In the 21st century, genomics and personalized medicine have emerged as two significant trends leading the future of healthcare. Genomics, the comprehensive study of all genetic material in an organism (Jordan, 1999), not only unveils the fundamental blueprint of life but also enables the understanding of genetic variations among individuals. The successful completion of the Human Genome Project (International human genome sequencing consortium, 2001) and the rapid advancements in sequencing technologies have made genomic data acquisition more accessible and cost-effective than ever, laying the technological groundwork for personalized medicine (Collins, 1999).

Personalized medicine, also known as precision medicine, refers to the medical practice of customizing treatment and prevention plans based on an individual's genetic information, environmental factors, and lifestyle habits (Delpierre and Lefèvre, 2023). The underlying philosophy is that genetic differences influence each person's susceptibility to diseases and response to drugs. Thus, personalized medical approaches can enhance treatment efficiency and safety, avoiding the one-size-fits-all treatment methods (Alyass, 2015).

The widespread application of genomic data is revolutionizing traditional medical decision-making processes. Whereas clinical decisions were traditionally reliant on population-level statistical data, doctors can now use individual genomic information to formulate more precise treatment plans. For example, in cancer treatment, analyzing the genomic characteristics of tumors can help select the most effective targeted drugs. Similarly, in managing cardiovascular diseases, genomic data can assist in predicting an individual's drug responsiveness, thereby optimizing drug selection and dosage (Tommel et al., 2023).

Despite the promising prospects of genomic data in personalized medicine, this field faces several challenges. Data privacy and security are major concerns due to the sensitivity and complexity of genetic information, which require high standards of confidentiality and security. Interpreting genetic data demands extensive expertise, and interpretations may vary across different individuals and populations. Additionally, the cost of personalized medicine remains a significant issue, especially in resource-limited settings (Chong, 2018). Moreover, legal, ethical, and societal acceptance are also critical factors to consider in advancing personalized medicine.

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 research 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 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.

André et al. (2014) hoped that molecular screening could identify genomic abnormalities in individual metastatic breast cancer patients and provide personalized targeted treatment. The results showed that out of 423 patients, 195 (46%) were identified with targetable genomic alterations, but only 55 (13%) received personalized treatment. This study confirmed the feasibility of personalized medicine, including rare genomic changes, in the treatment of metastatic breast cancer (André et al., 2014).

With the continuous advancement of genomic sequencing technology, not only can genetic information be understood more deeply, but this information can also be applied to personalized medicine, offering patients more precise and effective medical services.

2 Case Studies in Personalized Medicine

2.1 Cancer treatment

In the field of personalized medicine, cancer treatment strategies are rapidly evolving, especially through the use of tumor genomic characteristics to tailor treatment plans. This process begins with extracting DNA from patient tumor tissues, performing whole-genome sequencing to identify specific gene mutations, copy number variations, and other genomic abnormalities. The analysis of these data reveals key genes and signaling pathways related to tumor development, spread, and drug sensitivity.

Based on this genomic information, physicians can select targeted therapeutic drugs that specifically address the genetic variations of the tumor. During the treatment process, by monitoring tumor markers and regularly conducting imaging studies, physicians can assess the effectiveness of the treatment and adjust the treatment plan as needed. Moreover, the genomic characteristics of the tumor may change over time, therefore, periodic re-sequencing of the genome can help identify new mutations that arise during treatment, which may lead to the development of drug resistance. In such cases, physicians can adjust the treatment strategy based on the latest genomic information. For example, tumors exhibiting EGFR mutations can be treated with inhibitors targeting this specific marker. This personalized treatment approach can enhance efficacy while minimizing damage to normal cells, thereby reducing unnecessary side effects.

A study by Liu et al. (2016) found that the EML4-ALK fusion gene is present in approximately 5% of non-small cell lung cancer (NSCLC) patients and is an important target gene. The study analyzed the sensitivity and specificity of immunohistochemistry (IHC) in detecting EML4-ALK fusion gene mutations, evaluating the accuracy and clinical utility of this method, thus providing a basis for "individualized molecular treatment" for lung cancer patients. The results demonstrated that the specific antibody IHC method for detecting the EML4-ALK fusion gene is highly specific and sensitive, making it a simple and rapid screening method (Liu et al., 2016).

