Diagnostic testing is crucial in modern healthcare, providing essential health information and influencing clinical decisions and patient outcomes. Ensuring the validity and quality of these tests is vital, with stringent quality control and assurance procedures enforced by regulatory bodies and healthcare facilities. Reference materials (RMs) are essential for the accuracy and reliability of DNA-based diagnostic tests, serving as benchmarks for error detection, test validity, and consistency. This study reviews the necessity of effective control substances for precise diagnostic testing through a narrative literature review of synthetic DNA sequences, recombinant plasmids, genomic DNA, and cell lines as RMs, sourced from PubMed, Scopus, and Google Scholar over the last decade. Each RM type has specific advantages and disadvantages impacting diagnostic performance: gBlocks are highly specific but lack genomic complexity; recombinant plasmids offer flexibility but face stability and contamination issues; genomic DNA provides comprehensive diagnostic information but is complex and costly; cell lines simulate in vivo conditions well but are prone to genetic drift and contamination. The review emphasizes the critical role of RMs in DNA-based diagnostics and highlights challenges faced by Indonesian laboratories, recommending national coordination and international collaboration to enhance RMs’ availability, thereby improving patient outcomes and aligning with global standards.
Meilani ND, Malau J, Hermosaningtyas AA, Zahra AA, Kasasiah A, Rahmasari R, Lestari AN, Raekiansyah M. Reference materials for DNA-based diagnostics testing; principles, comparative analysis, contemporary applications, and future recommendation in Indonesia. J Appl Pharm Sci. 2024. Online First. http://doi.org/10.7324/JAPS.2025.211186
1. Balogh EP, Miller BT, Ball JR, editors. Improving diagnosis in health care. Washington, DC: National Academies Press; 2015.
2. DiGangi BA. Diagnostic testing. In: Miller L, Hurley KF, editors. Infectious disease management in animal shelters. Hoboken, NJ: Wiley; 2021. pp. 60–93.
3. Land KJ, Boeras DI, Chen XS, Ramsay AR, Peeling RW. Reassured diagnostics to inform disease control strategies, strengthen health systems and improve patient outcomes. Nat Microbiol. 2018;4:46–54. doi: https://doi.org/10.1038/s41564-018-0295-3
4. Ahmad-Nejad P, Ashavaid T, Vacaflores Salinas A, Huggett J, Harris K, Linder MW, et al. Current and future challenges in quality assurance in molecular diagnostics. Clinica Chimica Acta. 2021;519:239–246. doi: https://doi.org/10.1016/j.cca.2021.05.004
5. Devonshire AS, O’Sullivan DM, Honeyborne I, Jones G, Karczmarczyk M, Pavši? J, et al. The use of digital PCR to improve the application of quantitative molecular diagnostic methods for tuberculosis. BMC Infect Dis. 2016;16:1–10. doi: https://doi.org/10.1186/s12879-016-1696-7
6. van Pelt-Verkuil E, van Leeuwen WB. Quality assurance, management and control in molecular diagnostics. In: van Pelt-Verkuil E, van Leeuwen W, te Witt R, editors. Molecular Diagnostics. Singapore, Singapore: Springer; 2019.
7. International Organization for Standardization. Reference materials—Selected terms and definitions (ISO Guide 30:2015). Geneva, Switzerland: ISO; 2015. p. 8.
