Journal of Advanced Research in Health Sciences

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DOI :10.26650/JARHS2020-737495   IUP :10.26650/JARHS2020-737495    Full Text (PDF)

Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model

Eda Sun, Müge Sayitoğlu

Objective: RNA Sequencing technology can offer gene expression differences and the reasons for these differences by detecting variations in the coding region, expession of noncoding RNAs and gene fusions. However, it is very difficult to analyze and interpret this technology, which can provide such valuable information. Although there is no reliable genetic marker for T-cell acute lymphoblastic leukemia (T-ALL), which can be used in the follow-up of the disease, the molecular infrastructure and classification that will be directly used in determining the treatment protocol and the new target proteins to be used in treatment are not known. Material and Methods: In this study, we aimed to establish the most suitable workflow algorithm for RNA sequencing in cell lines belonging to a group with a complex genomic background such as T-ALL. With this study, the Jurkat and Molt4 cell lines were sequenced by RNA sequencing. In order to increase the significance of our study, the results of different thymocyte subgroups and 12 T-ALL patient samples (GSE48173) were investigated. Results: We conducted a bioinformatics data approach by using open access data tools, and we successfully detected the tissue specific quantitative alternative splicing gene products, gene specific variations and global gene expression levels, and verified them using the same approach in T-ALL patient data. Conclusion: Aside from these molecular findings that we have achieved, one of our goals in this study was to develop an algorithm of transcriptomic data, which is difficult to work with and to interpret, and showed the correctness of our algorithm by confirming the data described in the literature.

Keywords: RNA-Sequencing, informatics, acute lymphoblastic leukemia
DOI :10.26650/JARHS2020-737495   IUP :10.26650/JARHS2020-737495    Full Text (PDF)

Lösemi Modelinde Tüm Genom RNA Dizileme Analiz Algoritması Geliştirilmesi

Eda Sun, Müge Sayitoğlu

Amaç: RNA Dizileme teknolojisi gen anlatım farklılıkları ve kodlayan bölgedeki varyasyonlar, kodlama yapmayan küçük RNAların anlatımları ve gen füzyonlarının belirlenmesi ile bu farklılıkların nedenlerini sunabilmektedir. Ancak bu kadar enformatik bilgiler sunabilen bu teknolojinin analizlerinin yapılması ve yorumlanması oldukça zorludur. T- hücreli akut lenfoblastik lösemi (T-ALL) de prognostik öneme sahip ve hastalığın takibinde kullanılabilecek güvenilir bir genetik belirteç bulunmamakla birlikte, doğrudan tedavi protokolünü ve tedavide yararlanılacak yeni hedef proteinleri belirlemede esas olacak moleküler alt yapı ve sınıflandırma da bilinmemektedir. Gereç ve Yöntem: Biz de bu çalışmamızda, T-ALL gibi karmaşık bir genomik arka plana sahip lösemi hücrelerinde RNA-dizileme için en uygun enformatik iş akış algoritmasını oluşturmayı amaçladık. Bu çalışmada RNA dizileme ile Jurkat ve Molt 4 hücre hatları dizilenmiştir. Doğrulama ve karşılaştırma amacıyla açık veri bankalarından elde edilen sağlıklı timosit alt grupları ve T-ALL hasta (n=12) örnekleri (GSE48173) kullanılmıştır. Bulgular: Açık erişimli veri araçları ile gerçekleştirdiğimiz enformatik analizlerde doku spesifik alternatif kırpılma ürünlerinin kantitatif tayinini, spesifik gen varyasyonlarını ve global gen anlatım düzeylerini başarılı bir şekilde tespit ettik ve T-ALL hasta verisinde aynı yaklaşımları kullanarak doğrulama yaptık. Sonuç: Çalışmamızın sonucunda lösemi hastalarının veri analizinde kullanılabilecek uygun araçlar ve algoritma belirlenmiştir.

Keywords: RNA-Dizileme, enformatik, akut lenfoblastik lösemi

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APA

Sun, E., & Sayitoğlu, M. (2020). Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model. Journal of Advanced Research in Health Sciences, 3(2), 26-34. https://doi.org/10.26650/JARHS2020-737495


AMA

Sun E, Sayitoğlu M. Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model. Journal of Advanced Research in Health Sciences. 2020;3(2):26-34. https://doi.org/10.26650/JARHS2020-737495


ABNT

Sun, E.; Sayitoğlu, M. Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model. Journal of Advanced Research in Health Sciences, [Publisher Location], v. 3, n. 2, p. 26-34, 2020.


Chicago: Author-Date Style

Sun, Eda, and Müge Sayitoğlu. 2020. “Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model.” Journal of Advanced Research in Health Sciences 3, no. 2: 26-34. https://doi.org/10.26650/JARHS2020-737495


Chicago: Humanities Style

Sun, Eda, and Müge Sayitoğlu. “Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model.” Journal of Advanced Research in Health Sciences 3, no. 2 (May. 2024): 26-34. https://doi.org/10.26650/JARHS2020-737495


Harvard: Australian Style

Sun, E & Sayitoğlu, M 2020, 'Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model', Journal of Advanced Research in Health Sciences, vol. 3, no. 2, pp. 26-34, viewed 8 May. 2024, https://doi.org/10.26650/JARHS2020-737495


Harvard: Author-Date Style

Sun, E. and Sayitoğlu, M. (2020) ‘Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model’, Journal of Advanced Research in Health Sciences, 3(2), pp. 26-34. https://doi.org/10.26650/JARHS2020-737495 (8 May. 2024).


MLA

Sun, Eda, and Müge Sayitoğlu. “Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model.” Journal of Advanced Research in Health Sciences, vol. 3, no. 2, 2020, pp. 26-34. [Database Container], https://doi.org/10.26650/JARHS2020-737495


Vancouver

Sun E, Sayitoğlu M. Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model. Journal of Advanced Research in Health Sciences [Internet]. 8 May. 2024 [cited 8 May. 2024];3(2):26-34. Available from: https://doi.org/10.26650/JARHS2020-737495 doi: 10.26650/JARHS2020-737495


ISNAD

Sun, Eda - Sayitoğlu, Müge. “Whole Genome RNA Sequencing Analysis Algorithm in Leukemia Model”. Journal of Advanced Research in Health Sciences 3/2 (May. 2024): 26-34. https://doi.org/10.26650/JARHS2020-737495



TIMELINE


Submitted14.05.2020
Accepted12.06.2020
Published Online10.07.2020

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