Academic Journal
Comparative Analysis of Next-Generation Sequencing Data Processing Methods for Small RNA.
| Τίτλος: | Comparative Analysis of Next-Generation Sequencing Data Processing Methods for Small RNA. |
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| Συγγραφείς: | Zabegina, L. M.1 (AUTHOR) lidiazabegina@gmail.com, Aleksandrova, M. S.1 (AUTHOR), Garanin, A. Y.1 (AUTHOR), Shalaev, A. V.1 (AUTHOR), Lokteva, P. A.1 (AUTHOR), Nyuganen, A. O.1 (AUTHOR), Artemyeva, A. S.1 (AUTHOR), Apalko, S. V.2 (AUTHOR), Sherbak, S. G.2 (AUTHOR), Malek, A. V.1 (AUTHOR) lidiazabegina@gmail.com |
| Πηγή: | Russian Journal of Genetics. Oct2026, Vol. 62 Issue 10, p1325-1335. 11p. |
| Θεματικοί όροι: | *MicroRNA, *Thyroid gland tumors, *Non-coding RNA, *Salmon, *Nucleotide sequencing, *Stars |
| Περίληψη: | Next-generation sequencing is the most accurate method for discovering new genetic biomarkers. While working with small RNAs, processing and interpreting sequencing data is challenging. Using follicular thyroid neoplasms as an example, four methods for analyzing sequencing results were compared for 24 samples: follicular adenoma (n = 12) and follicular carcinoma (n = 12). Sequencing was performed on the DNBSEQ platform. Obtained data were analyzed by four approaches (MirMaster 2.0, Salmon, Star, and Python3), results were compared using a number of criteria and validated with RT-PCR for several representative molecules. For the analysis of small RNA (microRNA) sequencing data, the advantage of algorithms that align reads to reference sequences of mature microRNAs without mismatches and insertions was demonstrated. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
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