{"id":20074697,"url":"https://github.com/greenelab/tad_pathways_pipeline","last_synced_at":"2025-05-05T21:32:08.512Z","repository":{"id":79359967,"uuid":"78142104","full_name":"greenelab/tad_pathways_pipeline","owner":"greenelab","description":"Pipeline to implement a \"TAD_Pathways\" analysis. 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Way, Casey S. Greene, and Struan F.A. Grant - 2017**\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.254190.svg)](https://doi.org/10.5281/zenodo.254190)\n\n### Summary\n\nThe repository contains data and instructions to implement a \"TAD_Pathways\"\nanalysis for over 300 different trait/disease GWAS or custom SNP lists.\n\nTAD_Pathways uses the principles of topologically association domains (TADs) to\ndefine where an association signal (typically a GWAS signal) can most likely\nimpact gene function. We use TAD boundaries as defined by\n[Dixon et al. 2012](https://doi.org/10.1038/nature11082) and\n[hg19 Gencode genes](ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_19/)\nto identify which genes may be implicated. We then perform an overrepresentation\npathway analysis to identify significantly associated pathways implicated by the\ninput TAD-defined geneset.\n\nFor more specific details about our method, refer to our\n[short report](https://doi.org/10.1038/ejhg.2017.108 \"Implicating candidate genes at GWAS signals by leveraging topologically associating domains\")\nat the European Journal of Human Genetics.\n\nWe also present a 6 minute video introducing the method and discussing the\nexperimental validation at\n[EJHG-tube](http://www.nature.com/ejhg/videos/index.html).\n\n### Setup\n\nFirst, clone the repository and navigate into the top directory:\n\n```bash\ngit clone git@github.com:greenelab/tad_pathways_pipeline.git\ncd tad_pathways_pipeline\n```\n\nBefore you begin, download the necessary TAD based index files and GWAS\ncuration files and setup python environment:\n\n```bash\nbash initialize.sh\n\n# Using conda version 4.4.11\nconda activate tad_pathways\n```\n\nNow, a `TAD_Pathways` analysis can proceed. Follow an example pipeline to work\nfrom an existing GWAS or the custom pipeline example for insight on how to run\n`TAD_Pathways` on user curated SNPs.\n \n### Examples\n\nWe provide three different examples for a TAD pathways analysis pipeline. To run\neach of the analyses:\n\n```bash\nsource activate tad_pathways\n\n# Example using Bone Mineral Density GWAS\nbash example_pipeline_bmd.sh\n\n# Example using Type 2 Diabetes GWAS\nbash example_pipeline_t2d.sh\n\n# Example using custom input SNPs\nbash example_pipeline_custom.sh\n```\n\n### General Usage\n\nThere are two ways to implement a TAD_Pathways analysis:\n\n1. GWAS\n2. Custom\n\n#### GWAS\n\nTo perform a `TAD_Pathways` analysis on publicly available GWAS results, simply\nbrowse the `data/gwas_catalog/` directory to select a valid GWAS file. These\nfiles contain a curation of all significant SNPs mapped to specific traits as\ndistributed by the [NHGRI-EBI GWAS Catalog](https://www.ebi.ac.uk/gwas/).\n\nEach file in this directory is a tab separated text file of genome-wide\nsignificant SNPs and their genomic location along with their reported nearest\ngene and associated PUBMED id. For complete information on how these files were\nconstructed, refer to https://github.com/greenelab/tad_pathways.\n\nEach GWAS has 3 associated files, including files in `data/gwas_catalog/`. The\nother files are located in `data/gwas_tad_snps/` and `data/gwas_tad_genes/`.\nAll files are important for performing a `TAD_Pathways` analysis. See the\nGWAS example files for instructions on how to implement the necessary scripts.\n\n#### Custom\n\nTo perform a `TAD_Pathways` analysis on a list of custom SNPs, generate a comma\nseparated text file. The first row of the text file should have group names and\nsubsequent rows should list the rs numbers of interest. There can be many\ncolumns with variable length rows.\n\nE.g.: [`custom_example.csv`](custom_example.csv)\n\n| Group 1 | Group 2 |\n| ------- | ------- |\n| rs12345 | rs67891 |\n| rs19876 | rs54321 |\n| ...     | ...     |\n\nThen, perform the following steps:\n\n```bash\nsource activate tad_pathways\n\n# Map custom SNPs to genomic locations\nRscript --vanilla scripts/build_snp_list.R \\\n        --snp_file \"custom_example.csv\" \\\n        --output_file \"mapped_results.tsv\"\n\n# Build TAD based genelists for each group\npython scripts/build_custom_TAD_genelist.py \\\n       --snp_data_file \"mapped_results.tsv\" \\\n       --output_file \"custom_tad_genelist.tsv\"\n```\n\nThe output of these steps are Group specific text files with all genes in TADs\nharboring an input SNP. See\n[`example_pipeline_custom.sh`](example_pipeline_custom.sh) for more details.\n\n### Contact\n\nFor all questions and bug reporting please file a\n[GitHub issue](https://github.com/greenelab/tad_pathways/issues)\n\nFor all other questions contact Casey Greene at csgreene@mail.med.upenn.edu or\nStruan Grant at grants@email.chop.edu\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgreenelab%2Ftad_pathways_pipeline","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgreenelab%2Ftad_pathways_pipeline","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgreenelab%2Ftad_pathways_pipeline/lists"}