8. De la Salle B, Meijer P, Thomas A, Simundic AM. Special issue on external quality assessment in laboratory medicine—current challenges and future trends. Biochem Med (Zagreb). 2017;27:19–22. doi: https://doi.org/10.11613/BM.2017.003
9. Borysiak MD, Thompson MJ, Posner JD. Translating diagnostic assays from the laboratory to the clinic: analytical and clinical metrics for device development and evaluation. Lab Chip. 2016;16:1293–313. doi: https://doi.org/10.1039/C6LC00015K
10. Arnold JE, Camus MS, Freeman KP, Giori L, Hooijberg EH, Jeffery U, et al. ASVCP guidelines: principles of quality assurance and standards for veterinary clinical pathology (version 3.0): developed by the American Society for Veterinary Clinical Pathology’s (ASVCP) Quality Assurance and Laboratory Standards (QALS) Committee. Vet Clin Pathol. 2019;48:542–618. doi: http://doi.org/10.1111/vcp.12810
11. Gargis AS, Kalman L, Lubin IM. Assuring the quality of next-generation sequencing in clinical microbiology and public health laboratories. J Clin Microbiol. 2016;54:2857–65. doi: https://doi.org/10.1128/JCM.00949-16
12. Bhadra S, Paik I, Torres JA, Fadanka S, Gandini C, Akligoh H, et al. Preparation and use of cellular reagents: a low-resource molecular biology reagent platform. Curr Protoc. 2022;2:1–54. doi: https://doi.org/10.1002/cpz1.387
13. Whale AS, Bushell CA, Grant PR, Cowen S, Gutierrez Aguirre I, O’Sullivan DM, et al. Detection of rare drug resistance mutations by digital PCR in a human influenza a virus model system and clinical samples. J Clin Microbiol. 2016;54:392–400. doi: https://doi.org/10.1128/JCM.02611-15
14. Abdi G, Tarighat MA, Jain M, Tendulkar R, Tendulkar M, Barwant M. Revolutionizing genomics: exploring the potential of next-generation sequencing. In: Singh V, Kumar A, editors. Advances in Bioinformatics. Singapore, Singapore: Springer Nature;2024. pp. 1–33.
15. Ghasemi M, Nabipour I, Omrani A, Alipour Z, Assadi M. Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis. Am J Nucl Med Mol Imaging. 2016;6(6):310–27.
16. Huang F, Spangler JR, Huang AY. In vivo cloning of up to 16 kb plasmids in E. coli is as simple as PCR. PLoS One. 2017;12:1–21. doi: https://doi.org/10.1371/journal.pone.0183974
17. Wilding JL, Bodmer WF. Cancer cell lines for drug discovery and development. Cancer Res. 2014;74:2377–84. doi: https://doi.org/10.1158/0008-5472.CAN-13-2971
18. Xu L, Chen H, Canales M, Ciric L. Use of synthesized double-stranded gene fragments as qPCR standards for the quantification of antibiotic resistance genes. J Microbiol Methods. 2019;164:105670. doi: https://doi.org/10.1016/j.mimet.2019.105670
19. ARISE+ Indonesia. Amplifying the Importance of Reference Materials to Foster RMP Growth in Indonesia [Internet]. 2023 [cited 2024 May 30]. Available from: https://ariseplus-indonesia.org/en/activities/amplifying-the-importance-of-reference-materials-to-foster-rmp-growth-in-indonesia.html.
20. Prasetya B, Restu Wahono D, Yopi, Prasetya C. Innovation opportunity and challenge of standardization in response to COVID-19 pandemic and the socio-economic impact: a case study in Indonesia. Standards. 2022;2:66–82. doi: https://doi.org/10.3390/standards2010006
21. Cree IA, Deans Z, Ligtenberg MJL, Groenen P, Van Krieken JH, Normanno N, et al. Guidance for laboratories performing molecular pathology for cancer patients. J Clin Pathol. 2014;67:923–31. doi: https://doi.org/10.1136/jclinpath-2014-202404
22. Chan M, Jiang B, Tan TY. Using pooled recombinant plasmids as control materials for diagnostic real-time PCR. Clin Lab. 2016;62:1893–901. doi: https://doi.org/10.7754/Clin.Lab.2016.160114
23. Bogožalec Košir A, Cvelbar T, Kammel M, Grunert HP, Zeichhardt H, Milavec M. Digital PCR method for detection and quantification of specific antimicrobial drug-resistance mutations in human cytomegalovirus. J Virol Methods. 2020;281:113864. doi: https://doi.org/10.1016/j.jviromet.2020.113864
