International Journal of Clinical Case Reports 2024, Vol.14, No.1, 40-47 http://medscipublisher.com/index.php/ijccr 41 1 Genome Analysis and Screening 1.1 Development of genome sequencing technology Genome sequencing technology has made tremendous progress in the past few decades, enabling a more comprehensive and efficient analysis of the structure and function of the genome. This development has brought revolutionary changes to fields such as basic biology research, medical diagnosis, and personalized treatment. With continuous technological development and innovation, genome sequencing is expected to be more widely applied in the future, further advancing progress in science and medicine (Figure 1). Figure 1 Genome sequencing The first-generation sequencing technology was primarily based on the Sanger sequencing method, which was invented by Frederick Sanger in 1977. It is a classic sequencing method that determines the sequence of bases on DNA one at a time through chain termination reactions during the DNA synthesis process. Although the first-generation sequencing technology is highly accurate, it is relatively slow and costly. Next-Generation Sequencing (NGS) is a series of high-throughput sequencing technologies that emerged around 2005. These technologies involve the parallel processing of millions of DNA fragments, allowing for the efficient, rapid, and cost-effective acquisition of large-scale sequencing data. The advent of NGS technologies has significantly reduced sequencing costs, accelerated the pace of genome sequencing, and provided a robust foundation for large-scale genomic research and personalized medicine (Crovari et al., 2022). Third-Generation Sequencing (TGS) technology is a new generation of sequencing technology that emerged after NGS. It has advantages such as real-time sequencing, long reads, low cost, and portability, which can better solve problems such as assembly and analysis of large genomes. 1.2 Analysis and interpretation of genomic data The analysis and interpretation of genomic data involve the process of dissecting and understanding genomic information based on sequencing data. It is a complex and multi-step process, including data preprocessing, sequence alignment, variant detection, gene annotation, functional analysis, and result interpretation. The goal of these steps is to extract information about individual genetic variations and their relevance to diseases from genomic data, further promoting the development of personalized medicine and genomic research. Genome sequencing data is typically vast raw data that requires preprocessing. This involves tasks such as removing read errors introduced during sequencing, improving the quality of base sequencing, discarding low-quality sequence fragments, and applying appropriate data filtering. The preprocessed sequencing data is then aligned with a reference genome to determine their positions on the genome and corresponding genomic regions (Zhou et al., 2022).
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