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Impact of gene annotation choice on the quantification of RNA-seq data

BACKGROUND: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include c...

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Autores principales: Chisanga, David, Liao, Yang, Shi, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8969366/
https://www.ncbi.nlm.nih.gov/pubmed/35354358
http://dx.doi.org/10.1186/s12859-022-04644-8
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author Chisanga, David
Liao, Yang
Shi, Wei
author_facet Chisanga, David
Liao, Yang
Shi, Wei
author_sort Chisanga, David
collection PubMed
description BACKGROUND: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. RESULTS: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods. CONCLUSION: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04644-8.
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spelling pubmed-89693662022-04-01 Impact of gene annotation choice on the quantification of RNA-seq data Chisanga, David Liao, Yang Shi, Wei BMC Bioinformatics Research BACKGROUND: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. RESULTS: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods. CONCLUSION: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04644-8. BioMed Central 2022-03-30 /pmc/articles/PMC8969366/ /pubmed/35354358 http://dx.doi.org/10.1186/s12859-022-04644-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chisanga, David
Liao, Yang
Shi, Wei
Impact of gene annotation choice on the quantification of RNA-seq data
title Impact of gene annotation choice on the quantification of RNA-seq data
title_full Impact of gene annotation choice on the quantification of RNA-seq data
title_fullStr Impact of gene annotation choice on the quantification of RNA-seq data
title_full_unstemmed Impact of gene annotation choice on the quantification of RNA-seq data
title_short Impact of gene annotation choice on the quantification of RNA-seq data
title_sort impact of gene annotation choice on the quantification of rna-seq data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8969366/
https://www.ncbi.nlm.nih.gov/pubmed/35354358
http://dx.doi.org/10.1186/s12859-022-04644-8
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