Review
BibTex RIS Cite

MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları

Year 2022, Volume: 19 Issue: 2, 152 - 159, 01.08.2022
https://doi.org/10.32707/ercivet.1142293

Abstract

MikroRNA (miRNA) ekspresyonlarının belirlenmesinde RT-qPCR, mikroarray platformları ve miRNA dizileme en yaygın kullanılan tekniklerdir. Tüm bu tekniklerin kullanıldığı çalışmalarda en önemli hususlardan biri verilerin uygun normalizasyon yöntemi ile normalize edilmesidir. Normalizasyon ile biyolojik ve teknik varyasyonların sonuçlar üzerine olan etkisinin elimine edilmesi amaçlanmaktadır. MiRNA ekspresyonu çalışmalarında, farklı tekniklerden elde edilen verilerin normalizasyonunda kullanılan çok sayıda normalizasyon yaklaşımı kullanılmaktadır. Bu derlemede, miRNA ekspresyonu çalışmalarında en yaygın kullanılan normalizasyon yaklaşımları hakkında bilgiler özetlenmiştir.

References

  • Ballman KV, Grill DE, Oberg AL, Therneau TM. Fas-ter cyclic loess: normalizing RNA arrays via linear models. Bioinformatics 2004; 20(16): 2778-86.
  • Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high den-sity oligonucleotide array data based on variance and bias. Bioinformatics 2003; 19: 185-93.
  • Bustin SA, Benes V, Garson J, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT. The MIQE guide-lines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 2009; 55: 611-22.
  • Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GA, Zumla A. The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem 2005; 344 (1): 141-3.
  • Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Cas-tel D, Estelle J, Guernec G. A comprehensive eva-luation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 2013; 14: 671-83.
  • Donati S, Ciuffi S, Brandi ML. Human circulating miR-NAs real-time qRT-PCR-based analysis: an over-view of endogenous reference genes used for data normalization. Int J Mol Sci 2019; 20: 1-19.
  • Faraldi M, Gomarasca M, Banfi G, Lombardi G. Free circulating miRNAs measurement in clinical set-tings: the still unsolved issue of the normalization. Adv Clin Chem 2018; 87: 113-39.
  • Gershon D. Microarray technology: An array of op-portunities. Nature 2002; 416: 885-91.
  • Hammond SM. An overview of microRNAs. Adv Drug Deliv Rev 2015; 87: 3-14.
  • Heneghan HM, Miller N, Lowery AJ, Sweeney KJ, Newell J, Kerin M. Circulating micrornas as novel minimally invasive biomarkers for breast cancer. Ann Surg 2010; 251: 499-505.
  • Inyawilert W, Fu TY, Lin CT, Tang PC. Let-7-mediated suppression of mucin 1 expression in the mouse uterus during embryo implantation. J Reprod Dev 2015; 61: 138-44.
  • Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligo-nucleotide array probe level data. Biostatistics 2003; 4(2): 249-64.
  • Izumi H, Kosaka N, Shimizu T, Sekine K, Ochiya T, Takase M. Bovine milk contains microRNA and messenger RNA that are stable under degradative conditions. J Dairy Sci 2012; 95(9): 4831-41.
  • Korkmaz Ağaoğlu Ö, Sidekli Ö. Kantitatif RT-PCR (RT-qPCR) Çalışmalarında uygun housekeeping genlerin (HKGs) seçimi ve validasyonu. Erciyes Üniv Vet Fak Derg 2020; 17(1): 76-83.
  • Lange T, Stracke S, Rettig R, Lendeckel U, Kuhn J, Schlüter R, Rippe V, Endlich K, Endlich N. Identification of miR-16 as an endogenous reference gene for the normalization of urinary exosomal miRNA expression data from CKD patients. PLoS One 2017; 12(8); 1-13.
  • Lee Y, Ahn C, Han J, Choi H, Kim J, Yim J, Lee J, Provost P, Radmark O, Kim S, Kim VN. The nuc-lear RNase III Drosha initiates microRNA proces-sing. Nature 2003; 425: 415-9.
  • Liu CG, Calin GA, Volinia S, Croce CM. MicroRNA expression profiling using microarrays. Nat Protoc 2008; 3(4): 563-78.
  • Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods 2001; 25(4): 402-8.
  • López-Romero P, González MA, Callejas S, Dopazo A, Irizarry RA. Processing of agilent microRNA array data. BMC Res Notes 2010; 3(1); 1-6.
  • Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15(550): 1-21.
  • Lund E, Güttinger S, Calado A, Dahlberg JE, Kutay U. Nuclear export of microRNA precursors. Science 2004; 303(5654): 95-8.
