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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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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. |
format | Online Article Text |
id | pubmed-9177987 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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