24. Rowlands V, Rutkowski AJ, Meuser E, Carr TH, Harrington EA, Barrett JC. Optimisation of robust singleplex and multiplex droplet digital PCR assays for high confidence mutation detection in circulating tumour DNA. Sci Rep. 2019;9:1–13. doi: https://doi.org/10.1038/s41598-019-49043-x
25. Sikkema AP, Tabatabaei SK, Lee Y, Lund S, Lohman GJS. High-complexity one-pot golden gate assembly. Curr Protoc. 2023;3:e882. doi: https://doi.org/10.1002/cpz1.882
26. Martínez-García E, Benedetti I, Hueso A, de Lorenzo V. Mining environmental plasmids for synthetic biology parts and devices. In: Marcelo E. Tolmasky, Juan C. Alonso, editors. Plasmids: Biology and Impact in Biotechnology and Discovery. Hoboken, NJ: John Wiley & Sons; 2015. pp. 633–49. doi: https://doi.org/10.1128/9781555818982.ch32
27. Roquet N, Soleimany AP, Ferris AC, Aaronson S, Lu TK. Synthetic recombinase-based state machines in living cells. Science. 2016 Jul 22;353(6297):aad8559. doi: https://doi.org/10.1126/science.aad8559
28. Green MR, Sambrook J. Cloning in plasmid vectors: directional cloning. Cold Spring Harb Protoc. 2020;2020:485–8. doi: https://doi.org/10.1101/pdb.prot101238
29. Kit M. Development of recombinant positive control for African Swine fever virus pcr detection. Biotechnol Acta. 2020;13:58–63. doi: https://doi.org/10.15407/biotech13.06.058
30. Fang LT, Zhu B, Zhao Y, Chen W, Yang Z, Kerrigan L, et al. Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing. Nat Biotechnol. 2021;39:1151–60. doi: https://doi.org/10.1038/s41587-021-00993-6
31. Garcia EP, Minkovsky A, Jia Y, Ducar MD, Shivdasani P, Gong X, et al. Validation of oncopanel a targeted next-generation sequencing assay for the detection of somatic variants in cancer. Arch Pathol Lab Med. 2017;141:751–8. doi: https://doi.org/10.5858/arpa.2016-0527-OA
32. Segeritz CP, Vallier L. Cell culture: growing cells as model systems in vitro. In: Jalali M, Saldanha FYL, Jalali M, editors. Basic Science Methods for Clinical Researchers. London,UK: Elsevier Inc.; 2017.
33. El Harane S, Zidi B, El Harane N, Krause KH, Matthes T, Preynat-Seauve O. Cancer spheroids and organoids as novel tools for research precision medicine. Cells. 2023;12:1001
34. Lin G, Zhang K, Han Y, Li J. Quality control materials for pharmacogenomic testing in the clinic. Clin Chem Lab Med. 2017;55:926–33. doi: https://doi.org/10.1515/cclm-2016-0755
35. Cirmena G, Dameri M, Ravera F, Fregatti P, Ballestrero A, Zoppoli G. Assessment of circulating nucleic acids in cancer: from current status to future perspectives and potential clinical applications. Cancers (Basel). 2021;13:1–24. doi: https://doi.org/10.3390/cancers13143460
36. Flagg MP, Kao A, Hampton RY. Integrating after CEN Excision (ICE) Plasmids: Combining the ease of yeast recombination cloning with the stability of genomic integration. Yeast. 2019;36:593–605. doi: https://doi.org/10.1002/yea.3400
37. Volckmar A, Sültmann H, Riediger A, Fioretos T, Schirmacher P, Endris V, et al. A field guide for cancer diagnostics using cell-free DNA: from principles to practice and clinical applications. Genes Chromosomes Cancer. 2018;57:123–39. doi: https://doi.org/10.1002/gcc.22517
38. Kasu M, Shires K. The validation of forensic DNA extraction systems to utilize soil contaminated biological evidence. Leg Med. 2015;17:232–8. doi: https://doi.org/10.1016/j.legalmed.2015.01.004
39. Mirabelli P, Coppola L, Salvatore M. Cancer cell lines are useful model systems for medical research. Cancers (Basel). 2019;11:1098. doi: https://doi.org/10.3390/cancers11081098
40. Geraghty RJ, Capes-Davis A, Davis JM, Downward J, Freshney RI, Knezevic I, et al. Guidelines for the use of cell lines in biomedical research. Br J Cancer. 2014;111:1021–46. doi: https://doi.org/10.1038/bjc.2014.166
41. El-Metwally S. Next Generation Sequencing Technologies and Challenges in Sequence Assembly. Volume 7 of SpringerBriefs in Systems Biology. New York, NY: Springer; 2014.
