, 215 | clinical_significance, 216 | gold_stars, 217 | major_consequence, 218 | review_status 219 | } 220 | coverage(dataset: %s){ 221 | genome{ 222 | pos, 223 | mean, 224 | median, 225 | over_1, 226 | over_5, 227 | over_10, 228 | over_15, 229 | over_20, 230 | over_25, 231 | over_30, 232 | over_50, 233 | over_100 234 | } 235 | 236 | exome{ 237 | pos, 238 | mean, 239 | median, 240 | over_1, 241 | over_5, 242 | over_10, 243 | over_15, 244 | over_20, 245 | over_25, 246 | over_30, 247 | over_50, 248 | over_100 249 | } 250 | } 251 | gnomad_constraint{ 252 | exp_lof, 253 | exp_mis, 254 | exp_syn, 255 | obs_lof, 256 | obs_mis, 257 | obs_syn, 258 | oe_lof, 259 | oe_lof_lower, 260 | oe_lof_upper, 261 | oe_mis, 262 | oe_mis_lower, 263 | oe_mis_upper, 264 | oe_syn, 265 | oe_syn_lower, 266 | oe_syn_upper, 267 | lof_z, 268 | mis_z, 269 | syn_z, 270 | pLI, 271 | flags 272 | } 273 | exac_constraint{ 274 | exp_syn, 275 | exp_mis, 276 | exp_lof, 277 | obs_syn, 278 | obs_mis, 279 | obs_lof, 280 | mu_syn, 281 | mu_mis, 282 | mu_lof, 283 | syn_z, 284 | mis_z, 285 | lof_z, 286 | pLI 287 | } 288 | } 289 | } 290 | """ 291 | 292 | query_for_variants = """ 293 | { 294 | variant(%s: "%s", dataset: %s) { 295 | variantId 296 | reference_genome 297 | chrom 298 | pos 299 | ref 300 | alt 301 | colocatedVariants 302 | multiNucleotideVariants { 303 | combined_variant_id 304 | changes_amino_acids 305 | n_individuals 306 | other_constituent_snvs 307 | } 308 | exome { 309 | ac 310 | an 311 | ac_hemi 312 | ac_hom 313 | faf95 { 314 | popmax 315 | popmax_population 316 | } 317 | filters 318 | populations { 319 | id 320 | ac 321 | an 322 | ac_hemi 323 | ac_hom 324 | } 325 | age_distribution { 326 | het { 327 | bin_edges 328 | bin_freq 329 | n_smaller 330 | n_larger 331 | } 332 | hom { 333 | bin_edges 334 | bin_freq 335 | n_smaller 336 | n_larger 337 | } 338 | } 339 | qualityMetrics { 340 | alleleBalance { 341 | alt { 342 | bin_edges 343 | bin_freq 344 | n_smaller 345 | n_larger 346 | } 347 | } 348 | genotypeDepth { 349 | all { 350 | bin_edges 351 | bin_freq 352 | n_smaller 353 | n_larger 354 | } 355 | alt { 356 | bin_edges 357 | bin_freq 358 | n_smaller 359 | n_larger 360 | } 361 | } 362 | genotypeQuality { 363 | all { 364 | bin_edges 365 | bin_freq 366 | n_smaller 367 | n_larger 368 | } 369 | alt { 370 | bin_edges 371 | bin_freq 372 | n_smaller 373 | n_larger 374 | } 375 | } 376 | } 377 | } 378 | genome { 379 | ac 380 | an 381 | ac_hemi 382 | ac_hom 383 | faf95 { 384 | popmax 385 | popmax_population 386 | } 387 | filters 388 | populations { 389 | id 390 | ac 391 | an 392 | ac_hemi 393 | ac_hom 394 | } 395 | age_distribution { 396 | het { 397 | bin_edges 398 | bin_freq 399 | n_smaller 400 | n_larger 401 | } 402 | hom { 403 | bin_edges 404 | bin_freq 405 | n_smaller 406 | n_larger 407 | } 408 | } 409 | qualityMetrics { 410 | alleleBalance { 411 | alt { 412 | bin_edges 413 | bin_freq 414 | n_smaller 415 | n_larger 416 | } 417 | } 418 | genotypeDepth { 419 | all { 420 | bin_edges 421 | bin_freq 422 | n_smaller 423 | n_larger 424 | } 425 | alt { 426 | bin_edges 427 | bin_freq 428 | n_smaller 429 | n_larger 430 | } 431 | } 432 | genotypeQuality { 433 | all { 434 | bin_edges 435 | bin_freq 436 | n_smaller 437 | n_larger 438 | } 439 | alt { 440 | bin_edges 441 | bin_freq 442 | n_smaller 443 | n_larger 444 | } 445 | } 446 | } 447 | } 448 | flags 449 | rsid 450 | sortedTranscriptConsequences { 451 | canonical 452 | gene_id 453 | gene_version 454 | gene_symbol 455 | hgvs 456 | hgvsc 457 | hgvsp 458 | lof 459 | lof_flags 460 | lof_filter 461 | major_consequence 462 | polyphen_prediction 463 | sift_prediction 464 | transcript_id 465 | transcript_version 