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Depth normalization of small RNA sequencing: using data and biology to select a suitable method

Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for ‘normalizing’ sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which n...

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Detalles Bibliográficos
Autores principales: Düren, Yannick, Lederer, Johannes, Qin, Li-Xuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9177987/
https://www.ncbi.nlm.nih.gov/pubmed/35188574
http://dx.doi.org/10.1093/nar/gkac064
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author Düren, Yannick
Lederer, Johannes
Qin, Li-Xuan
author_facet Düren, Yannick
Lederer, Johannes
Qin, Li-Xuan
author_sort Düren, Yannick
collection PubMed
description Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for ‘normalizing’ sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which normalization is the most suitable for a given data set. To address this problem, we developed ‘DANA’—an approach for assessing the performance of normalization methods for microRNA sequencing data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biological features of microRNAs for their expression pattern and chromosomal clustering to simultaneously assess (i) how effectively normalization removes handling artifacts and (ii) how aptly normalization preserves biological signals. With DANA, we confirm that the performance of eight commonly used normalization methods vary widely across different data sets and provide guidance for selecting a suitable method for the data at hand. Hence, it should be adopted as a routine preprocessing step (preceding normalization) for microRNA sequencing data analysis. DANA is implemented in R and publicly available at https://github.com/LXQin/DANA.
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spelling pubmed-91779872022-06-09 Depth normalization of small RNA sequencing: using data and biology to select a suitable method Düren, Yannick Lederer, Johannes Qin, Li-Xuan Nucleic Acids Res Methods Online Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for ‘normalizing’ sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which normalization is the most suitable for a given data set. To address this problem, we developed ‘DANA’—an approach for assessing the performance of normalization methods for microRNA sequencing data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biological features of microRNAs for their expression pattern and chromosomal clustering to simultaneously assess (i) how effectively normalization removes handling artifacts and (ii) how aptly normalization preserves biological signals. With DANA, we confirm that the performance of eight commonly used normalization methods vary widely across different data sets and provide guidance for selecting a suitable method for the data at hand. Hence, it should be adopted as a routine preprocessing step (preceding normalization) for microRNA sequencing data analysis. DANA is implemented in R and publicly available at https://github.com/LXQin/DANA. Oxford University Press 2022-02-21 /pmc/articles/PMC9177987/ /pubmed/35188574 http://dx.doi.org/10.1093/nar/gkac064 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Online
Düren, Yannick
Lederer, Johannes
Qin, Li-Xuan
Depth normalization of small RNA sequencing: using data and biology to select a suitable method
title Depth normalization of small RNA sequencing: using data and biology to select a suitable method
title_full Depth normalization of small RNA sequencing: using data and biology to select a suitable method
title_fullStr Depth normalization of small RNA sequencing: using data and biology to select a suitable method
title_full_unstemmed Depth normalization of small RNA sequencing: using data and biology to select a suitable method
title_short Depth normalization of small RNA sequencing: using data and biology to select a suitable method
title_sort depth normalization of small rna sequencing: using data and biology to select a suitable method
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9177987/
https://www.ncbi.nlm.nih.gov/pubmed/35188574
http://dx.doi.org/10.1093/nar/gkac064
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