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Comparative Analysis of Next-Generation Sequencing Data Processing Methods for Small RNA.

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Τίτλος: Comparative Analysis of Next-Generation Sequencing Data Processing Methods for Small RNA.
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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  – Url: https://dx.doi.org/doi:10.1134/S1022795426700766
    Name: EDS - Springer Nature Journals (s7799221)
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An: 197587362
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PreciseRelevancyScore: 1452.43029785156
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  Data: Comparative Analysis of Next-Generation Sequencing Data Processing Methods for Small RNA.
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  Data: <searchLink fieldCode="AR" term="%22Zabegina%2C+L%2E+M%2E%22">Zabegina, L. M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lidiazabegina@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Aleksandrova%2C+M%2E+S%2E%22">Aleksandrova, M. S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Garanin%2C+A%2E+Y%2E%22">Garanin, A. Y.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shalaev%2C+A%2E+V%2E%22">Shalaev, A. V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lokteva%2C+P%2E+A%2E%22">Lokteva, P. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nyuganen%2C+A%2E+O%2E%22">Nyuganen, A. O.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Artemyeva%2C+A%2E+S%2E%22">Artemyeva, A. S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Apalko%2C+S%2E+V%2E%22">Apalko, S. V.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sherbak%2C+S%2E+G%2E%22">Sherbak, S. G.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Malek%2C+A%2E+V%2E%22">Malek, A. V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lidiazabegina@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Russian+Journal+of+Genetics%22">Russian Journal of Genetics</searchLink>. Oct2026, Vol. 62 Issue 10, p1325-1335. 11p.
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  Data: *<searchLink fieldCode="DE" term="%22MicroRNA%22">MicroRNA</searchLink><br />*<searchLink fieldCode="DE" term="%22Thyroid+gland+tumors%22">Thyroid gland tumors</searchLink><br />*<searchLink fieldCode="DE" term="%22Non-coding+RNA%22">Non-coding RNA</searchLink><br />*<searchLink fieldCode="DE" term="%22Salmon%22">Salmon</searchLink><br />*<searchLink fieldCode="DE" term="%22Nucleotide+sequencing%22">Nucleotide sequencing</searchLink><br />*<searchLink fieldCode="DE" term="%22Stars%22">Stars</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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]
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        Text: English
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        Type: general
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              Text: Oct2026
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              Y: 2026
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