466 | } 467 | } 468 | 469 | } 470 | """ 471 | 472 | query_for_genes = """ 473 | { 474 | gene(%s: "%s", reference_genome: %s) { 475 | gene_id 476 | symbol 477 | start 478 | stop 479 | strand 480 | chrom 481 | hgnc_id 482 | gene_name 483 | symbol 484 | full_gene_name 485 | reference_genome 486 | omim_id 487 | canonical_transcript_id 488 | 489 | structural_variants(dataset: %s){ 490 | ac, 491 | ac_hom, 492 | an, 493 | af, 494 | reference_genome, 495 | chrom, 496 | chrom2, 497 | end, 498 | end2, 499 | consequence, 500 | filters, 501 | length, 502 | pos, 503 | pos2, 504 | type, 505 | variant_id 506 | } 507 | 508 | variants(dataset: %s) { 509 | pos 510 | rsid 511 | ref 512 | alt 513 | consequence 514 | genome { 515 | genome_af:af 516 | genome_ac:ac 517 | genome_an:an 518 | genome_ac_hemi:ac_hemi 519 | genome_ac_hom:ac_hom 520 | } 521 | exome { 522 | exome_af:af 523 | exome_ac:ac 524 | exome_an:an 525 | exome_ac_hemi:ac_hemi 526 | exome_ac_hom:ac_hom 527 | } 528 | flags 529 | lof 530 | consequence_in_canonical_transcript 531 | gene_symbol 532 | hgvsc 533 | lof_filter 534 | lof_flags 535 | hgvsc 536 | hgvsp 537 | reference_genome 538 | variant_id: variantId 539 | } 540 | 541 | mane_select_transcript{ 542 | ensembl_id 543 | ensembl_version 544 | refseq_id 545 | refseq_version 546 | } 547 | 548 | transcripts{ 549 | reference_genome 550 | gene_id 551 | transcript_id 552 | strand 553 | start 554 | stop 555 | chrom 556 | } 557 | 558 | exac_regional_missense_constraint_regions { 559 | start 560 | stop 561 | obs_mis 562 | exp_mis 563 | obs_exp 564 | chisq_diff_null 565 | } 566 | 567 | clinvar_variants { 568 | variant_id 569 | clinvar_variation_id 570 | reference_genome 571 | chrom 572 | pos 573 | ref 574 | alt 575 | clinical_significance 576 | gold_stars 577 | major_consequence 578 | review_status 579 | } 580 | 581 | coverage(dataset: %s) { 582 | exome { 583 | pos 584 | mean 585 | median 586 | over_1 587 | over_5 588 | over_10 589 | over_15 590 | over_20 591 | over_25 592 | over_30 593 | over_50 594 | over_100 595 | } 596 | genome { 597 | pos 598 | mean 599 | median 600 | over_1 601 | over_5 602 | over_10 603 | over_15 604 | over_20 605 | over_25 606 | over_30 607 | over_50 608 | over_100 609 | } 610 | } 611 | 612 | 613 | gnomad_constraint { 614 | exp_lof 615 | exp_mis 616 | exp_syn 617 | obs_lof 618 | obs_mis 619 | obs_syn 620 | oe_lof 621 | oe_lof_lower 622 | oe_lof_upper 623 | oe_mis 624 | oe_mis_lower 625 | oe_mis_upper 626 | oe_syn 627 | oe_syn_lower 628 | oe_syn_upper 629 | lof_z 630 | mis_z 631 | syn_z 632 | pLI 633 | flags 634 | } 635 | 636 | exac_constraint { 637 | exp_syn 638 | exp_mis 639 | exp_lof 640 | obs_syn 641 | obs_mis 642 | obs_lof 643 | mu_syn 644 | mu_mis 645 | mu_lof 646 | syn_z 647 | mis_z 648 | lof_z 649 | pLI 650 | } 651 | } 652 | } 653 | """ 654 | 655 | if filter_by == "transcript_id": 656 | query = query_for_transcripts % (search_term.upper(), reference_genome, dataset, dataset) 657 | 658 | elif filter_by == "rs_id": 659 | query = query_for_variants % ("rsid", search_term.lower(), dataset) 660 | 661 | elif filter_by == "gene_id": 662 | query = query_for_genes % ("gene_id", search_term.upper(), reference_genome, sv_dataset, dataset, dataset) 663 | 664 | elif filter_by == "gene_name": 665 | query = query_for_genes % ("gene_name", search_term.upper(), reference_genome, sv_dataset, dataset, dataset) 666 | 667 | else: 668 | print("Unknown `filter_by` type!") 669 | 670 | # Get repsonse 671 | global response 672 | response = requests.post(end_point, data={'query': query}, timeout=timeout) 673 | 674 | # Parse response 675 | if response.status_code == 200: 676 | 677 | st.markdown("---") 678 | st.subheader("Outputs