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Evaluation of SNP in the CDH8 and CDH10 Genes Associated with Autism Using In-Silico Tools

Year 2024, Volume: 19 Issue: 1, 213 - 222, 28.03.2024
https://doi.org/10.55525/tjst.1344460

Abstract

Autism spectrum disorder (ASD) is defined as a pervasive and multifactorial neurodevelopmental disorder (ND). It is characterized by repetitive behavioral patterns as well as symptoms of social interaction and communication disorder. The cadherin (CDH) superfamily is a large group of synaptic cell adhesion molecules and has been widely related with ND, including autism. The aim of this study is to evaluate the potentially deleterious missense single nucleotide polymorphisms (SNPs) in CDH8 and CDH10 genes, which are related with ASD and cause amino acid changes, using internet-based software tools. To identify potentially harmful missense SNPs; all SNPs were screened using SIFT, PolyPhen-2, PROVEAN, SNPs&GO, Meta-SNP, and SNAP2 software tools, and common deleterious ones were filtered out. Its effect on protein stabilization was investigated with I-Mutant 3.0 and MUpro tools. Three-dimensional models of these common damaging amino acid changes were evaluated with the HOPE software. As a result of in silico analysis of 577 missense SNPs in the CDH8 gene; The rs145143780 (Y572C) polymorphism common damaging ‎SNP has been detected by all software tools.‎ According to the results of the in silico analysis of 526 missense SNPs found in the CDH10 gene; The rs13174039 (V459G), rs147882578 (N485K), rs201423740 (Y306C), rs201956238 (F317L) and rs373340564 (R128C) common damaging SNPs have been identified in all polymorphisms by all software tools. As a result of this study, it is thought that the data obtained will make important contributions to future relevant experimental studies.