  • Mandruzzato S. Technological platforms for microarray gene expression profiling. Simone M. eds. In: Microarray Technology and Cancer Gene Profiling, New York: Springer Science+Business, 2007; pp.12-8.
  • Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol 2009; 10(6): 1-10.
  • Metpally RP, Nasser S, Malenica I, Courtright A, Carlson E, Ghaffari L, Villa S, Tembe W, Keuren-Jensen V. Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model. Front Genet 2013; 4(20): 1-13.
  • Meyer SU, Pfaffl MW, Ulbrich SE. Normalization stra-tegies for microRNA profiling experiments: A ‘normal’way to a hidden layer of complexity? Bio-technol Lett 2010; 32(12): 1777-88.
  • Montenegro D, Romero R, Kim SS, Tarca AL, Draghici S, Kusanovic JP, Kim JS, Lee DC, Erez O, Gotsch F, Hassan SS. Expression patterns of mic-roRNAs in the chorioamniotic membranes: a role for microRNAs in human pregnancy and parturition. J Pathol 2019; 217(1): 113-21.
  • Pradervand S, Weber J, Thomas J, Bueno M, Wirapati P, Lefort K, Dotto GP, Harshman K, Impact of normalization on miRNA microarray expression profiling. RNA 2009; 15(3): 493-501.
  • Pritchard CC, Cheng HH, Tewari M. MicroRNA profi-ling: Approaches and considerations. Nat Rev Genet 2012; 13(5): 358-69.
  • Qureshi R, Sacan A. A novel method for the normali-zation of microRNA RT-PCR data. BMC Med Genomics 2013; 6(1): 1-13.
  • Rao Y, Lee Y, Jarjoura D, Ruppert AS, Liu CG, Hsu JC, Hagan JP. A comparison of normalization tech-niques for microRNA microarray data. Stat Appl Genet Mol Biol 2008; 7(1); 1-18.
  • Redshaw N, Wilkes T, Whale A, Cowen S, Huggett J, Foy CA. A comparison of miRNA isolation and RT-qPCR technologies and their effects on quantifica-tion accuracy and repeatability. Biotechniques 2013; 54(3): 155-64.
  • Ritchie ME, Phipson B, Wu DI, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expres-sion analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43 (7): 1-13.
  • Robinson MD, Mccarthy DJ, Smyth GK. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010: 26, 139-40.
  • Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 2010; 11(3): 1-9.
  • Saliminejad K, Khorram Khorshid HR, Soleymani Fard S, Ghaffari SH. An overview of microRNAs: Biology, functions, therapeutics, and analysis met-hods. J Cell Physiol 2019; 234(5): 5451-65.
  • Schwarzenbach H, Da Silva AM, Calin G, Pantel K. Data normalization strategies for microRNA quanti-fication. Clin Chem 2015; 61(11): 1333-42.
  • Sidekli Ö, Korkmaz Ağaoğlu Ö. Kantitatif RT-PCR (RT-qPCR) ile mikroRNA (miRNA) ekspresyon profillemesi. Erciyes Üniv Vet Fak Derg 2021; 18(1): 48-56.
  • Sidekli Ö, Korkmaz Ağaoğlu Ö. Gebelik sürecinde rol oynayan mikroRNA (miRNA)’lar. Lalahan Hay Araşt Enst Derg 2019; 59(1): 36-48.
  • Tam S, Tsao MS, McPherson JD. Optimization of miRNA-seq data preprocessing. Brief Bioinform 2015; 16(6): 950-63.
  • Wu D, Hu Y, Tong S, Williams BR, Smyth GK, Gan-tier MP. The use of miRNA microarrays for the analysis of cancer samples with global miRNA decrease. RNA 2013; 19(7): 876-88.
  • Zalewski K, Misiek M, Kowalik A, Bakuła-Zalewska E, Kopczyński J, Zielińska A, Bidziński M, Rad-ziszewski J, Góźdź S, Kowalewska M. Normalizers for microrna quantification in plasma of patients with vulvar intraepithelial neoplasia lesions and vulvar carcinoma. Tumour Biol 2017; 39: 1-6.
  • Zhang L, Liu XR, Liu JZ, Song YX, Zhou ZQ, Cao BY. miR-182 selectively targets HOXA10 in goat endometrial epithelium cells in vitro. Reprod Domest Anim 2017; 52(6): 1081-92.
  • Zheng G, Wang H, Zhang X, Yang Y, Wang L, Du L, Li W, Li J, Qu A, Liu Y, Wang C. Identification and validation of reference genes for qPCR detection of serum microRNAs in colorectal adenocarcinoma patients. PLoS ONE 2013; 8: 1-10.
  • Zhu HT, Dong QZ, Wang G, Zhou HJ, Ren N, Jia HL, Ye QH, Qin LX. Identification of suitable reference genes for qRT-PCR analysis of circulating microR-NAs in hepatitis B virus-infected patients. Mol Biotechnol 2012; 50: 49-56.
  • Zhu J, He F, Hu S, Yu J. On the nature of human housekeeping genes. Trends Genet 2008; 24(10): 481-4.

