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Recommendations for bioinformatics in clinical practice
Oslo University Hospital, Oslo, Norway.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH Royal Institute of Technology, Stockholm, Sweden.
Rigshospitalet, Copenhagen, Denmark.
Number of Authors: 342025 (English)In: Genome Medicine, E-ISSN 1756-994X, Vol. 17, no 1, article id 124Article, review/survey (Refereed) Published
Abstract [en]

Background: Next-generation sequencing (NGS) is well established in clinical diagnostics, and whole-genome sequencing (WGS) is increasingly becoming the method of choice, as a result of lower prices and robust comprehensive data. While guidelines exist for variant interpretation and laboratory quality considerations, there remains a need for standardised bioinformatics practices to ensure clinical consensus, accuracy, reproducibility and comparability.

Methods: This article presents consensus recommendations developed by 13 clinical bioinformatics units participating in the Nordic Alliance for Clinical Genomics (NACG) by expert bioinformaticians working in clinical production. The recommendations are based on clinical practice and focus on analysis types, test and validation, standardisation and accreditation, as well as core competencies and technical management required for clinical bioinformatics operations.

Results: Key recommendations include adopting the hg38 genome build as reference, and a standard set of recommended analyses, including the use of multiple tools for structural variant (SV) calling and in-house data sets for filtering recurrent calls. Clinical bioinformatics in production should operate at standards similar to ISO 15189, utilising off-grid clinical-grade high-performance computing systems, standardised file formats and strict version control. Reproducibility should be ensured through containerised software environments. Pipelines must be documented and tested for accuracy and reproducibility, minimally covering unit, integration and end-to-end testing. Standard truth sets such as GIAB and SEQC2 for germline and somatic variant calling, respectively, should be supplemented by recall testing of real human samples that have been previously tested using a validated method. Data integrity must be verified using file hashing, while sample identity must be confirmed through fingerprinting and genetically inferred identification markers such as sex and relatedness. Finally, clinical bioinformatics should encompass diverse skills, including software development, data management, quality assurance and domain expertise in human genetics.

Conclusions: These recommendations provide a consensus framework for standardising bioinformatics practices across clinical WGS applications and can serve as a practical guide to facilities that are new to large-scale sequencing-based diagnostics, or as a reference for those who already run high-volume clinical production using NGS.

Place, publisher, year, edition, pages
Springer Nature , 2025. Vol. 17, no 1, article id 124
National Category
Medical Genetics and Genomics Bioinformatics (Computational Biology) Bioinformatics and Computational Biology
Identifiers
URN: urn:nbn:se:kth:diva-372605DOI: 10.1186/s13073-025-01543-4ISI: 001595676000001PubMedID: 41107899Scopus ID: 2-s2.0-105019114636OAI: oai:DiVA.org:kth-372605DiVA, id: diva2:2013034
Note

QC 20251111

Available from: 2025-11-11 Created: 2025-11-11 Last updated: 2025-11-11Bibliographically approved

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Neethiraj, Ramprasad

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