References

  • Mostafavi M, Gaitanis J. Autism spectrum disorder and medical cannabis: review and clinical experience. Semin Pediatr Neurol. 2020; 35, 100833.
  • Ozdemir, O. The green tea polyphenol EGCG modulates NGF, BDNF, and CAMKII-α pathways to alleviate neurological damage in autism-induced rats. Acta Pol. Pharm. Drug Res. 2021; 77, 889-895.
  • Hirota T, King BH. Autism spectrum disorder: A review. Jama. 2023; 329(2), 157-168.
  • Persico AM, Napolioni V. Autism genetics. Behav. Brain Res. 2013; 251, 95–112.
  • Myers SM, Challman TD, Bernier R, Bourgeron T, Chung WK, Constantino JN, Eichler EE, Jacquemont S, Miller DT, Mitchell KJ. Insufficient evidence for “autism-specific” genes. The American Journal of Human Genetics. 2020; 106(5), 587–595.
  • Pagnamenta AT, Khan H, Walker S, Gerrelli D, Wing K, Bonaglia MC, Giorda R, Berney T, Mani E, Molteni M. Rare familial 16q21 microdeletions under a linkage peak implicate cadherin 8 (CDH8) in susceptibility to autism and learning disability. J. Med. Genet. 2011; 48(1), 48–54.
  • Lin YC, Frei JA, Kilander MBC, Shen W, Blatt GJ. A subset of autism-associated genes regulate the structural stability of neurons. Front. Cell. Neurosci. 2016; 10, 263.
  • Friedman LG, Riemslagh FW, Sullivan JM, Mesias R, Williams FM, Huntley GW, Benson DL. Cadherin‐8 expression, synaptic localization, and molecular control of neuronal form in prefrontal corticostriatal circuits. J. Comp. Neurol. 2015; 523(1), 75–92.
  • Takeichi M. The cadherin superfamily in neuronal connections and interactions. Nat. Rev. Neurosci. 2007; 8(1), 11-20.
  • Maître JL, Heisenberg CP. Three functions of cadherins in cell adhesion. Curr. Biol. 2013; 23(14), R626–R633.
  • Redies C, Hertel N, Hübner CA. Cadherins and neuropsychiatric disorders. Brain Res. 2012; 1470, 130-144.
  • Ray M, Mishra J, Priyadarshini A, Sahoo S. In silico identification of potential drug target and analysis of effective single nucleotide polymorphisms for autism spectrum disorder. Gene Reports. 2019; 16, 100420.
  • Avsar O. Investigation of Putative Functional SNPs of Human HAT1 Protein: A Comprehensive “in silico” Study. Cytol. Genet. 2022; 56(1), 98–107.
  • Kucukkal TG, Petukh M, Li L, Alexov E. Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins. Curr. Opin. Struct. Biol. 2015; 32, 18–24.
  • Owji H, Eslami M, Nezafat N, Ghasemi Y. In silico elucidation of deleterious non-synonymous SNPs in SHANK3, the autism spectrum disorder gene. J. Mol. Neurosci. 2020; 70, 1649–1667.
  • Tekcan A. In silico analysis of FMR1 gene missense SNPs. Cell Biochem. Biophys. 2016; 74, 109–127.
  • Bekisz S, Baudin L, Buntinx F, Noël A, Geris L. In vitro, in vivo, and in silico models of lymphangiogenesis in solid malignancies. Cancers. 2022; 14(6), 1525.
  • Hoda A, Lika M, Kolaneci V. Identification of deleterious nsSNPs in human HGF gene: in silico approach. J. Biomol. Struct. Dyn. 2023; 41(21), 11889–11903.
  • Yusuf M. Insights into the in-silico research: current scenario, advantages, limits, and future perspectives. Life in Silico. 2023; 1(1), 13–25.
  • Arpi MNT, Simpson TI. SFARI genes and where to find them; modelling Autism Spectrum Disorder specific gene expression dysregulation with RNA-seq data. Sci. Rep. 2022; 12(1), 10158.
  • Li M, He M, Xu F, Guan Y, Tian J, Wan Z, Zhou H, Gao M, Chong T. Abnormal expression and the significant prognostic value of aquaporins in clear cell renal cell carcinoma. PloS One. 2022; 17(3), e0264553.
  • Montojo J, Zuberi K, Rodriguez H, Kazi F, Wright G, Donaldson SL, Morris Q, Bader GD. GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. Bioinformatics. 2010; 26(22), 2927–2928.
  • Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003; 31(13), 3812–3814.
  • Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen‐2. Curr. Protoc. Hum. Genet. 2013; 76(1), 7-20.
  • Pshennikova VG, Barashkov NA, Romanov GP, Teryutin FM, Solov’ev AV, Gotovtsev NN, Nikanorova A A, Nakhodkin SS, Sazonov NN, Morozov IV. Comparison of predictive in silico tools on missense variants in GJB2, GJB6, and GJB3 genes associated with autosomal recessive deafness 1A (DFNB1A). The Scientific World Journal. 2019; 2019.
  • Choi Y, Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 2015; 31(16), 2745–2747.
  • Sandell L, Sharp NP. Fitness effects of mutations: An assessment of PROVEAN predictions using mutation accumulation data. Genome Biol. Evol. 2022; 14(1), evac004.
  • Pawlina-Tyszko K, Semik-Gurgul E, Gurgul A, Oczkowicz M, Szmatoła T, Bugno-Poniewierska M. Application of the targeted sequencing approach reveals the single nucleotide polymorphism (SNP) repertoire in microRNA genes in the pig genome. Sci. Rep. 2021; 11(1), 1–12.
  • Schwarz DF, Hädicke O, Erdmann J, Ziegler A, Bayer D, Möller S. SNPtoGO: characterizing SNPs by enriched GO terms. Bioinformatics. 2008; 24(1), 146–148.
  • Capriotti E, Altman RB, Bromberg Y. Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics. 2013;14(3), 1–9.
  • Petrosino M, Novak, L, Pasquo A, Chiaraluce R, Turina P, Capriotti E, Consalvi V. Analysis and interpretation of the impact of missense variants in cancer. Int. J. Mol. Sci. 2021; 22(11), 5416.
  • AbdulAzeez S, Borgio JF. In-silico computing of the most deleterious nsSNPs in HBA1 gene. PloS One. 2016; 11(1), e0147702.
  • Munshani S, Ibrahim, EY, Domenicano I, Ehrlich BE. The impact of mutations in wolframin on psychiatric disorders. Front. Pediatr. 2021; 9, 718132.
  • Desai M, Chauhan JB. Predicting the functional and structural consequences of nsSNPs in human methionine synthase gene using computational tools. Systems Biology in Reproductive Medicine. 2019; 65(4), 288–300.
  • Lim SW, Tan KJ, Azuraidi OM, Sathiya M, Lim EC, Lai KS, Yap WS, Afizan NARNM. Functional and structural analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the MYB oncoproteins associated with human cancer. Sci. Rep. 2021; 11(1), 1–14.
  • Tanwar H, Kumar DT, Doss C, Zayed H. Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA. Metab. Brain Dis. 2019; 34(6), 1577–1594.
  • Sadakierska-Chudy A, Szymanowski P, Lebioda A, Płoski R. Identification and In Silico Characterization of a Novel COLGALT2 Gene Variant in a Child with Mucosal Rectal Prolapse. Int. J. Mol. Sci. 2022; 23(7), 3670.
  • Venkata Subbiah H, Ramesh Babu P, Subbiah U. In silico analysis of non-synonymous single nucleotide polymorphisms of human DEFB1 gene. Egypt. J. Med. Hum. Genet. 2020; 21(1), 1–9.
  • Matthews BW, Nicholson H, Becktel WJ. Enhanced protein thermostability from site-directed mutations that decrease the entropy of unfolding. Proc. Natl. Acad. Sci. U.S.A. 1987; 84(19), 6663-6667.
  • Ragoonanan V, Aksan A. Protein stabilization. Transfus Med Hemother. 2007; 34(4), 246-252.
  • Ma DQ, Whitehead PL, Menold MM, Martin ER, Ashley-Koch AE, Mei H, Ritchie MD, Delong GR, Abramson RK, Wright HH. Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. The American Journal of Human Genetics. 2005; 77(3), 377–388.
  • Moore JH. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered. 2003; 56(1–3), 73–82.
  • Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010; 38(suppl_2), W214–W220.
  • Wang K, Zhang H, Ma D, Bucan M, Glessner JT, Abrahams BS, Salyakina D, Imielinski M, Bradfield JP, Sleiman PMA. Common genetic variants on 5p14. 1 associate with autism spectrum disorders. Nature. 2009; 459(7246), 528–533.
  • Frei JA, Niescier RF, Bridi MS, Durens M, Nestor JE, Kilander MBC, Yuan X, Dykxhoorn DM, Nestor M W, Huang S. Regulation of neural circuit development by cadherin-11 provides implications for autism. Eneuro. 2021; 8(4).