Commonly Used Normalization Approaches in MicroRNA Expression Profiling

Year 2022, Volume: 19 Issue: 2, 152 - 159, 01.08.2022
https://doi.org/10.32707/ercivet.1142293

Abstract

RT-qPCR, microarray platforms and miRNA sequencing are the most common techniques used to determine microRNA (miRNA) expressions. One of the most important issues in studies these techniques are used is the normalization of the data by using appropriate normalization method. The purpose of normalization is to eliminate the effects of biological and technical variations on study results. Numerous normalization approaches are used for normalization of data obtained from different techniques in miRNA expression studies. In this review, information about the most commonly used normalization approaches in miRNA expression studies is summarized.

References

  • Ballman KV, Grill DE, Oberg AL, Therneau TM. Fas-ter cyclic loess: normalizing RNA arrays via linear models. Bioinformatics 2004; 20(16): 2778-86.
  • Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high den-sity oligonucleotide array data based on variance and bias. Bioinformatics 2003; 19: 185-93.
  • Bustin SA, Benes V, Garson J, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT. The MIQE guide-lines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 2009; 55: 611-22.
  • Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GA, Zumla A. The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem 2005; 344 (1): 141-3.
  • Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Cas-tel D, Estelle J, Guernec G. A comprehensive eva-luation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 2013; 14: 671-83.
  • Donati S, Ciuffi S, Brandi ML. Human circulating miR-NAs real-time qRT-PCR-based analysis: an over-view of endogenous reference genes used for data normalization. Int J Mol Sci 2019; 20: 1-19.
  • Faraldi M, Gomarasca M, Banfi G, Lombardi G. Free circulating miRNAs measurement in clinical set-tings: the still unsolved issue of the normalization. Adv Clin Chem 2018; 87: 113-39.
  • Gershon D. Microarray technology: An array of op-portunities. Nature 2002; 416: 885-91.
  • Hammond SM. An overview of microRNAs. Adv Drug Deliv Rev 2015; 87: 3-14.
  • Heneghan HM, Miller N, Lowery AJ, Sweeney KJ, Newell J, Kerin M. Circulating micrornas as novel minimally invasive biomarkers for breast cancer. Ann Surg 2010; 251: 499-505.
  • Inyawilert W, Fu TY, Lin CT, Tang PC. Let-7-mediated suppression of mucin 1 expression in the mouse uterus during embryo implantation. J Reprod Dev 2015; 61: 138-44.
  • Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligo-nucleotide array probe level data. Biostatistics 2003; 4(2): 249-64.
  • Izumi H, Kosaka N, Shimizu T, Sekine K, Ochiya T, Takase M. Bovine milk contains microRNA and messenger RNA that are stable under degradative conditions. J Dairy Sci 2012; 95(9): 4831-41.
  • Korkmaz Ağaoğlu Ö, Sidekli Ö. Kantitatif RT-PCR (RT-qPCR) Çalışmalarında uygun housekeeping genlerin (HKGs) seçimi ve validasyonu. Erciyes Üniv Vet Fak Derg 2020; 17(1): 76-83.
  • Lange T, Stracke S, Rettig R, Lendeckel U, Kuhn J, Schlüter R, Rippe V, Endlich K, Endlich N. Identification of miR-16 as an endogenous reference gene for the normalization of urinary exosomal miRNA expression data from CKD patients. PLoS One 2017; 12(8); 1-13.
  • Lee Y, Ahn C, Han J, Choi H, Kim J, Yim J, Lee J, Provost P, Radmark O, Kim S, Kim VN. The nuc-lear RNase III Drosha initiates microRNA proces-sing. Nature 2003; 425: 415-9.
  • Liu CG, Calin GA, Volinia S, Croce CM. MicroRNA expression profiling using microarrays. Nat Protoc 2008; 3(4): 563-78.
  • Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods 2001; 25(4): 402-8.
  • López-Romero P, González MA, Callejas S, Dopazo A, Irizarry RA. Processing of agilent microRNA array data. BMC Res Notes 2010; 3(1); 1-6.
  • Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15(550): 1-21.
  • Lund E, Güttinger S, Calado A, Dahlberg JE, Kutay U. Nuclear export of microRNA precursors. Science 2004; 303(5654): 95-8.