Further research by Liu et al. (2023) found significant differences in the expression of the cAMP-dependent protein kinase inhibitor gamma (PKIG) in lung squamous carcinoma (LUSC) compared to normal tissue, and it has important reference value for the diagnosis and prognosis assessment of LUSC. The expression of PKIG is positively correlated with the infiltration level of regulatory T cells (Tregs), as well as with the expression levels of various chemokines/receptors and immunosuppressants, indicating that PKIG is highly related to the prognosis and immune microenvironment of LUSC, and it holds promise as a potential biomolecular marker for LUSC immune therapy (Liu et al., 2023).

However, personalized cancer treatment strategies face multiple challenges, including how to deal with the internal heterogeneity of tumors, how to manage side effects during treatment, and how to cope with the high costs of treatment. To overcome these challenges, ongoing research, technological advancements, and clinical trials are essential. With the continuous progress of genomics, bioinformatics, and related technologies, personalized medicine will be able to provide more effective and safer treatment options for more cancer patients, greatly improving their quality of life and survival rates.

2.2 Cardiovascular disease prevention

The strategies for preventing cardiovascular diseases are being revolutionized by personalized medicine approaches, primarily thanks to a deeper understanding of genetic risk factors. Through the analysis of genomic data, medical experts can now assess an individual's genetic predisposition to cardiovascular diseases. This assessment helps doctors design more targeted prevention plans for high-risk groups.

By identifying genetic markers associated with cardiovascular diseases, such as specific gene mutations, doctors can pinpoint individuals who are genetically more susceptible to these conditions. This information is extremely valuable as it can be used not only for early warning but also to guide doctors in customizing preventative measures for these high-risk individuals. For instance, for those who are genetically predisposed to high cholesterol, doctors might recommend a specialized diet plan aimed at lowering their cholesterol levels, thereby reducing their risk of cardiovascular diseases.

In addition to lifestyle adjustments, genetic information can also be utilized for personalized medication treatments. In some cases, an individual's genotype may influence their response to specific drugs. This information can guide doctors in choosing the most suitable medication for each patient and adjusting dosages to maximize efficacy and minimize side effects.

The studies by Assimes and Roberts (2016) explored how genetic susceptibility to Coronary Artery Disease (CAD) impacts prevention and management. By identifying more than 60 CAD susceptibility loci, they revealed new potential pathogenic pathways and highlighted the benefits of long-term risk factor modification. Mendelian randomization studies provided insights into the causal relationships between CAD-related traits. Genetic risk scores have been proposed as a predictive tool to improve the delivery of prevention strategies, opening new pathways for personalized prevention and treatment strategies based on genetic information.

Rasmussen and Frikke-Schmidt (2023) emphasized in their research that by integrating genetic risk factors with modifiable cardiovascular risk factors, more effective targeted prevention of dementia can be achieved. This approach provides a personalized prevention strategy aimed at controlling cardiovascular risks through lifestyle changes, thereby reducing the risk of dementia. The study notes that while this comprehensive prevention strategy has potential, further research is needed to evaluate its actual effectiveness in reducing the incidence of dementia (Rasmussen and Frikke-Schmidt, 2023).

Furthermore, for those individuals already identified as high-risk, regular physiological and genetic monitoring is essential. This includes routine checks of blood pressure and cholesterol levels, as well as tracking their genetic markers, to ensure their prevention strategies remain up-to-date and effective. By integrating genetic information, doctors can now tailor cardiovascular disease prevention plans for each individual, thereby enhancing the effectiveness of preventive measures, reducing the incidence of cardiovascular diseases, and improving the quality of life for individuals.

2.3 Diagnosis and treatment of rare diseases

The diagnosis and treatment of rare diseases have always been significant challenges in the medical field, due to the vast number of different diseases and the relatively small number of patients suffering from each. This has led to somewhat delayed research and development of treatment methods for these diseases. However, with advancements in genomics, the situation is starting to change.

In terms of diagnosis, the application of genomic data has significantly accelerated the diagnostic process. Traditional diagnostic methods could take years to reach a conclusion, involving a series of complex tests and evaluations. In contrast, whole-genome or whole-exome sequencing technologies can analyze thousands of genes in a patient in just a few weeks, quickly identifying the specific genetic mutations causing the disease. This not only substantially shortens the diagnostic time for patients but also increases the accuracy of the diagnosis, avoiding prolonged periods of uncertainty and anxiety.

