Yuhong Huang , Yufen Wang , Guangman Xu

Traditional Chinese Medicine Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 4   doi: 10.5376/cmb.2025.15.0020
Received: 18 Jun., 2025    Accepted: 29 Jul., 2025    Published: 21 Aug., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Huang Y.H., Wang Y.F., and Xu G.M., 2025, Standardizing bioinformatics pipelines for clinical genomics, Computational Molecular Biology, 15(4): 208-217 (doi: 10.5376/cmb.2025.15.0020)

High-throughput sequencing technology has been widely adopted in clinical genomics for the diagnosis of genetic diseases and personalized treatment of tumors. However, the differences in bioinformatics analysis processes among various laboratories may lead to inconsistent variant detection results, affecting clinical interpretation and data sharing. Based on the research on the standardization of bioinformatics processes, this article analyzes the common data analysis processes in clinical genomics, the key steps and tools involved in each link, and clarifies the necessity and challenges of process standardization. We further explored the technical strategies for achieving standardization, including the adoption of workflow management systems, containerization technologies, unified reference standards, and quality control verification schemes, and introduced relevant domestic and international standards, norms, and application practices. The results show that standardized bioinformatics processes help improve the accuracy and repeatability of variant detection, ensure the comparability of results from different laboratories, and meet clinical diagnostic norms and regulatory requirements. This work provides a reference for the standardization of the clinical genomics student information analysis process and can promote the reliable application of sequencing data in clinical practice.

Clinical; Genomics; Bioinformatics analysis process; Standardization repeatability; Quality control