for `{}` is being prepared.".format(search_term)) 679 | st.markdown("\n") 680 | 681 | if filter_by == "transcript_id": 682 | if not os.path.exists('outputs/' + search_term + "/"): 683 | os.mkdir('outputs/'+ search_term + "/") 684 | else: 685 | shutil.rmtree('outputs/'+ search_term + "/") 686 | os.mkdir('outputs/'+ search_term + "/") 687 | json_keys = list(response.json()["data"]["transcript"].keys()) 688 | for json_key in json_keys: 689 | if response.json()["data"]["transcript"][json_key] is not None and type(response.json()["data"]["transcript"][json_key]) not in [str, int]: 690 | data = json_normalize(response.json()["data"]["transcript"][json_key]) 691 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 692 | data.to_csv("outputs/" + search_term + "/" + json_key + ".tsv", sep="\t", index=False) 693 | if (len(data) > 0) and (mode == "single"): 694 | st.markdown("\n **Table for: `" + json_key + "`**") 695 | st.dataframe(data) 696 | 697 | elif filter_by == "rs_id": 698 | if not os.path.exists('outputs/' + search_term + "/"): 699 | os.mkdir('outputs/'+ search_term + "/") 700 | else: 701 | shutil.rmtree('outputs/'+ search_term + "/") 702 | os.mkdir('outputs/'+ search_term + "/") 703 | json_keys = list(response.json()["data"]["variant"].keys()) 704 | 705 | general_info = "```" 706 | for json_key in json_keys: 707 | # print(json_key, type(response.json()["data"]["variant"][json_key])) 708 | 709 | # Basic info in `variant` part 710 | if response.json()["data"]["variant"][json_key] is not None and type(response.json()["data"]["variant"][json_key]) in [str, int]: 711 | with open("outputs/" + search_term + "/" + search_term + ".txt", "a") as f: 712 | f.write("\n" + json_key + ":" + str(response.json()["data"]["variant"][json_key])) 713 | general_info += "\n" + json_key + ":" + str(response.json()["data"]["variant"][json_key]) 714 | # Other parts rather than `genome` and `exome` 715 | if response.json()["data"]["variant"][json_key] is not None and type(response.json()["data"]["variant"][json_key]) not in [str, int] and json_key not in ["genome", "exome"]: 716 | data = json_normalize(response.json()["data"]["variant"][json_key]) 717 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 718 | data.to_csv("outputs/" + search_term + "/" + json_key + ".tsv", sep="\t", index=False) 719 | if (len(data) > 0) and (mode == "single"): 720 | st.markdown("\n **Table for: `" + json_key + "`**") 721 | st.dataframe(data) 722 | 723 | # Deep parsing for nested things in `genome` and `exome` 724 | if json_key in ["genome", "exome"]: 725 | for sub_json_key in list(response.json()["data"]["variant"][json_key].keys()): 726 | # print(json_key, sub_json_key, type(response.json()["data"]["variant"][json_key][sub_json_key])) 727 | 728 | if response.json()["data"]["variant"][json_key][sub_json_key] is not None and type(response.json()["data"]["variant"][json_key][sub_json_key]) in [str, int]: 729 | with open("outputs/" + search_term + "/" + search_term + ".txt", "a") as f: 730 | f.write("\n" + json_key + "_" + sub_json_key + ":" + str(response.json()["data"]["variant"][json_key][sub_json_key])) 731 | general_info += "\n" + json_key + "_" + sub_json_key + ":" + str(response.json()["data"]["variant"][json_key][sub_json_key]) 732 | 733 | if response.json()["data"]["variant"][json_key][sub_json_key] is not None and type(response.json()["data"]["variant"][json_key][sub_json_key]) not in [str, int]: 734 | data = json_normalize(response.json()["data"]["variant"][json_key][sub_json_key]) 735 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 736 | data.to_csv("outputs/" + search_term + "/" + json_key + "_" + sub_json_key + ".tsv", sep="\t", index=False) 737 | if (len(data) > 0) and (mode == "single"): 738 | st.markdown("\n **Table for: `" + sub_json_key + "`**") 739 | st.dataframe(data) 740 | 741 | general_info += "```" 742 | if mode == "single": 743 | st.markdown("--- \n **General Info for your query**") 744 | st.info(general_info) 745 | 746 | elif filter_by == "gene_id": 747 | if not os.path.exists('outputs/' + search_term + "/"): 748 | os.mkdir('outputs/'+ search_term + "/") 749 | else: 750 | shutil.rmtree('outputs/'+ search_term + "/") 751 | os.mkdir('outputs/'+ search_term + "/") 752 | 753 | json_keys = list(response.json()["data"]["gene"].keys()) 754 | general_info ="```" 755 | for json_key in json_keys: 756 | # print(json_key, type(response.json()["data"]["gene"][json_key]), response.json()["data"]["gene"][json_key] is None, type(response.json()["data"]["gene"][json_key]) not in [str, int]) 757 | if response.json()["data"]["gene"][json_key] is not None and type(response.json()["data"]["gene"][json_key]) in [str, int]: 758 | with open("outputs/" + search_term + "/" + search_term + ".txt", "a") as f: 759 | f.write("\n" + json_key + ":" + str(response.json()["data"]["gene"][json_key])) 760 | general_info += "\n" + json_key + ":" + str(response.json()["data"]["gene"][json_key]) 761 | 762 | if response.json()["data"]["gene"][json_key] is not None and type(response.json()["data"]["gene"][json_key]) not in [str, int]: 763 | data = json_normalize(response.json()["data"]["gene"][json_key]) 764 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 765 | data.to_csv("outputs/" + search_term + "/" + json_key + ".tsv", sep="\t", index=False) 766 | if (len(data) > 0) and (mode == "single"): 767 | st.markdown("\n **Table for: `" + json_key + "`**") 768 | st.dataframe(data) 769 | 770 | general_info += "```" 771 | if mode == "single": 772 | st.markdown("--- \n **General Info for your query**") 773 | st.info(general_info) 774 | 775 | elif filter_by == "gene_name": 776 | if not os.path.exists('outputs/' + search_term + "/"): 777 | os.mkdir('outputs/'+ search_term + "/") 778 | else: 779 | shutil.rmtree('outputs/'+ search_term + "/") 780 | os.mkdir('outputs/'+ search_term + "/") 781 | 782 | json_keys = list(response.json()["data"]["gene"].keys()) 783 | general_info ="```" 784 | for json_key in json_keys: 785 | # print(json_key, type(response.json()["data"]["gene"][json_key]), response.json()["data"]["gene"][json_key] is None, type(response.json()["data"]["gene"][json_key]) not in [str, int]) 786 | if response.json()["data"]["gene"][json_key] is not None and type(response.json()["data"]["gene"][json_key]) in [str, int]: 787 | with open("outputs/" + search_term + "/" + search_term + ".txt", "a") as f: 788 | f.write("\n" + json_key + ": " + str(response.json()["data"]["gene"][json_key])) 789 | general_info += ("\n" + json_key + ": " + str(response.json()["data"]["gene"][json_key])) 790 | 791 | if response.json()["data"]["gene"][json_key] is not None and type(response.json()["data"]["gene"][json_key]) not in [str, int]: 792 | data = json_normalize(response.json()["data"]["gene"][json_key]) 793 | data.columns = data.columns.map(lambda x: x.split(".")