Regarding treatment, the application of genomic data also shows tremendous potential. Once a diagnosis is confirmed, physicians can customize treatment plans based on the patient's specific genetic mutations. For some rare diseases, especially those with clear genetic causes, treatments targeting specific genetic defects, such as gene therapy or molecular targeted drugs, can be used. This approach directly addresses the root causes of the disease rather than merely treating symptoms, offering hope for significant improvement or even a cure for patients' conditions.

Wright et al. (2018) in their study published in “Nature Reviews Genetics” indicate that most rare diseases affect children and many of these conditions have genetic underpinnings. Although current technologies and knowledge often fall short of a definitive diagnosis, pediatric genomics has made significant advances in increasing the rate of pathogenic gene discovery and improving the diagnosis of rare pediatric diseases through the adoption of next-generation sequencing technologies, particularly whole-exome and whole-genome sequencing.

Hartin et al. (2020) in “Molecular Medicine” mention that approximately 400 million people worldwide suffer from rare diseases. While whole-exome and whole-genome sequencing have greatly facilitated the diagnosis of rare diseases, the overall diagnostic rate remains below 50%. Reducing the time needed for disease diagnosis is one of the most critical needs affecting patients with rare diseases.

Posey (2019) discusses the revolutionary impact of genomic sequencing on the diagnosis of rare diseases in the “Orphanet Journal of Rare Diseases”. The article emphasizes that while genomic medicine has the potential to fundamentally transform healthcare, particularly in the diagnosis and treatment of rare diseases, achieving this goal requires identifying rare variants in approximately 20,000 protein-coding genes and understanding their impact on health. Although whole-genome sequencing enhances the sensitivity of variant detection, each technique has its limitations, and future challenges include improving variant detection sensitivity and resolving genetic heterogeneity (Posey, 2019).

2.4 Personalization of drug metabolism

In the field of personalized medicine, the personalization of drug metabolism is a significant and challenging topic. It explores how to adjust drug treatments based on individual genetic differences to optimize therapeutic effects and minimize side effects. A classic example is the use of the antiplatelet drug clopidogrel in patients with cardiovascular diseases. The metabolism of the drug is influenced by the CYP2C19 enzyme in the human body, and reactions to the same drug can vary drastically among individuals. This is particularly true for individuals carrying genetic variants such as CYP2C19*2 or *3, who have reduced metabolic capacity for clopidogrel, potentially leading to insufficient therapeutic effects and increased risk of cardiovascular events.

Scott et al. (2011) in their study published in “Clinical Pharmacology & Therapeutics”, pointed out that CYP2C19 polymorphism significantly affects the therapeutic outcomes of clopidogrel. The presence of the CYP2C19*2 allele, in particular, increases the risk of adverse cardiovascular events in patients with acute coronary syndrome being treated with clopidogrel. This study underscores the importance of personalized antiplatelet therapy based on CYP2C19 genotype to optimize effectiveness and reduce the risk of adverse effects, advancing the practice of genotype-based personalized medicine. These findings are crucial for guiding drug treatment choices in patients with cardiovascular diseases.

To address this issue, it is recommended clinically to test for the CYP2C19 genotype before administering clopidogrel. If a patient is found to carry genetic variants that affect drug metabolism, physicians may adjust the drug dosage or consider switching to an alternative medication that does not depend on CYP2C19 metabolism. This decision-making process, based on individual genetic information, demonstrates the power of personalized medicine and reflects the future trend of precision medicine.

3 Challenges and Opportunities

3.1 Challenges faced

3.1.1 Data privacy and security challenges

Genomic data contains a vast amount of personal sensitive information; thus, ensuring its security and privacy is a critical issue that needs immediate resolution (Hekel et al., 2021). On one hand, data breaches could lead to personal privacy violations and potentially severe economic and social losses. On the other hand, secure storage and transmission of data require high-standard technical support. Therefore, developing comprehensive data protection mechanisms and legal regulations to ensure the security and privacy of patient information throughout the collection, storage, processing, and sharing processes is an urgent issue to address.

3.1.2 Complexity of genomic data interpretation

The interpretation of genomic data involves multiple fields such as biology, statistics, and computer science. Its complexity lies in the large volume of data and its intricate meanings, necessitating specialized knowledge and technology for accurate interpretation. Moreover, the relationship between genes and diseases is not always one-to-one; many diseases may be influenced by multiple genes and environmental factors. Thus, extracting useful information from complex genomic data and correctly interpreting the relationship between gene variations and health is a significant challenge.