  • Mandruzzato S. Technological platforms for microarray gene expression profiling. Simone M. eds. In: Microarray Technology and Cancer Gene Profiling, New York: Springer Science+Business, 2007; pp.12-8.
  • Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol 2009; 10(6): 1-10.
  • Metpally RP, Nasser S, Malenica I, Courtright A, Carlson E, Ghaffari L, Villa S, Tembe W, Keuren-Jensen V. Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model. Front Genet 2013; 4(20): 1-13.
  • Meyer SU, Pfaffl MW, Ulbrich SE. Normalization stra-tegies for microRNA profiling experiments: A ‘normal’way to a hidden layer of complexity? Bio-technol Lett 2010; 32(12): 1777-88.
  • Montenegro D, Romero R, Kim SS, Tarca AL, Draghici S, Kusanovic JP, Kim JS, Lee DC, Erez O, Gotsch F, Hassan SS. Expression patterns of mic-roRNAs in the chorioamniotic membranes: a role for microRNAs in human pregnancy and parturition. J Pathol 2019; 217(1): 113-21.
  • Pradervand S, Weber J, Thomas J, Bueno M, Wirapati P, Lefort K, Dotto GP, Harshman K, Impact of normalization on miRNA microarray expression profiling. RNA 2009; 15(3): 493-501.
  • Pritchard CC, Cheng HH, Tewari M. MicroRNA profi-ling: Approaches and considerations. Nat Rev Genet 2012; 13(5): 358-69.
  • Qureshi R, Sacan A. A novel method for the normali-zation of microRNA RT-PCR data. BMC Med Genomics 2013; 6(1): 1-13.
  • Rao Y, Lee Y, Jarjoura D, Ruppert AS, Liu CG, Hsu JC, Hagan JP. A comparison of normalization tech-niques for microRNA microarray data. Stat Appl Genet Mol Biol 2008; 7(1); 1-18.
  • Redshaw N, Wilkes T, Whale A, Cowen S, Huggett J, Foy CA. A comparison of miRNA isolation and RT-qPCR technologies and their effects on quantifica-tion accuracy and repeatability. Biotechniques 2013; 54(3): 155-64.
  • Ritchie ME, Phipson B, Wu DI, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expres-sion analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43 (7): 1-13.
  • Robinson MD, Mccarthy DJ, Smyth GK. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010: 26, 139-40.
  • Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 2010; 11(3): 1-9.
  • Saliminejad K, Khorram Khorshid HR, Soleymani Fard S, Ghaffari SH. An overview of microRNAs: Biology, functions, therapeutics, and analysis met-hods. J Cell Physiol 2019; 234(5): 5451-65.
  • Schwarzenbach H, Da Silva AM, Calin G, Pantel K. Data normalization strategies for microRNA quanti-fication. Clin Chem 2015; 61(11): 1333-42.
  • Sidekli Ö, Korkmaz Ağaoğlu Ö. Kantitatif RT-PCR (RT-qPCR) ile mikroRNA (miRNA) ekspresyon profillemesi. Erciyes Üniv Vet Fak Derg 2021; 18(1): 48-56.
  • Sidekli Ö, Korkmaz Ağaoğlu Ö. Gebelik sürecinde rol oynayan mikroRNA (miRNA)’lar. Lalahan Hay Araşt Enst Derg 2019; 59(1): 36-48.
  • Tam S, Tsao MS, McPherson JD. Optimization of miRNA-seq data preprocessing. Brief Bioinform 2015; 16(6): 950-63.
  • Wu D, Hu Y, Tong S, Williams BR, Smyth GK, Gan-tier MP. The use of miRNA microarrays for the analysis of cancer samples with global miRNA decrease. RNA 2013; 19(7): 876-88.
  • Zalewski K, Misiek M, Kowalik A, Bakuła-Zalewska E, Kopczyński J, Zielińska A, Bidziński M, Rad-ziszewski J, Góźdź S, Kowalewska M. Normalizers for microrna quantification in plasma of patients with vulvar intraepithelial neoplasia lesions and vulvar carcinoma. Tumour Biol 2017; 39: 1-6.
  • Zhang L, Liu XR, Liu JZ, Song YX, Zhou ZQ, Cao BY. miR-182 selectively targets HOXA10 in goat endometrial epithelium cells in vitro. Reprod Domest Anim 2017; 52(6): 1081-92.
  • Zheng G, Wang H, Zhang X, Yang Y, Wang L, Du L, Li W, Li J, Qu A, Liu Y, Wang C. Identification and validation of reference genes for qPCR detection of serum microRNAs in colorectal adenocarcinoma patients. PLoS ONE 2013; 8: 1-10.
  • Zhu HT, Dong QZ, Wang G, Zhou HJ, Ren N, Jia HL, Ye QH, Qin LX. Identification of suitable reference genes for qRT-PCR analysis of circulating microR-NAs in hepatitis B virus-infected patients. Mol Biotechnol 2012; 50: 49-56.
  • Zhu J, He F, Hu S, Yu J. On the nature of human housekeeping genes. Trends Genet 2008; 24(10): 481-4.
There are 45 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Ali Osman Turgut This is me 0000-0001-6863-0939