[-1]) 794 | data.to_csv("outputs/" + search_term + "/" + json_key + ".tsv", sep="\t", index=False) 795 | if (len(data) > 0) and (mode == "single"): 796 | st.markdown("\n **Table for: `" + json_key + "`**") 797 | st.dataframe(data) 798 | 799 | general_info += "```" 800 | if mode == "single": 801 | st.markdown("--- \n **General Info for your query**") 802 | st.info(general_info) 803 | 804 | return response 805 | 806 | # Plotting 807 | ## Generate grouping and freq plot 808 | def make_grouping_freq_plot(df, group_by, title): 809 | df[group_by] = df[group_by].str.replace("_", " ").str.title() 810 | df2 = df.groupby(group_by).size().reset_index(name='Number of Variants') 811 | fig = px.bar(df2, x=group_by, y="Number of Variants", color=group_by, barmode='stack', template="ggplot2", 812 | hover_data=[group_by]).for_each_trace(lambda t: t.update(name=t.name.split("=")[1])) 813 | fig.update_layout( 814 | title_text=title, 815 | xaxis_tickangle=-45, 816 | yaxis=dict(title='Variants', titlefont_size=14, tickfont_size=12), 817 | xaxis=dict( 818 | title="Categories of {}".format(group_by.replace("_", " ").title()), 819 | titlefont_size=14, 820 | tickfont_size=12)) 821 | return fig 822 | 823 | def generate_plot(search_by, filter_by, mode): 824 | st.subheader("Plots \n ") 825 | try: 826 | if filter_by in ["gene_name", "gene_id", "transcript_id"]: 827 | variant_file = "./outputs/" + search_by + "/variants.tsv" 828 | 829 | if os.path.isfile(variant_file): 830 | ## gnomad 831 | variants_df = pd.read_csv(variant_file, sep="\t") 832 | fig1 = make_grouping_freq_plot(variants_df, "consequence", 'Consequence of gnomAD Variants') 833 | fig2 = make_grouping_freq_plot(variants_df, "lof", 'LoF of gnomAD Variants') 834 | fig3 = make_grouping_freq_plot(variants_df, "lof_filter", 'LoF Filtes of gnomAD Variants') 835 | 836 | ## clinvar 837 | clivar_df = pd.read_csv("./outputs/" + search_by + "/clinvar_variants.tsv", sep="\t") 838 | clivar_df["clinical_significance"] = clivar_df["clinical_significance"].apply(lambda x: x.split("'")[1]) 839 | fig4 = make_grouping_freq_plot(clivar_df, "clinical_significance", 'Clinical Significance of ClinVar Variants') 840 | fig5 = make_grouping_freq_plot(clivar_df, "major_consequence", 'Major Consequence of ClinVar Variants') 841 | 842 | # Show in the app 843 | if mode == "single": 844 | st.plotly_chart(fig1) 845 | st.plotly_chart(fig2) 846 | st.plotly_chart(fig3) 847 | st.plotly_chart(fig4) 848 | st.plotly_chart(fig5) 849 | 850 | # Export as HTML 851 | fig1.write_html("./outputs/" + search_by + "/gnomAD_variants_by_consequence.html") 852 | fig2.write_html("./outputs/" + search_by + "/gnomAD_variants_by_lof.html") 853 | fig3.write_html("./outputs/" + search_by + "/gnomAD_variants_by_lof_filter.html") 854 | fig4.write_html("./outputs/" + search_by + "/clinvar_variants_by_clinical_significance.html") 855 | fig5.write_html("./outputs/" + search_by + "/clinvar_variants_by_major_consequence.html") 856 | else: 857 | st.warning("Plots were not generated since `variants.tsv` could not be created. It may happens if the data is not available for your dataset") 858 | 859 | if filter_by in ["gene_name", "gene_id"]: 860 | structural_variants_file = "./outputs/" + search_by + "/structural_variants.tsv" 861 | if os.path.isfile(variant_file): 862 | ## structural_variants 863 | sv_df = pd.read_csv(structural_variants_file, sep="\t") 864 | fig6 = make_grouping_freq_plot(sv_df, "consequence", 'Major Consequence of Structural Variants') 865 | 866 | # Show in the app 867 | if mode == "single": 868 | st.plotly_chart(fig6) 869 | 870 | # Export as HTML 871 | fig6.write_html("./outputs/" + search_by + "/structural_variants_by_consequence.html") 872 | else: 873 | st.warning("Plots were not generated since `structural_variants.tsv` could not be created. It may happens if the data is not available for your dataset") 874 | 875 | except Exception as plotError: 876 | # st.text(plotError) 877 | pass 878 | 879 | # Action 880 | if (filter_by is not None) and (search_by is not None) and (st.button('Get Data and Generate Plots', key='run')): 881 | try: 882 | if file_buffer is None: 883 | # Single 884 | with st.spinner('Getting data and generating the plots ...'): 885 | response = get_variants_by(filter_by, search_by, dataset, "single") 886 | if response.status_code in [404, 405, 503]: 887 | st.error('API is not accessible right now. Check the end point out for gnomAD API!') 888 | st.markdown(""" 889 | > For techinal detail, status code is `{}` and 890 | > 891 | > current end point is `{}`. 