3.1.3 Cost-effectiveness issues

Although personalized medicine promises more accurate diagnostics and treatments for patients, its high costs remain a significant barrier to widespread adoption. The high costs include not only genomic sequencing itself but also subsequent data analysis, medical consultations, and other processes. This makes personalized treatments unaffordable for many patients, thus limiting the broad application of this technology.

3.1.4 Legal, ethical, and social acceptability

Personalized medicine involves numerous ethical and legal issues (Walker et al., 2021), including but not limited to: ethical boundaries of gene editing, obtaining patient informed consent, and issues regarding the ownership and use rights of genetic information (Quattrocchi et al., 2019). Additionally, the social acceptability of personalized medicine varies; some patients may have reservations about genetic testing, fearing that the results could affect their future insurance purchases or job opportunities. Therefore, how to promote technological development while resolving these legal and ethical issues, as well as enhancing public awareness and acceptance, is also a current challenge.

3.2 Opportunities in technology and policy

On the path of developing personalized medicine, technological advancements and policy support have opened new doors, offering opportunities not only to overcome existing challenges but also to drive the field toward a broader future.

3.2.1 Opportunities in technological advancements

With the rapid development of big data technologies and AI, we are now able to process and analyze large volumes of genomic data like never before. These technologies help us more accurately identify disease-related genetic markers, enhancing the accuracy of disease predictions and thus providing patients with more personalized prevention and treatment plans.

The advent of next-generation sequencing technologies has significantly reduced the cost and time of genetic sequencing, making large-scale genomic sequencing feasible. This provides a technological guarantee for quickly and accurately identifying individual genetic differences.

The prevalence of smart devices and mobile applications offers possibilities for real-time health monitoring for patients. These technologies not only facilitate communication between patients and doctors but also collect health data in real-time, providing richer data support for personalized medicine.

3.2.2 Opportunities in policy making

Several countries and regions around the world have begun to recognize the importance of personalized medicine and have launched a series of policies and plans to support research and application in this field. Financial contributions from governments and private institutions provide strong support for basic research and clinical applications.

To promote the effective use of genomic data while protecting personal privacy, multiple countries have developed detailed policies on data sharing and privacy protection. These policies aim to establish a healthy data ecosystem that can promote scientific research while ensuring the security of personal information.

The establishment of ethical guidelines and legal frameworks is crucial for the healthy development of personalized medicine. By clearly defining the ethical principles and legal responsibilities in research and applications, these measures can prevent technological abuse and ensure patient interests.

Technological advancements and policy support lay a solid foundation for the future development of personalized medicine. By fully leveraging these opportunities, we can expect personalized medicine to better meet patients' health needs and advance medical and health services in the near future.

3.3 Recommendations

As genomics and personalized medicine rapidly advance, we are on the threshold of a new medical revolution. In the future, genomic data will play an increasingly critical role in personalized medical decisions, but this will also bring new challenges and requirements.

Future research will need to focus on increasing the speed and accuracy of genomic sequencing, as well as developing more efficient data analysis tools. These technological advancements will help extract valuable information from massive amounts of genomic data, thereby providing more accurate and personalized treatment plans for each patient.

To achieve the comprehensive application of personalized medicine, it is necessary to address ethical, legal, and social acceptance issues. This includes ensuring the security and privacy of individual genomic data, establishing fair and reasonable medical policies, and enhancing public understanding and acceptance of personalized medicine concepts. Additionally, international cooperation is crucial, requiring the establishment of cross-border legal frameworks and data sharing mechanisms.

Economically, reducing the costs of personalized medicine will be a significant issue. As technologies mature and scale up, the costs of personalized medicine are expected to gradually decrease. At the same time, more cost-effectiveness analyses are needed to demonstrate that personalized medicine can save costs for patients and the entire medical system in the long run.

Education and training will also be a vital area for future development. There is a need to cultivate a group of medical professionals who understand genomics and personalized medicine, including doctors, nurses, genetic counselors, and data analysts. Moreover, it is necessary to educate the public about genomics and personalized medicine to increase their understanding and trust in this emerging field.

As the clinical application of personalized medicine continues to grow, the demand for empirical research will also increase. This includes conducting clinical trials to validate genome-directed treatment strategies and assessing their effectiveness and safety. Additionally, a systematic review and summary of existing cases of personalized treatment are needed to accumulate experience and establish best practices.