In-Silico Araçlar Kullanılarak Otizmle İlişkili CDH8 ve CDH10 Genlerindeki SNP'lerin Değerlendirilmesi

Year 2024, Volume: 19 Issue: 1, 213 - 222, 28.03.2024
https://doi.org/10.55525/tjst.1344460

Abstract

Otizm spektrum bozukluğu (OSB), yaygın ve çok faktörlü bir nörogelişimsel bozukluk (NB) olarak tanımlanır. Tekrarlayan davranış kalıplarının yanı sıra sosyal etkileşim ve iletişim bozukluğu belirtileri ile karakterizedir. Kadherin (CDH) süper ailesi, büyük bir sinaptik hücre adezyon molekülleri grubudur ve otizm de dahil olmak üzere NB ile geniş çapta ilişkilendirilmiştir. Bu çalışmanın amacı, otizm spektrum bozukluğu ile ilişkili ve amino asit değişikliklerine neden olan CDH8 ve CDH10 genlerinde potansiyel olarak zararlı olan missense tek nükleotid polimorfizmlerinin internet tabanlı yazılım araçları kullanılarak değerlendirilmesidir. Potansiyel olarak zararlı yanlış anlamlı SNP'leri tanımlamak için; tüm SNP'ler SIFT, PolyPhen-2, PROVEAN, SNPs&GO, Meta-SNP ve SNAP2 yazılım araçları kullanılarak tarandı ve ortak zararlı olanlar filtrelendi. Protein stabilizasyonu üzerindeki etkisi, I-Mutant 3.0 ve MUpro araçlarıyla araştırıldı. Bu ortak zararlı amino asit değişikliklerinin üç boyutlu modelleri HOPE yazılımı ile değerlendirildi. CDH8 genindeki 577 missense SNP'nin in silico analizi sonucunda; ‎SNP'ye zarar veren rs145143780 (Y572C) polimorfizmi tüm yazılım araçları tarafından tespit edilmiştir.‎ CDH10 geninde bulunan 526 missense SNP'nin in silico analiz sonuçlarına göre; rs13174039 (V459G), rs147882578 (N485K), rs201423740 (Y306C), rs201956238 (F317L) ve rs373340564 (R128C) ortak zarar veren SNP'ler, tüm polimorfizmlerde tüm yazılım araçları tarafından tanımlanmıştır. Bu çalışma sonucunda elde edilen verilerin gelecekte ilgili deneysel çalışmalara önemli katkılar sağlayacağı düşünülmektedir.