892 | """.format(response.status_code, end_point)) 893 | 894 | else: 895 | generate_plot(search_by, filter_by, "single") 896 | st.markdown("\n --- \n") 897 | st.success("DONE! Check your `outputs/` folder.") 898 | 899 | elif file_buffer is not None: 900 | # Multiple 901 | with st.spinner('Getting data and will back soon...'): 902 | for i, search_item in search_df.iterrows(): 903 | response = get_variants_by(filter_by, search_item[0], dataset, "multiple") 904 | 905 | if response.status_code in [404, 405, 503]: 906 | st.error('API is not accessible right now. Check the end point out for gnomAD API!') 907 | st.markdown(""" 908 | > For techinal detail, status code is `{}` and 909 | > 910 | > current end point is `{}`. 911 | """.format(response.status_code, end_point)) 912 | 913 | else: 914 | generate_plot(search_item[0], filter_by, "multiple") 915 | st.progress(100) 916 | 917 | st.markdown("\n --- \n") 918 | st.success("DONE! Check your `outputs/` folder.") 919 | 920 | except (ConnectionError, ConnectionAbortedError, ConnectionRefusedError, ConnectionResetError): 921 | st.error("An unknown error occured regarding the internet connection!") 922 | 923 | except AttributeError as ae: 924 | 925 | # Error Message from gnomAD 926 | try: 927 | for msg in response.json()["errors"]: 928 | st.error("Errors from gnomAD for your process:\n\t" + msg["message"]) 929 | except Exception as anyOtherException: 930 | pass 931 | 932 | if filter_by != "rs_id": 933 | # General Error Message 934 | st.warning(""" 935 | It might be caused since the search did not find a result from the database. 936 | Try to check the `input` for `{}` or other `options`. 937 | """.format(filter_by)) 938 | 939 | # Technical Error Message 940 | st.markdown(""" 941 | > As a note, technical reason is `{}`. 942 | > 943 | > If you think this should not occur, you can contact with developer to issue this problem on Github page. 944 | """.format(ae)) 945 | 946 | except (TypeError, KeyError): 947 | try: 948 | for msg in response.json()["errors"]: 949 | st.error("Errors from gnomAD for your process:\n\t" + msg["message"]) 950 | except Exception as anyOtherException: 951 | pass -------------------------------------------------------------------------------- /img/main_screen.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/furkanmtorun/gnomad_python_api/7018b845d9437c9617662192c523789c2592d597/img/main_screen.png -------------------------------------------------------------------------------- /img/results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/furkanmtorun/gnomad_python_api/7018b845d9437c9617662192c523789c2592d597/img/results.png -------------------------------------------------------------------------------- /img/results_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/furkanmtorun/gnomad_python_api/7018b845d9437c9617662192c523789c2592d597/img/results_2.png -------------------------------------------------------------------------------- /myFavoriteGenes.txt: -------------------------------------------------------------------------------- 1 | ENSG00000169174 2 | ENSG00000171862 3 | ENSG00000170445 4 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | requests 3 | plotly 4 | streamlit 5 | tqdm 6 | argparse --------------------------------------------------------------------------------