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Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method

MicroRNAs are vital gene expression regulators, extensively studied worldwide. The large-scale characterization of miRNAomes is possible using next-generation sequencing (NGS). This technology offers great opportunities, but these cannot be fully exploited without proper and comprehensive bioinforma...

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Autores principales: Pawlina-Tyszko, Klaudia, Szmatoła, Tomasz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687144/
https://www.ncbi.nlm.nih.gov/pubmed/38030823
http://dx.doi.org/10.1007/s10142-023-01276-w
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author Pawlina-Tyszko, Klaudia
Szmatoła, Tomasz
author_facet Pawlina-Tyszko, Klaudia
Szmatoła, Tomasz
author_sort Pawlina-Tyszko, Klaudia
collection PubMed
description MicroRNAs are vital gene expression regulators, extensively studied worldwide. The large-scale characterization of miRNAomes is possible using next-generation sequencing (NGS). This technology offers great opportunities, but these cannot be fully exploited without proper and comprehensive bioinformatics analysis. This may be achieved by the use of reliable dedicated software; however, different programs may generate divergent results, leading to additional discrepancies. Thus, the aim of this study was to compare three bioinformatic algorithms dedicated to NGS-based microRNA profiling and validate them using an alternative method, namely RT-qPCR. The comparison analysis revealed differences in the number and sets of identified miRNAs. The qPCR confirmed the expression of the investigated microRNAs. The correlation analysis of NGS and qPCR measurements showed strong and significant coefficients for a subset of the tested miRNAs, including those detected by all three algorithms. Single miRNA variants (isomiRs) showed different levels of correlation with the qPCR data. The obtained results revealed the good performance of all tested programs, despite the observed differences. Moreover, they implied that some specific miRNAs may be differentially estimated using NGS technology and the qPCR method, regardless of the used bioinformatics software. These discrepancies may stem from many factors, including the composition of the isomiR profile, their abundance, length, and investigated species. In conclusion, in this study, we shed light on the bioinformatics aspects of miRNAome profiling, elucidating its complexity and pinpointing potential features influencing validation. Thus, qPCR validation results should be open to interpretation when not fully concordant with NGS results until further, additional analyses are conducted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10142-023-01276-w.
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spelling pubmed-106871442023-12-01 Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method Pawlina-Tyszko, Klaudia Szmatoła, Tomasz Funct Integr Genomics Original Article MicroRNAs are vital gene expression regulators, extensively studied worldwide. The large-scale characterization of miRNAomes is possible using next-generation sequencing (NGS). This technology offers great opportunities, but these cannot be fully exploited without proper and comprehensive bioinformatics analysis. This may be achieved by the use of reliable dedicated software; however, different programs may generate divergent results, leading to additional discrepancies. Thus, the aim of this study was to compare three bioinformatic algorithms dedicated to NGS-based microRNA profiling and validate them using an alternative method, namely RT-qPCR. The comparison analysis revealed differences in the number and sets of identified miRNAs. The qPCR confirmed the expression of the investigated microRNAs. The correlation analysis of NGS and qPCR measurements showed strong and significant coefficients for a subset of the tested miRNAs, including those detected by all three algorithms. Single miRNA variants (isomiRs) showed different levels of correlation with the qPCR data. The obtained results revealed the good performance of all tested programs, despite the observed differences. Moreover, they implied that some specific miRNAs may be differentially estimated using NGS technology and the qPCR method, regardless of the used bioinformatics software. These discrepancies may stem from many factors, including the composition of the isomiR profile, their abundance, length, and investigated species. In conclusion, in this study, we shed light on the bioinformatics aspects of miRNAome profiling, elucidating its complexity and pinpointing potential features influencing validation. Thus, qPCR validation results should be open to interpretation when not fully concordant with NGS results until further, additional analyses are conducted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10142-023-01276-w. Springer Berlin Heidelberg 2023-11-30 2023 /pmc/articles/PMC10687144/ /pubmed/38030823 http://dx.doi.org/10.1007/s10142-023-01276-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Pawlina-Tyszko, Klaudia
Szmatoła, Tomasz
Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method
title Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method
title_full Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method
title_fullStr Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method
title_full_unstemmed Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method
title_short Benchmarking of bioinformatics tools for NGS-based microRNA profiling with RT-qPCR method
title_sort benchmarking of bioinformatics tools for ngs-based microrna profiling with rt-qpcr method
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687144/
https://www.ncbi.nlm.nih.gov/pubmed/38030823
http://dx.doi.org/10.1007/s10142-023-01276-w
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