{"id":21251282,"url":"https://github.com/datngu/tagsnp_evaluation","last_synced_at":"2025-03-15T05:23:11.593Z","repository":{"id":128898312,"uuid":"400936755","full_name":"datngu/TagSNP_evaluation","owner":"datngu","description":"Evaluation tag SNP selection strategies","archived":false,"fork":false,"pushed_at":"2022-01-13T07:31:51.000Z","size":1153,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-21T20:48:47.182Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/datngu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-08-29T02:55:08.000Z","updated_at":"2022-01-10T08:28:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"9ecd74fb-e03f-42da-be97-9cf4ff0c4553","html_url":"https://github.com/datngu/TagSNP_evaluation","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datngu%2FTagSNP_evaluation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datngu%2FTagSNP_evaluation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datngu%2FTagSNP_evaluation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datngu%2FTagSNP_evaluation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/datngu","download_url":"https://codeload.github.com/datngu/TagSNP_evaluation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243686916,"owners_count":20331229,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-21T03:41:21.983Z","updated_at":"2025-03-15T05:23:11.574Z","avatar_url":"https://github.com/datngu.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TagSNP_evaluation\nEvaluation tag SNP selection strategies\n\nThese scipts tested in Ubuntu v18 environment.\n\n\n## Software requirements\nLeave one out imputation cross validation requires R, vcftools, bcftools, minimac3, minimac4, and plink.\n- VCFtools 0.1.17 : https://vcftools.github.io\n- bcftools 1.10.2 : https://github.com/samtools/bcftools\n- plink1.9 : https://www.cog-genomics.org/plink/\n- minimac3 : https://genome.sph.umich.edu/wiki/Minimac3\n- minimac4 : https://genome.sph.umich.edu/wiki/Minimac4\n- R packages version 3.6.0 or latter\n\n## Clone this project and config pipeline:\n\n```sh\n\ngit clone https://github.com/datngu/TagSNP_evaluation\n\n```\n# I. RUNNING PIPELINES\n\nAssumming that you have a processed (Bialellic SNPs with MAF \u003e= 1%) vcf.gz file named: chr10_EAS.vcf.gz and want to compare impuatation accuracy with 30000 tag SNP selected.\n\n## 1. Running TagIt\n\n```sh\ncd TagSNP_evaluation/TagIt\nbash configure.sh\n\nbash TagIt_pipeline.sh ../chr10_EAS.vcf.gz chr10_EAS\n\n# picking top 30000 tag SNP selected from \nhead -n 30000 chr10_EAS/chr10_EAS_tags_cleaned.txt \u003e chr10_EAS_tags_30000_cleaned.txt\n\n```\nYou output should be: `TagSNP_evaluation/TagIt/chr10_EAS_tags_30000_cleaned.txt`\n\n## 2. Running FastTagger\n\n```sh\n\ncd TagSNP_evaluation/FastTagger \n\nbash bash configure.sh\n\nbash FastTagger_pipeline.sh  ../chr10_EAS.vcf.gz chr10_EAS 30000 chr10_EAS\n\n```\n\nYou output should be: `TagSNP_evaluation/FastTagger/cleaned_EAS_chr10_EAS_30000.tagSNP.txt`\n\n## 3. Running EQ_uniform\n\n```sh\n\ncd TagSNP_evaluation/EQ_uniform\n\nRscript EQ_uniform.R vcf=../chr10_EAS.vcf.gz size=30000 out=EQ_uniform_array_30000.txt\n\n```\n\nYou output should be: `TagSNP_evaluation/EQ_uniform/EQ_uniform_array_30000.txt`\n\n## 4. Running EQ_MAF\n\n```sh\n\ncd TagSNP_evaluation/EQ_MAF\n\nRscript EQ_MAF.R vcf=../chr10_EAS.vcf.gz size=30000 out=EQ_MAF_array_30000.txt\n\n```\n\nYou output should be: `TagSNP_evaluation/EQ_MAF/EQ_MAF_array_30000.txt`\n\n# II. EVALUATION WITH LEAVE ONE OUT CROSS VALIDATION\n\n## 1. Create imputation reference pannel\nThis step take very long time, depends on your size of chromosome and population.\n```sh\ncd TagSNP_evaluation\ncreate_imputation_ref.sh -v chr10_EAS.vcf.gz -o chr10_EAS_imputation_ref -p 16\n\n```\n\n\n## 2. Leave one out imputation with your pre-buit reference panel and computing Pearson's correlation\n\n```sh\ncd TagSNP_evaluation\n# 1. TagIt\n## imputation\nimputation_with_prebuilt_ref.sh -t TagIt/chr10_EAS_tags_30000_cleaned.txt -r chr10_EAS_imputation_ref -o TagIt_EAS -p 16\n## computing Pearson's correlation\ncompute_imputation_accuracy.R imputation=TagIt_EAS out=TagIt_EAS.Rdata\n\n# 2. FastTagger\n## imputation\nimputation_with_prebuilt_ref.sh -t FastTagger/cleaned_EAS_chr10_EAS_30000.tagSNP.txt -r chr10_EAS_imputation_ref -o FastTagger_EAS -p 16\n## computing Pearson's correlation\ncompute_imputation_accuracy.R imputation=FastTagger_EAS out=FastTagger_EAS.Rdata\n\n\n# 3. EQ_uniform\n## imputation\nimputation_with_prebuilt_ref.sh -t EQ_uniform/EQ_uniform_array_30000.txt -r chr10_EAS_imputation_ref -o EQ_uniform_EAS -p 16\n## computing Pearson's correlation\ncompute_imputation_accuracy.R imputation=EQ_uniform_EAS out=EQ_uniform_EAS.Rdata\n\n# 4. EQ_MAF\n## imputation\nimputation_with_prebuilt_ref.sh -t EQ_MAF/EQ_MAF_array_30000.txt -r chr10_EAS_imputation_ref -o EQ_MAF_EAS -p 16\n## computing Pearson's correlation\ncompute_imputation_accuracy.R imputation=EQ_MAF_EAS out=EQ_MAF_EAS.Rdata\n\n```\n\nYou output should be: `TagIt_EAS.Rdata, FastTagger_EAS.Rdata, EQ_uniform_EAS.Rdata, EQ_MAF_EAS.Rdata`\n\n# III. REFERENCE\n\nNguyen, D. T., Dinh, H. Q., Vu, G. M., Nguyen, D. T., \u0026 Vo, N. S. (2021, November). A comprehensive imputation-based evaluation of tag SNP selection strategies. In 2021 13th International Conference on Knowledge and Systems Engineering (KSE) (pp. 1-6). IEEE. https://doi.org/10.1109/KSE53942.2021.9648614\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatngu%2Ftagsnp_evaluation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatngu%2Ftagsnp_evaluation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatngu%2Ftagsnp_evaluation/lists"}