42. Marma MS. Terminator to reversible terminator: generational shift in DNA sequencing technologies. In: Islam MM, Hossain MM, editors. Science and Technology Innovation for a Sustainable Economy. Cham, Switzerland: Springer International Publishing; 2020. pp. 43–58.
43. Sturk Andreaggi K, Peck MA, Boysen C, Dekker P, McMahon TP, Marshall CK. AQME: A forensic mitochondrial DNA analysis tool for next-generation sequencing data. Forensic Sci Int Genet. 2017;31:189–97. doi: https://doi.org/10.1016/j.fsigen.2017.09.010
44. Li Z, Sinha A, Zhang Y, Tanner N, Cheng HT, Premsrirut P, et al. Extraction-free LAMP assays for generic detection of Old World Orthopoxviruses and specific detection of Mpox virus. Sci Rep. 2023;13:1–12. doi: https://doi.org/10.1038/s41598-023-48391-z
45. Head SR, Kiyomi Komori H, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR, et al. Library construction for next-generation sequencing: overviews and challenges. Biotechniques. 2014;56:61–77. doi: https://doi.org/10.2144/000114133
46. Choochuen P, Laochareonsuk W, Tanaanantarak P, Kanjanapradit K, Sangkhathat S. Juvenile hyaline fibromatosis: report of a case with a novel ANTXR2 gene mutation. Am J Case Rep. 2022;23:1–6. doi: https://doi.org/10.12659/AJCR.935921
47. McCombie WR, McPherson JD, Mardis ER. Next-generation sequencing technologies. Cold Spring Harb Perspect Med. 2019;9:a036798. doi: https://doi.org/10.1101/cshperspect.a036798
48. Tripathi R, Sharma P, Chakraborty P, Varadwaj PK. Next-generation sequencing revolution through big data analytics. Front Life Sci. 2016;9:119–49. doi: https://doi.org/10.1080/21553769.2016.1178180
49. Bai J, Jhaney I, Wells J. Developing a reproducible microbiome data analysis pipeline using the amazon web services cloud for a cancer research group: proof-of-concept study. JMIR Med Inform. 2019;7:e14667. doi: https://doi.org/10.2196/14667
50. Gonzalo R, Sánchez A. Introduction to microarrays Technology and Data Analysis. 1st ed. Comprehen Anal Chem. 2018;82:37–69.
51. Miller S, Karaoz U, Brodie E, Dunbar S. Solid and suspension microarrays for microbial diagnostics. Methods Microbiol. 2015;42:395–431.
52. Ona S. Sanger sequencing [Internet]. BioRender. 2020. Available from: https://app.biorender.com/biorender-templates/figures/all/t-5ef132f6c7bcd500b388a9c3-sanger-sequencing
53. Ona S. Next Generation Sequencing (Illumina) [Internet]. BioRender. 2020. Available from: https://app.biorender.com/biorender-templates/figures/all/t-5ef134a11c72b100ad8d13ac-next-generation-sequencing-illumina
54. Agapito G. Computer Tools to Analyze Microarray Data. Methods Mol Biol. 2019;1986:267–82. doi: https://doi.org/10.1007/978-1-4939-9442-7_13
55. Ziemann M, Reimann V, Liang Y, Shi Y, Ma H, Xie Y, et al. CvkR is a MerR-type transcriptional repressor of class 2 type V-K CRISPR-associated transposase systems. Nat Commun. 2023;14:1–16. doi: https://doi.org/10.1038/s41467-023-36542-9
56. Dugoff L, Norton ME, Kuller JA. The use of chromosomal microarray for prenatal diagnosis. Am J Obstet Gynecol. 2016;215:B2–B9. doi: https://doi.org/10.1016/j.ajog.2016.07.016
57. Pavši? J, Žel J, Milavec M. Assessment of the real-time PCR and different digital PCR platforms for DNA quantification. Anal Bioanal Chem. 2016;408:107–21. doi: https://doi.org/10.1007/s00216-015-9107-2
58. Auliah FN, Lawi A, Thamrin SA, Budiman E. Selection of informative genes to classify type 2 diabetes mellitus using support vector machine. 5th International Conference on Computing Engineering and Design, ICCED 2019. Singapore, Singapore: Institute of Electrical and Electronics Engineers Inc; 2019.
