{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/snpqt-flexible-reproducible-and-comprehensive","title":"snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data","arxiv_id":"2105.01923","date":"2021-05-05","proceeding":null,"authors":["Christina Vasilopoulou","Benjamin Wingfield","Andrew P. Morris","William Duddy"],"abstract":"Motivation: Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of disparate bioinformatics tools. Results: To provide an automated solution that retains comprehensive quality checks and flexible workflow architecture, we have developed snpQT, a scalable, stand-alone software pipeline, offering some 36 discrete quality filters or correction steps, with plots before-and-after user-modifiable thresholding. This includes build conversion, population stratification against 1,000 Genomes data, population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used and users need not be superusers nor have any prior coding experience. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset. Availability: snpQT is open source and freely available at https://github.com/nebfield/snpQT Contact: Vasilopoulou-C@ulster.ac.uk, w.duddy@ulster.ac.uk","url_abs":"https://arxiv.org/abs/2105.01923v1","url_pdf":"https://arxiv.org/pdf/2105.01923v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"snpqt-flexible-reproducible-and-comprehensive","repo_url":"https://github.com/nebfield/snpQT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"imputation","task_name":"Imputation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}