References

  • Mostafavi M, Gaitanis J. Autism spectrum disorder and medical cannabis: review and clinical experience. Semin Pediatr Neurol. 2020; 35, 100833.
  • Ozdemir, O. The green tea polyphenol EGCG modulates NGF, BDNF, and CAMKII-α pathways to alleviate neurological damage in autism-induced rats. Acta Pol. Pharm. Drug Res. 2021; 77, 889-895.
  • Hirota T, King BH. Autism spectrum disorder: A review. Jama. 2023; 329(2), 157-168.
  • Persico AM, Napolioni V. Autism genetics. Behav. Brain Res. 2013; 251, 95–112.
  • Myers SM, Challman TD, Bernier R, Bourgeron T, Chung WK, Constantino JN, Eichler EE, Jacquemont S, Miller DT, Mitchell KJ. Insufficient evidence for “autism-specific” genes. The American Journal of Human Genetics. 2020; 106(5), 587–595.
  • Pagnamenta AT, Khan H, Walker S, Gerrelli D, Wing K, Bonaglia MC, Giorda R, Berney T, Mani E, Molteni M. Rare familial 16q21 microdeletions under a linkage peak implicate cadherin 8 (CDH8) in susceptibility to autism and learning disability. J. Med. Genet. 2011; 48(1), 48–54.
  • Lin YC, Frei JA, Kilander MBC, Shen W, Blatt GJ. A subset of autism-associated genes regulate the structural stability of neurons. Front. Cell. Neurosci. 2016; 10, 263.
  • Friedman LG, Riemslagh FW, Sullivan JM, Mesias R, Williams FM, Huntley GW, Benson DL. Cadherin‐8 expression, synaptic localization, and molecular control of neuronal form in prefrontal corticostriatal circuits. J. Comp. Neurol. 2015; 523(1), 75–92.
  • Takeichi M. The cadherin superfamily in neuronal connections and interactions. Nat. Rev. Neurosci. 2007; 8(1), 11-20.
  • Maître JL, Heisenberg CP. Three functions of cadherins in cell adhesion. Curr. Biol. 2013; 23(14), R626–R633.
  • Redies C, Hertel N, Hübner CA. Cadherins and neuropsychiatric disorders. Brain Res. 2012; 1470, 130-144.
  • Ray M, Mishra J, Priyadarshini A, Sahoo S. In silico identification of potential drug target and analysis of effective single nucleotide polymorphisms for autism spectrum disorder. Gene Reports. 2019; 16, 100420.
  • Avsar O. Investigation of Putative Functional SNPs of Human HAT1 Protein: A Comprehensive “in silico” Study. Cytol. Genet. 2022; 56(1), 98–107.
  • Kucukkal TG, Petukh M, Li L, Alexov E. Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins. Curr. Opin. Struct. Biol. 2015; 32, 18–24.
  • Owji H, Eslami M, Nezafat N, Ghasemi Y. In silico elucidation of deleterious non-synonymous SNPs in SHANK3, the autism spectrum disorder gene. J. Mol. Neurosci. 2020; 70, 1649–1667.
  • Tekcan A. In silico analysis of FMR1 gene missense SNPs. Cell Biochem. Biophys. 2016; 74, 109–127.
  • Bekisz S, Baudin L, Buntinx F, Noël A, Geris L. In vitro, in vivo, and in silico models of lymphangiogenesis in solid malignancies. Cancers. 2022; 14(6), 1525.
  • Hoda A, Lika M, Kolaneci V. Identification of deleterious nsSNPs in human HGF gene: in silico approach. J. Biomol. Struct. Dyn. 2023; 41(21), 11889–11903.
  • Yusuf M. Insights into the in-silico research: current scenario, advantages, limits, and future perspectives. Life in Silico. 2023; 1(1), 13–25.
  • Arpi MNT, Simpson TI. SFARI genes and where to find them; modelling Autism Spectrum Disorder specific gene expression dysregulation with RNA-seq data. Sci. Rep. 2022; 12(1), 10158.
  • Li M, He M, Xu F, Guan Y, Tian J, Wan Z, Zhou H, Gao M, Chong T. Abnormal expression and the significant prognostic value of aquaporins in clear cell renal cell carcinoma. PloS One. 2022; 17(3), e0264553.
  • Montojo J, Zuberi K, Rodriguez H, Kazi F, Wright G, Donaldson SL, Morris Q, Bader GD. GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. Bioinformatics. 2010; 26(22), 2927–2928.
  • Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003; 31(13), 3812–3814.
  • Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen‐2. Curr. Protoc. Hum. Genet. 2013; 76(1), 7-20.
