Rare copy number variant analysis in case–control studies using snp array data: a scalable and automated data analysis pipeline
Our pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research.
Authors
- Stefan E. Johansson (ORCID: https://orcid.org/0000-0002-2298-7008)
- Haydeé Artaza (ORCID: https://orcid.org/0000-0002-1585-5488)
- Ksenia Lavrichenko
- Marc Vaudel (ORCID: https://orcid.org/0000-0003-1179-9578)
- Anette S. B. Wolff (ORCID: https://orcid.org/0000-0002-8485-2248)
- Ellen C. Røyrvik (ORCID: https://orcid.org/0000-0002-8979-1704)
Institutions
- Oslo University Hospital (NO)
- Norwegian Institute of Public Health (NO)
- Haukeland University Hospital (NO)
- University of Bergen (NO)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-11-15
- DOI
- https://doi.org/10.1186/s12859-024-05979-0
- Primary Topic
- Genomic variations and chromosomal abnormalities
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Norges Teknisk-Naturvitenskapelige Universitet
- Novo Nordisk
- Helse Vest
- Universitetet i Bergen
- Norges Forskningsråd
- Novo Nordisk Fonden