Computational Molecular Biology 2025, Vol.15, No.4, 208-217 http://bioscipublisher.com/index.php/cmb 21 0 should remain highly consistent even if the analysts are changed or the same batch of data is re-analyzed at intervals. This consistency is particularly important for clinical cases that require long-term follow-up or multiple re-examinations. 3.2 Promote the comparability of results and data sharing among laboratories Today, with the rapid development of precision medicine, data sharing has become an important means to promote new discoveries. However, if different laboratories each use different analytical processes, it is like speaking different “languages”, and deviations are very likely to occur when directly communicating results. Researchers found that patient variant sets generated by different institutions are often not directly compatible. Some mutations that are reported as pathogenic in one laboratory may be completely undetectable by changed processes. This situation can seriously hinder cross-institutional data integration. To solve this problem, standardization becomes particularly crucial. Unified reference versions, quality control standards and variation interpretation rules can make results among different laboratories comparable (Brancato et al., 2024). This consistency not only helps to establish a large clinical variant database, but also facilitates international cooperation. Only when the way data is produced is consistent can the frequency and effect of variants in different populations or diseases be truly compared. In addition, the standardized process also provides a foundation for external quality assessment (EQA). Many quality assessment projects require laboratories to analyze the uniformly provided data according to the same standard in order to determine the source of differences. If the processes are different, quality evaluation loses its meaning (Cherney et al., 2024). 3.3 Comply with clinical regulations and certification requirements At the regulatory level, the standardization of bioinformatics processes is also an unavoidable requirement. Clinical genomics testing falls within the scope of medical diagnosis, and regulatory authorities have clear requirements for its accuracy and consistency. Medical laboratory quality standards such as ISO 15189 have put forward specific norms for high-throughput sequencing processes including bioinformatics analysis (Haanpääet al., 2025). These specifications require laboratories to establish validated standard operating procedures (SOPs) that are traceable and controllable from sample processing to data analysis. In the field of molecular diagnostics, societies such as AMP and CAP have also issued relevant guidelines, emphasizing that laboratories should validate the performance of NGS analysis pipelines, evaluate the sensitivity, specificity and repeatability of the tests, and re-validate after software updates or parameter adjustments (Jennings et al., 2017; Samarakoon et al., 2025). Only laboratories that meet these requirements can obtain certification, and their test reports will be accepted by clinicians. At the same time, standardization also enables regulatory authorities to more easily formulate unified checklists, promoting the orderly development of the entire industry. Therefore, whether from the perspectives of quality management, data mutual recognition or regulatory compliance, promoting the standardization of bioinformatics processes has become an inevitable trend and is a necessary prerequisite for the long-term healthy development of clinical genomics. 4 Key Technologies and Strategies for Standardizing Bioinformatics Processes 4.1 Workflow management system In modern bioinformatics analysis, if one wants to make the process truly repeatable and traceable, the Workflow Management System (WMS) has almost become an essential tool. It is not a simple “automated script”, but a system that can clearly describe complex analysis steps and dependencies. Nextflow, Snakemake, Cromwell (supporting WDL language), and Galaxy are several common solutions at present. Among them, Nextflow and Snakemake are the most frequently used in both scientific research and clinical practice. Nextflow writes processes in DSL language and can flexibly adapt to various computing platforms; Snakemake is more like Python, with intuitive rule definitions and can also be extended for use in cluster environments (Köster and Rahmann, 2018). With the help of these systems, many analysis steps that originally needed to be carried out manually can now be automated. After defining the process, no matter who runs it, as long as the input is the same, the output result will remain consistent. This not only reduces the differences in human operation but also makes the results easier to trace. Most WMS come with a built-in logging function, which automatically records the software version, parameter configuration and operation information of each step. For clinical laboratories,
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