59. Kralik P, Ricchi M. A basic guide to real time PCR in microbial diagnostics: definitions, parameters, and everything. Front Microbiol. 2017;8:1–9. doi: https://doi.org/10.3389/fmicb.2017.00108
60. Park M, Won J, Choi BY, Lee CJ. Optimization of primer sets and detection protocols for SARS-CoV-2 of coronavirus disease 2019 (COVID-19) using PCR and real-time PCR. Exp Mol Med. 2020;52:963–77. doi: https://doi.org/10.1038/s12276-020-0452-7
61. Patel JC, Lucchi NW, Srivastava P, Lin JT, Sug-Aram R, Aruncharus S, et al. Field evaluation of a real-time fluorescence loop-mediated isothermal amplification assay, realamp, for the diagnosis of Malaria in Thailand and India. J Infect Dis. 2014;210:1180–7. doi: https://doi.org/10.1093/infdis/jiu252
62. Ramírez JC, Cura CI, Da Cruz Moreira O, Lages-Silva E, Juiz N, Velázquez E, et al. Analytical validation of quantitative real-time PCR methods for quantification of trypanosoma cruzi DNA in blood samples from chagas disease patients. J Mol Diagn. 2015;17:605–15. doi: https://doi.org/10.1016/j.jmoldx.2015.04.010
63. Long S, Berkemeier B. Ultrasensitive detection and quantification of viral nucleic acids with Raindance droplet digital PCR (ddPCR). Methods. 2022;201:49–64. doi: https://doi.org/10.1016/j.ymeth.2021.04.025
64. Whale AS, Jones GM, Pavši? J, Dreo T, Redshaw N, Akyürek S, et al. Assessment of digital PCR as a primary reference measurement procedure to support advances in precision medicine. Clin Chem. 2018;64:1296–307. doi: https://doi.org/10.1373/clinchem.2017.285478
65. Zhang H, Cao L, Brodsky J, Gablech I, Xu F, Li Z, et al. Quantitative or digital PCR? a comparative analysis for choosing the optimal one for biosensing applications. Trends Anal Chem. 2024;174:117676. doi: https://doi.org/10.1016/j.trac.2024.117676
66. Gutteridge A, Rathbone VM, Gibbons R, Bi M, Archard N, Davies KEJ, et al. Digital PCR analysis of circulating tumor DNA: a biomarker for chondrosarcoma diagnosis, prognostication, and residual disease detection. Cancer Med. 2017;6:2194–202. doi: https://doi.org/10.1002/cam4.1146
67. Chen B, Xie Y, Zhang N, Li W, Liu C, Li D, et al. Evaluation of droplet digital PCR assay for the diagnosis of candidemia in blood samples. Front Microbiol. 2021;12:700008. doi: https://doi.org/10.3389/fmicb.2021.700008
68. Kadri K. Polymerase chain reaction (PCR): principle and applications. In: Nagpal ML, Boldura OM, Balta C, Enany S, editors. Synthetic Biology - New Interdisciplinary Science. IntechOpen; 2020.
69. Findlay SD, Vincent KM, Berman JR, Postovit LM. A digital pcr-based method for efficient and highly specific screening of genome edited cells. PLoS One. 2016;11:1–17. doi: https://doi.org/10.1371/journal.pone.0153901
70. Singh C, Roy-Chowdhuri S. Quantitative real-time PCR: recent advances. Methods Mol Biol. 2016;1392:161–76.
71. Ricchi M, Bertasio C, Boniotti MB, Vicari N, Russo S, Tilola M, et al. Comparison among the quantification of bacterial pathogens by qPCR, dPCR, and cultural methods. Front Microbiol. 2017;8:1174. doi: https://doi.org/10.3389/fmicb.2017.01174
72. Engstrom-Melnyk J, Rodriguez PL, Peraud O, Hein RC. Clinical applications of quantitative real-time PCR in virology. Methods Microbiol. 2015;42:161–97.
73. Levin RE, Ekezie F-GC, Sun D-W. DNA-based technique: polymerase chain reaction (PCR). In: Da-Wen Sun. Modern Techniques for Food Authentication. Amsterdam, The Netherlands: Elsevier; 2018. pp. 527–616.