The future of personalized medicine is filled with challenges and opportunities. Through efforts in technological advancement, policy making, education, training, and international cooperation, we can expect to see the widespread adoption and optimization of personalized medicine in the near future, thus providing more precise and efficient treatment options for patients.

Reference

Alyass A., Turcotte M., and Meyre D., 2015, From big data analysis to personalized medicine for all: challenges and opportunities, BMC Med Genomics 8: 33.

https://doi.org/10.1186/s12920-015-0108-y

André F., Bachelot T., Commo F., Campone M., Arnedos M., Diéras V., Lacroix-Triki M., Lacroix L., Cohen P., Gentien D., Adélaide J., Dalenc F., Gonçalves A., Lévy C., Ferrero J., Bonneterre J., Lefeuvre C., Jimenez M., Filleron T., and Bonnefoi H., 2014, Comparative genomic hybridisation array and DNA sequencing to direct treatment of metastatic breast cancer: a multicentre, prospective trial (safir01/unicanCER), The Lancet. Oncology, 15(3): 267-274.

https://doi.org/10.1016/S1470-2045(13)70611-9

Assimes T., and Roberts R., 2016, Genetics: implications for prevention and management of coronary artery disease, Journal of the American College of Cardiology, 68 25, 2797-2818.

https://doi.org/10.1016/j.jacc.2016.10.039

Borodinov A., Manoilov V., Zarutsky I., Petrov A., and Kurochkin V., 2020, GEnerations of dna sequencing methods (Review), 30: 3-20.

https://doi.org/10.18358/np-30-4-i320

Cheval J., Sauvage V., Frangeul L., Dacheux L., Guigon G., Dumey N., Pariente K., Rousseaux C., Dorange F., Berthet N., Brisse S., Moszer I., Bourhy H., Manuguerra C., Lecuit M., Burguière A., Caro V., and Eloit M., 2011, Evaluation of high-throughput sequencing for identifying known and unknown viruses in biological samples, Journal of Clinical Microbiology, 49: 3268-3275.

https://doi.org/10.1128/JCM.00850-11

Chong H.Y., Allotey P.A., and Chaiyakunapruk N., 2018, Current landscape of personalized medicine adoption and implementation in Southeast Asia, BMC Med Genomics, 11: 94.

https://doi.org/10.1186/s12920-018-0420-4

Collins F., 1999, The Human genome project and the future of medicine, Annals of the New York Academy of Sciences, 882.

https://doi.org/10.1111/j.1749-6632.1999.tb08532.x

International human genome sequencing consortium, 2001, Initial sequencing and analysis of the human genome, Nature, 409: 860-921.

Delpierre C., and Lefèvre T., 2023, Precision and personalized medicine: What their current definition says and silences about the model of health they promote, Implication for the development of personalized health, Front. Sociol, 8: 1112159.

https://doi.org/10.3389/fsoc.2023.1112159

Hartin S., Means J., Alaimo J., and Younger S., 2020, Expediting rare disease diagnosis: a call to bridge the gap between clinical and functional genomics, Molecular Medicine, 26.117

https://doi.org/10.1186/s10020-020-00244-5

Hekel R., Budiš J., Kucharík M., Radvánszky J., Pös Z., and Szemeš T., 2021, Privacy-preserving storage of sequenced genomic data, BMC Genomics, 22: 712.

https://doi.org/10.1186/s12864-021-07996-2

Kim H., Jeon S., Kim C., Kim Y., Cho Y., Kim J., Blazyte A., Manica A., Lee S., and Bhak J., 2019, Chromosome-scale assembly comparison of the korean reference genome koref from promethion and pacbio with hi-c mapping information, GigaScience, 8.

https://doi.org/10.1093/gigascience/giz125

Liu C., Cai L., Zhong D., and Wang J., 2016, The Correlation of PKIG and its role in the tumor microenvironment, Chinese Journal of Lung Cancer, 19: 38-45.

Liu Q., Li H., Li B., Ren M., Li Z., Chen Y., Zheng Z., Meng Y., and Feng H., 2023, The Correlation of PKIG and its role in the tumor microenvironment, Chinese Journal of Lung Cancer, 26: 523-537.

