English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

Lösemi Modelinde Tüm Genom RNA Dizileme Analiz Algoritması Geliştirilmesi