74. Ellis JA, Ong B. The MassARRAY® system for targeted SNP genotyping. Methods Mol Biol. 2017;1492:77–94. doi: https://doi.org/10.1007/978-1-4939-6442-0_5
75. Navarro E, Serrano-Heras G, Castaño MJ, Solera J. Real-time PCR detection chemistry. Clinica Chimica Acta. 2015;439:231–50. doi: https://doi.org/10.1016/j.cca.2014.10.017
76. Chen R, Lu X, Li M, Chen G, Deng Y, Du F, et al. Polymerase chain reaction using “V” shape thermal cycling program. Theranostics. 2019;9:1572–9. doi: https://doi.org/10.7150/thno.31986
77. VanInsberghe M, Zahn H, White AK, Petriv OI, Hansen CL. Highly multiplexed single-cell quantitative PCR. PLoS One. 2018;13:e0191601. doi: https://doi.org/10.1371/journal.pone.0191601
78. Koessler T, Addeo A, Nouspikel T. Implementing circulating tumor DNA analysis in a clinical laboratory: a user manual. Adv Clin Chem. 2019;89:131–88.
79. Cao L, Cui X, Hu J, Li Z, Choi JR, Yang Q, et al. Advances in digital polymerase chain reaction (dPCR) and its emerging biomedical applications. Biosens Bioelectron. 2017;90:459–74. doi: https://doi.org/10.1016/j.bios.2016.09.082
80. Sabatini LM, Mathews C, Ptak D, Doshi S, Tynan K, Hegde MR, et al. Genomic sequencing procedure microcosting analysis and health economic cost-impact analysis: a report of the association for molecular pathology. J Mol Diagn. 2016;18:319–28. doi: https://doi.org/10.1016/j.jmoldx.2015.11.010
81. Deharvengt SJ, Petersen LM, Jung HS, Tsongalis GJ. Nucleic acid analysis in the clinical laboratory. In: Clarke W, Marzinke M, editors. Contemporary practice in clinical chemistry. Amsterdam, The Netherlands: Elsevier Science; 2020. pp. 215–34.
82. Stubbs SCB, Blacklaws BA, Yohan B, Yudhaputri FA, Hayati RF, Schwem B, et al. Assessment of a multiplex PCR and Nanopore-based method for dengue virus sequencing in Indonesia. Virol J. 2020;17:1–13. doi: https://doi.org/10.1186/s12985-020-1294-6
83. Llamas B, Valverde G, Fehren-Schmitz L, Weyrich LS, Cooper A, Haak W. From the field to the laboratory: controlling DNA contamination in human ancient DNA research in the high-throughput sequencing era. Sci Technol Archaeol Res. 2017;3:1–14. doi: https://doi.org/10.1080/20548923.2016.1258824
84. Zaman GS. Introductory Chapter: History and Scope of Quality Control in Laboratories. Quality Control in Laboratory. 2018;1–8. doi: https://doi.org/10.5772/intechopen.74593
85. Seo M, Takabatake R, Izumi S, Unno H, Kawashima Y, Ki U, et al. Novel bioprinting application for the production of reference material containing a defined copy number of target DNA. Anal Chem. 2019;91:12733–40. doi: https://doi.org/10.1021/acs.analchem.9b02207
86. Pinheiro LB, O’Brien H, Druce J, Do H, Kay P, Daniels M, et al. Interlaboratory reproducibility of droplet digital polymerase chain reaction using a new DNA reference material format. Anal Chem. 2017;89:11243–51. doi: https://doi.org/10.1021/acs.analchem.6b05032
87. Niu Q, Su X, Lian L, Huang J, Xue S, Zhou W, et al. Developing qualitative plasmid DNA reference materials to detect mechanisms of quinolone and fluoroquinolone resistance in foodborne pathogens. Foods. 2022;11:154. doi: https://doi.org/10.3390/foods11020154
88. Mano J, Hatano S, Futo S, Yoshii J, Nakae H, Naito S, et al. Development of a reference material of a single DNA molecule for the quality control of PCR testing. Anal Chem. 2014;86:8621–7. doi: https://doi.org/10.1021/ac501314s
89. Yao Z, Liu H, Xie F, Fischer S, Adkins RS, Aldridge AI, et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature. 2021;598:103–10. doi: https://doi.org/10.1038/s41586-021-03500-8