  • Pshennikova VG, Barashkov NA, Romanov GP, Teryutin FM, Solov’ev AV, Gotovtsev NN, Nikanorova A A, Nakhodkin SS, Sazonov NN, Morozov IV. Comparison of predictive in silico tools on missense variants in GJB2, GJB6, and GJB3 genes associated with autosomal recessive deafness 1A (DFNB1A). The Scientific World Journal. 2019; 2019.
  • Choi Y, Chan AP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 2015; 31(16), 2745–2747.
  • Sandell L, Sharp NP. Fitness effects of mutations: An assessment of PROVEAN predictions using mutation accumulation data. Genome Biol. Evol. 2022; 14(1), evac004.
  • Pawlina-Tyszko K, Semik-Gurgul E, Gurgul A, Oczkowicz M, Szmatoła T, Bugno-Poniewierska M. Application of the targeted sequencing approach reveals the single nucleotide polymorphism (SNP) repertoire in microRNA genes in the pig genome. Sci. Rep. 2021; 11(1), 1–12.
  • Schwarz DF, Hädicke O, Erdmann J, Ziegler A, Bayer D, Möller S. SNPtoGO: characterizing SNPs by enriched GO terms. Bioinformatics. 2008; 24(1), 146–148.
  • Capriotti E, Altman RB, Bromberg Y. Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics. 2013;14(3), 1–9.
  • Petrosino M, Novak, L, Pasquo A, Chiaraluce R, Turina P, Capriotti E, Consalvi V. Analysis and interpretation of the impact of missense variants in cancer. Int. J. Mol. Sci. 2021; 22(11), 5416.
  • AbdulAzeez S, Borgio JF. In-silico computing of the most deleterious nsSNPs in HBA1 gene. PloS One. 2016; 11(1), e0147702.
  • Munshani S, Ibrahim, EY, Domenicano I, Ehrlich BE. The impact of mutations in wolframin on psychiatric disorders. Front. Pediatr. 2021; 9, 718132.
  • Desai M, Chauhan JB. Predicting the functional and structural consequences of nsSNPs in human methionine synthase gene using computational tools. Systems Biology in Reproductive Medicine. 2019; 65(4), 288–300.
  • Lim SW, Tan KJ, Azuraidi OM, Sathiya M, Lim EC, Lai KS, Yap WS, Afizan NARNM. Functional and structural analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the MYB oncoproteins associated with human cancer. Sci. Rep. 2021; 11(1), 1–14.
  • Tanwar H, Kumar DT, Doss C, Zayed H. Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA. Metab. Brain Dis. 2019; 34(6), 1577–1594.
  • Sadakierska-Chudy A, Szymanowski P, Lebioda A, Płoski R. Identification and In Silico Characterization of a Novel COLGALT2 Gene Variant in a Child with Mucosal Rectal Prolapse. Int. J. Mol. Sci. 2022; 23(7), 3670.
  • Venkata Subbiah H, Ramesh Babu P, Subbiah U. In silico analysis of non-synonymous single nucleotide polymorphisms of human DEFB1 gene. Egypt. J. Med. Hum. Genet. 2020; 21(1), 1–9.
  • Matthews BW, Nicholson H, Becktel WJ. Enhanced protein thermostability from site-directed mutations that decrease the entropy of unfolding. Proc. Natl. Acad. Sci. U.S.A. 1987; 84(19), 6663-6667.
  • Ragoonanan V, Aksan A. Protein stabilization. Transfus Med Hemother. 2007; 34(4), 246-252.
  • Ma DQ, Whitehead PL, Menold MM, Martin ER, Ashley-Koch AE, Mei H, Ritchie MD, Delong GR, Abramson RK, Wright HH. Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. The American Journal of Human Genetics. 2005; 77(3), 377–388.
  • Moore JH. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered. 2003; 56(1–3), 73–82.
  • Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010; 38(suppl_2), W214–W220.
  • Wang K, Zhang H, Ma D, Bucan M, Glessner JT, Abrahams BS, Salyakina D, Imielinski M, Bradfield JP, Sleiman PMA. Common genetic variants on 5p14. 1 associate with autism spectrum disorders. Nature. 2009; 459(7246), 528–533.
  • Frei JA, Niescier RF, Bridi MS, Durens M, Nestor JE, Kilander MBC, Yuan X, Dykxhoorn DM, Nestor M W, Huang S. Regulation of neural circuit development by cadherin-11 provides implications for autism. Eneuro. 2021; 8(4).
There are 45 citations in total.

