International Journal of Clinical Case Reports 2024, Vol.14, No.2, 107-116 http://medscipublisher.com/index.php/ijccr 109 continue to develop and improve, future breakthroughs in the speed, cost, and accuracy of genome sequencing can be anticipated, which will bring more opportunities and challenges to personalized medicine. 1.2 Collection and processing of genomic data The collection and processing of genomic data are complex and critical steps that involve multiple stages from sample collection to data analysis, which are essential for achieving precision medicine, drug discovery, and genetic research (Wang et al., 2021). Researchers need to employ efficient data processing, quality control, and parallel computing techniques to ensure the accuracy and availability of genomic data. The first step in collecting genomic data is sample collection, which requires obtaining biological samples containing genetic information from individuals, such as blood, saliva, or skin tissue. The collection method must ensure the integrity and purity of DNA to avoid contamination and degradation, which is crucial for subsequent genome sequencing. After sample collection, the next step is DNA extraction and purification, which involves breaking the cell membrane through chemical or physical methods to release DNA, and removing proteins and other cell components. The extracted DNA is then quantified and checked for quality to ensure sufficient purity and concentration for sequencing. Depending on the sequencing technology used, the DNA library suitable for the sequencing platform is prepared by performing a series of preprocessing steps on the DNA samples, such as fragmentation, end-repair, adapter ligation, and amplification. Subsequently, the DNA library is sequenced on the sequencing platform, generating a large number of short sequences (for second-generation sequencing technologies) or long reads (for third-generation sequencing technologies). These raw sequencing data then undergo quality control to remove low-quality reads and technical duplicates, ensuring the accuracy of data analysis. After quality control, the data are used for assembly and alignment. Bioinformatics algorithms and software are used to assemble the reads into longer sequences or directly align them to the reference genome to identify genetic and structural variations. Finally, after alignment and variation detection, the genetic variation data obtained need to be annotated for function and analyzed for clinical relevance (Roy et al., 2012) to interpret the biological and medical significance of these variations. Throughout the entire process of data collection and processing, the security and privacy of data should be carefully considered, ensuring that all personal genetic information is processed and stored in accordance with strict ethical and legal standards. The genomic data obtained through the above steps provide strong support for personalized medicine, making precise diagnosis and treatment based on individual genetic information possible (Zou et al., 2021). 1.3 The application of genomic data in medicine In the medical field, genomic data has been widely applied in several aspects. For diagnostics, genomic sequencing can reveal gene mutations that lead to hereditary diseases, providing precise diagnostic information. Regarding treatment, genomic information can assist doctors in selecting the most suitable treatment options for patients. This is particularly significant in cancer treatment, where analyzing the tumor's genome can identify key gene mutations driving tumor growth, thereby facilitating the choice of targeted therapeutic drugs. Moreover, genomic data plays an increasingly important role in preventive medicine. For example, by assessing an individual's genetic risk, personalized preventive measures and lifestyle modification recommendations can be provided. Wei et al. (2011) designed a gene chip that captured all exons of 193 genes related to 103 hereditary diseases. Using the Targeted DNA-HiSeq technology, an average of 99.14% of 3,382 exons was successfully detected, with coverage exceeding 30 times. Through this method, researchers discovered six known mutations (located in four disease-causing genes) and two new mutations (located in two different disease-causing genes), as well as a deletion mutation in the DMD gene's exon.
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