90. Axtner J, Crampton-Platt A, Hörig LA, Mohamed A, Xu CCY, Yu DW, et al. An efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies. Gigascience. 2019;8:giz029. doi: https://doi.org/10.1093/gigascience/giz029
91. Debode F, Janssen E, Bragard C, Berben G. Detection by real-time PCR and pyrosequencing of the cry 1Ab and cry 1Ac genes introduced in genetically modified (GM) constructs. Food Additives Contaminants: Part A. 2017;34:1398–409. doi: https://doi.org/10.1080/19440049.2017.1317925
92. Rehder C, Bean LJH, Bick D, Chao E, Chung W, Das S, et al. Next-generation sequencing for constitutional variants in the clinical laboratory, 2021 revision: a technical standard of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2021;23:1399–415. doi: https://doi.org/10.1038/s41436-021-01139-4
93. Chen X, Ji Y, Li K, Wang X, Peng C, Xu X, et al. Development of a duck genomic reference material by digital PCR platforms for the detection of meat adulteration. Foods. 2021;10:1890. doi: https://doi.org/10.3390/foods10081890
94. Ma Y, Duan X, Huang J. DNA Hydrogels as functional materials and their biomedical applications. Adv Funct Mater. 2024;34:1–20. doi: https://doi.org/10.1002/adfm.202309070
95. Hardwick SA, Deveson IW, Mercer TR. Reference standards for next-generation sequencing. Nat Rev Genet. 2017;18:473–84. doi: https://doi.org/10.1038/nrg.2017.44
96. Zook JM, Catoe D, McDaniel J, Vang L, Spies N, Sidow A, et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci Data. 2016;3:160025. doi: https://doi.org/10.1038/sdata.2016.25
97. Kalman LV, Datta V, Williams M, Zook JM, Salit ML, Han JY. Development and characterization of reference materials for genetic testing: focus on public partnerships. Ann Lab Med. 2016;36:513–20. doi: https://doi.org/10.3343/alm.2016.36.6.513
98. Li Z, Li X, Wang C, Song G, Pi L, Zheng L, et al. One novel multiple-target plasmid reference molecule targeting eight genetically modified canola events for genetically modified canola detection. J Agric Food Chem. 2017;65:8489–500. doi: https://doi.org/10.1021/acs.jafc.7b02453
99. Li L, Chen J, Moore K, Jin W. Method validation. In: Shillito R, Shan G, editors. Application of sampling and detection methods in agricultural plant biotechnology. Amsterdam, The Netherlands: Elsevier; 2022. pp. 67–84.
100. Ye X, Lei B. The current status and trends of DNA extraction. BioEssays. 2023;45:e2200242. doi: https://doi.org/10.1002/bies.202200242
101. Qin Z, Wang C, Zhang J, Wang Z, Wei Y, Li Y, et al. DNA-based materials inspired by natural extracellular DNA. Adv Funct Mater. 2023;33. doi: https://doi.org/10.1002/adfm.202211669
102. Ma W, Zhan Y, Zhang Y, Mao C, Xie X, Lin Y. The biological applications of DNA nanomaterials: current challenges and future directions. Signal Transduct Target Ther. 2021;6:1–28. doi: https://doi.org/10.1038/s41392-021-00727-9
103. Scott SA, Scott ER, Seki Y, Chen AJ, Wallsten R, Owusu Obeng A, et al. Development and analytical validation of a 29 gene clinical pharmacogenetic genotyping panel: Multi-Ethnic Allele and Copy Number Variant Detection. Clin Transl Sci. 2021;14:204–13. doi: https://doi.org/10.1111/cts.12844
104. Gaedigk A, Turner A, Everts RE, Scott SA, Aggarwal P, Broeckel U, et al. Characterization of reference materials for genetic testing of CYP2D6 alleles. J Mol Diagn. 2019;21:1034–52. doi: https://doi.org/10.1016/j.jmoldx.2019.06.007
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