Özgecan Korkmaz Ağaoğlu This is me 0000-0002-7414-1725

Publication Date August 1, 2022
Submission Date May 31, 2021
Acceptance Date October 6, 2021
Published in Issue Year 2022 Volume: 19 Issue: 2

Cite

APA Turgut, A. O., & Korkmaz Ağaoğlu, Ö. (2022). MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları. Erciyes Üniversitesi Veteriner Fakültesi Dergisi, 19(2), 152-159. https://doi.org/10.32707/ercivet.1142293
AMA Turgut AO, Korkmaz Ağaoğlu Ö. MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları. Erciyes Üniv Vet Fak Derg. August 2022;19(2):152-159. doi:10.32707/ercivet.1142293
Chicago Turgut, Ali Osman, and Özgecan Korkmaz Ağaoğlu. “MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları”. Erciyes Üniversitesi Veteriner Fakültesi Dergisi 19, no. 2 (August 2022): 152-59. https://doi.org/10.32707/ercivet.1142293.
EndNote Turgut AO, Korkmaz Ağaoğlu Ö (August 1, 2022) MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları. Erciyes Üniversitesi Veteriner Fakültesi Dergisi 19 2 152–159.
IEEE A. O. Turgut and Ö. Korkmaz Ağaoğlu, “MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları”, Erciyes Üniv Vet Fak Derg, vol. 19, no. 2, pp. 152–159, 2022, doi: 10.32707/ercivet.1142293.
ISNAD Turgut, Ali Osman - Korkmaz Ağaoğlu, Özgecan. “MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları”. Erciyes Üniversitesi Veteriner Fakültesi Dergisi 19/2 (August 2022), 152-159. https://doi.org/10.32707/ercivet.1142293.
JAMA Turgut AO, Korkmaz Ağaoğlu Ö. MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları. Erciyes Üniv Vet Fak Derg. 2022;19:152–159.
MLA Turgut, Ali Osman and Özgecan Korkmaz Ağaoğlu. “MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları”. Erciyes Üniversitesi Veteriner Fakültesi Dergisi, vol. 19, no. 2, 2022, pp. 152-9, doi:10.32707/ercivet.1142293.
Vancouver Turgut AO, Korkmaz Ağaoğlu Ö. MikroRNA Ekspresyon Profillemesinde Yaygın Kullanılan Normalizasyon Yaklaşımları. Erciyes Üniv Vet Fak Derg. 2022;19(2):152-9.