snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data
| Autor: | Christina Vasilopoulou, William Duddy, Benjamin Wingfield, Andrew P. Morris |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Quality Control
Population Stratification GWAS pipeline Computer science media_common.quotation_subject Population computer.software_genre General Biochemistry Genetics and Molecular Biology 03 medical and health sciences 0302 clinical medicine Software Genomic Variants Humans GWAS Quality (business) Quantitative Biology - Genomics Imputation (statistics) General Pharmacology Toxicology and Pharmaceutics education QC Imputation 030304 developmental biology media_common Genomics (q-bio.GN) 0303 health sciences education.field_of_study Genome General Immunology and Microbiology Software Tool Article business.industry Reproducibility of Results Genomics Articles General Medicine Anaconda Pipeline (software) Nextflow Workflow FOS: Biological sciences User control Scalability BioContainers Data mining business computer 030217 neurology & neurosurgery SNPs |
| Zdroj: | F1000Research |
| Popis: | Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. 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. |
| Databáze: | OpenAIRE |
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