Details

Primary Language English
Subjects Bioinformatics and Computational Biology (Other), Biochemistry and Cell Biology (Other), Genetics (Other), Animal Cell and Molecular Biology
Journal Section TJST
Authors

Azadeh Rezaeirad 0000-0002-9349-1149

Ömer Faruk Karasakal 0000-0001-7803-3249

Tuğba Kaman 0000-0002-5885-0193

Mesut Karahan 0000-0002-8971-678X

Publication Date March 28, 2024
Submission Date August 17, 2023
Published in Issue Year 2024 Volume: 19 Issue: 1

Cite

APA Rezaeirad, A., Karasakal, Ö. F., Kaman, T., Karahan, M. (2024). Evaluation of SNP in the CDH8 and CDH10 Genes Associated with Autism Using In-Silico Tools. Turkish Journal of Science and Technology, 19(1), 213-222. https://doi.org/10.55525/tjst.1344460
AMA Rezaeirad A, Karasakal ÖF, Kaman T, Karahan M. Evaluation of SNP in the CDH8 and CDH10 Genes Associated with Autism Using In-Silico Tools. TJST. March 2024;19(1):213-222. doi:10.55525/tjst.1344460
Chicago Rezaeirad, Azadeh, Ömer Faruk Karasakal, Tuğba Kaman, and Mesut Karahan. “Evaluation of SNP in the CDH8 and CDH10 Genes Associated With Autism Using In-Silico Tools”. Turkish Journal of Science and Technology 19, no. 1 (March 2024): 213-22. https://doi.org/10.55525/tjst.1344460.
EndNote Rezaeirad A, Karasakal ÖF, Kaman T, Karahan M (March 1, 2024) Evaluation of SNP in the CDH8 and CDH10 Genes Associated with Autism Using In-Silico Tools. Turkish Journal of Science and Technology 19 1 213–222.
IEEE A. Rezaeirad, Ö. F. Karasakal, T. Kaman, and M. Karahan, “Evaluation of SNP in the CDH8 and CDH10 Genes Associated with Autism Using In-Silico Tools”, TJST, vol. 19, no. 1, pp. 213–222, 2024, doi: 10.55525/tjst.1344460.
ISNAD Rezaeirad, Azadeh et al. “Evaluation of SNP in the CDH8 and CDH10 Genes Associated With Autism Using In-Silico Tools”. Turkish Journal of Science and Technology 19/1 (March 2024), 213-222. https://doi.org/10.55525/tjst.1344460.
JAMA Rezaeirad A, Karasakal ÖF, Kaman T, Karahan M. Evaluation of SNP in the CDH8 and CDH10 Genes Associated with Autism Using In-Silico Tools. TJST. 2024;19:213–222.
MLA Rezaeirad, Azadeh et al. “Evaluation of SNP in the CDH8 and CDH10 Genes Associated With Autism Using In-Silico Tools”. Turkish Journal of Science and Technology, vol. 19, no. 1, 2024, pp. 213-22, doi:10.55525/tjst.1344460.
Vancouver Rezaeirad A, Karasakal ÖF, Kaman T, Karahan M. Evaluation of SNP in the CDH8 and CDH10 Genes Associated with Autism Using In-Silico Tools. TJST. 2024;19(1):213-22.