International Journal of Clinical Case Reports 2024, Vol.14, No.2, 107-116 http://medscipublisher.com/index.php/ijccr 108 This study aims to explore in-depth the challenges and opportunities in the transition from genomic data to personalized medical decisions. By analyzing existing research and practices, this study intends to provide viable recommendations and strategies for healthcare practitioners to promote the development of personalized medicine and offer safer and more effective medical services to patients. Furthermore, this study will also explore future directions for personalized medicine, providing references for related research and practice areas. 1 Overview of Genomic Data 1.1 Advances in genome sequencing technology The development of genome sequencing technologies dates back to 1977 when Frederick Sanger and his colleagues developed a revolutionary DNA sequencing method, known as the first-generation sequencing technique. Sanger sequencing, due to its high accuracy, was considered the gold standard in nucleic acid sequencing for several decades (Borodinov et al., 2020), playing a crucial role in the Human Genome Project (Shendure, 2004). The primary limitations of Sanger sequencing were its high cost and low throughput, which made large-scale genome sequencing projects both time-consuming and expensive. With the completion of the Human Genome Project, sequencing technologies rapidly evolved. Entering the 21st century, second-generation sequencing technologies achieved breakthroughs, also known as High Throughput Sequencing (HTS) technologies, such as Illumina sequencing and Roche 454 sequencing. These technologies significantly increased sequencing speed and reduced costs by processing tens of thousands of DNA fragments in parallel (Loman et al., 2012). Particularly, the Illumina platform became dominant in the market due to its high output, low cost, and high accuracy (Cheval et al., 2011). These second-generation sequencing technologies made individual genome sequencing more practical (Metzker, 2010), laying the foundation for personalized and precision medicine. As technology progressed, third-generation sequencing technologies emerged, also known as single-molecule real-time (SMRT) sequencing techniques, including PacBio SMRT technology and Oxford Nanopore's nanopore sequencing technology. The main advantage of third-generation sequencing is that they provide longer read lengths, which helps to address challenges in the genome related to repetitive sequences and structural variations, showing great potential in resolving complex genome structures, improving genome assembly quality, and detecting long-distance variations. However, they still face significant challenges in terms of accuracy and cost. Weirather et al. (2017) compared the applications of PacBio and Oxford Nanopore technologies in transcriptome analysis. The results indicated that while PacBio offered slightly better data quality, Oxford Nanopore provided higher yields. Additionally, hybrid sequencing approaches combining Illumina strategies demonstrated superior performance in most transcriptome analyses. This suggests that both sequencing technologies are suitable for full-length single-molecule transcriptome analysis. Zhao et al. (2019) reviewed the applications of PacBio Iso-Seq and Oxford Nanopore direct RNA sequencing technologies in plants. These technologies offer significant advantages for identifying full-length splice isoforms, complex alternative splicing events, and other post-transcriptional events. Direct RNA sequencing provides valuable information about RNA modifications, which would be lost in PCR amplification steps of other methods. Kim et al. (2019) compared the cost-effectiveness and quality of human genome de novo assembly using ONT PromethION and PacBio SMRT sequencing technologies. The findings showed that ONT PromethION could achieve good quality chromosomal-scale human genome assemblies at a lower cost compared to PacBio. From the first-generation Sanger sequencing to the second-generation high-throughput sequencing, and now to the third-generation single-molecule sequencing, each generation has significantly advanced genome research and the realization of personalized medicine. As new generations of sequencing technologies
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