From sequencing data to disease understanding: How can a doctor process patient’s NGS data on their own computer

Introduction. In modern medicine, physicians are increasingly required to be versatile specialists, combining in-depth medical knowledge with technical expertise. While the accessibility of genomic research has dramatically increased over the past few decades, its full integration into medical practice still faces significant challenges. Given the rapid proliferation of new knowl­edge regarding the associations between genomic data and human diseases, there is a growing clinical need for physicians to be able to analyze this data themselves. This is especially true for subsequent medico-genetic studies, particularly when patients already have existing Next-Generation Sequencing (NGS) data (e.g., from exome sequencing).

Aim. The objective of this study is to develop and provide a detailed guide for medical specialists to independently perform bioinformatics analysis of a patient’s NGS data.

Materials and methods. The source data for this study are examples of NGS data files provided to patients following a medicogenetic examination. We used both established and custom-developed software algorithms for read alignment against a refer­ence genome, variant discovery, variant filtering based on quality criteria and specific genes (and their transcripts), and assess­ing their potential health impact.

Results. We developed a comprehensive algorithm and a bioinformatics processing pipeline for sequencing data analysis. This pipeline utilizes a Linux command-line interface, along with Docker containers for established bioinformatics tools such as bwa, gatk, samtools, and bcftools, as well as R scripts based on the Bioconductor project and our own proprietary developments. This algorithm allows medical professionals to independently obtain and interpret genetic variants from a patient’s NGS data.

Conclusion. The information obtained through this pipeline can serve as a foundation for further work in diagnosing hereditary diseases, personalized medicine, and pharmacogenetics. The proposed algorithm effectively achieves the study’s objective, enabling the retrieval of patient genomic sequence variants (exomes) suitable for subsequent analysis and interpretation on a personal computer. We anticipate that a physician’s computer can handle this task in a reasonable amount of time, ensuring reliable and reproducible data processing.

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