Loman N., Misra R., Dallman T., Constantinidou C., Gharbia S., Wain J., and Pallen M., 2012, Performance comparison of benchtop high-throughput sequencing platforms. Nature Biotechnology, 30, 434-439.

https://doi.org/10.1038/nbt.2198

Metzker M., 2010, Sequencing technologies - the next generation, Nat Rev Genet, 11: 31-46

https://doi.org/10.1038/nrg2626

Posey J., 2019, Genome sequencing and implications for rare disorders, Orphanet Journal of Rare Diseases, 14:153.

https://doi.org/10.1186/s13023-019-1127-0

Quattrocchi A., Fante Z., Fazio N., Romano S., Volonnino G., Fazio V., Santoro P., and Gennaro U., 2019, Personalized medicine in psychiatric disorders: prevention and bioethical questions, La Clinica terapeutica, 170(6): 421-424.

Rasmussen I., and Frikke-Schmidt R., 2023, Modifiable cardiovascular risk factors and genetics for targeted prevention of dementia, European Heart Journal, 44: 2526-2543.

https://doi.org/10.1093/eurheartj/ehad293

Roy A., Diao Y., Mauceli E., Shen Y., and Wu B., 2012, Massive genomic data processing and deep analysis, Proc, VLDB Endow., 5: 1906-1909.

https://doi.org/10.14778/2367502.2367534

Scott S., Sangkuhl K., Gardner E., Stein C., Hulot J., Hulot J., Johnson J., Roden D., Klein T., Shuldiner A., and Shuldiner A., 2011, Clinical pharmacogenetics implementation consortium guidelines for cytochrome p450-2c19 (cyp2c19) genotype and clopidogrel therapy, Clinical Pharmacology & Therapeutics, 90.

https://doi.org/10.1038/clpt.2011.132

Shendure J., Mitra R., Varma C., 2014, Advanced sequencing technologies: methods and goals. Nat Rev Genet, 5: 335-344

https://doi.org/10.1038/nrg1325

Tommel J., Kenis D., Lambrechts N., Brohet R.M., Swysen J., Mollen L., Hoefmans M., Pusparum M., Evers A.W.M., Ertaylan G., 2023; Personal Genomes in Practice: Exploring Citizen and Healthcare Professionals’ Perspectives on Personalized Genomic Medicine and Personal Health Data Spaces Using a Mixed-Methods Design, Genes, 14(4): 786.

https://doi.org/10.3390/genes14040786

Walker M., Nielsen J., Goddard E., Harris A., and Hutchison K., 2021, Induced pluripotent stem cell-based systems for personalising epilepsy treatment: research ethics challenges and new insights for the ethics of personalised medicine. ajob neuroscience, 13: 120-131.

https://doi.org/10.1080/21507740.2021.1949404

Wang X., Zhang S., Wu Y., and Yang X., 2021, Revealing potential drug-disease-gene association patterns for precision medicine, Scientometrics, 126: 3723-3748.

https://doi.org/10.1007/s11192-021-03892-4

Wei X., Ju X., Yi X., Zhu Q., Qu N., Liu T., Chen Y., Jiang H., Yang G., Zhen R., Lan Z., Qi M., Wang J., Yang Y., Chu Y., Li X., Guang Y., and Huang J., 2011, Identification of sequence variants in genetic disease-causing genes using targeted next-generation sequencing, PLoS ONE, 6(12):e29500.

https://doi.org/10.1371/journal.pone.0029500

Weirather J., Cesare M., Wang Y., Piazza P., Sebastiano V., Wang X., Buck D., and Au K., 2017, Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis, F1000Research, (1): 6.

https://doi.org/10.12688/f1000research.10571.1

Wright C., FitzPatrick D., and Firth H., 2018, Paediatric genomics: diagnosing rare disease in children, Nature Reviews Genetics, 19: 253-268.

https://doi.org/10.1038/nrg.2017.116

Zhao L., Zhang H., Kohnen M., Prasad K., Gu L., and Reddy A., 2019, Analysis of transcriptome and epitranscriptome in plants using pacbio iso-seq and nanopore-based direct rna sequencing,Frontiers in Genetics, (1): 10.

https://doi.org/10.3389/fgene.2019.00253

Zou Y., Zhu Y., Li Y., Wu F., and Wang J., 2021, Parallel computing for genome sequence processing, Briefings in bioinformatics.

https://doi.org/10.1093/bib/bbab070