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Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias
BACKGROUND: Genetic variation for gene expression is a source of phenotypic variation for natural and agricultural species. The common approach to map and to quantify gene expression from genetically distinct individuals is to assign their RNA-seq reads to a single reference genome. However, RNA-seq...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056621/ https://www.ncbi.nlm.nih.gov/pubmed/33874908 http://dx.doi.org/10.1186/s12864-021-07577-3 |
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author | Zhan, Shuhua Griswold, Cortland Lukens, Lewis |
author_facet | Zhan, Shuhua Griswold, Cortland Lukens, Lewis |
author_sort | Zhan, Shuhua |
collection | PubMed |
description | BACKGROUND: Genetic variation for gene expression is a source of phenotypic variation for natural and agricultural species. The common approach to map and to quantify gene expression from genetically distinct individuals is to assign their RNA-seq reads to a single reference genome. However, RNA-seq reads from alleles dissimilar to this reference genome may fail to map correctly, causing transcript levels to be underestimated. Presently, the extent of this mapping problem is not clear, particularly in highly diverse species. We investigated if mapping bias occurred and if chromosomal features associated with mapping bias. Zea mays presents a model species to assess these questions, given it has genotypically distinct and well-studied genetic lines. RESULTS: In Zea mays, the inbred B73 genome is the standard reference genome and template for RNA-seq read assignments. In the absence of mapping bias, B73 and a second inbred line, Mo17, would each have an approximately equal number of regulatory alleles that increase gene expression. Remarkably, Mo17 had 2–4 times fewer such positively acting alleles than did B73 when RNA-seq reads were aligned to the B73 reference genome. Reciprocally, over one-half of the B73 alleles that increased gene expression were not detected when reads were aligned to the Mo17 genome template. Genes at dissimilar chromosomal ends were strongly affected by mapping bias, and genes at more similar pericentromeric regions were less affected. Biased transcript estimates were higher in untranslated regions and lower in splice junctions. Bias occurred across software and alignment parameters. CONCLUSIONS: Mapping bias very strongly affects gene transcript abundance estimates in maize, and bias varies across chromosomal features. Individual genome or transcriptome templates are likely necessary for accurate transcript estimation across genetically variable individuals in maize and other species. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07577-3. |
format | Online Article Text |
id | pubmed-8056621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80566212021-04-20 Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias Zhan, Shuhua Griswold, Cortland Lukens, Lewis BMC Genomics Research Article BACKGROUND: Genetic variation for gene expression is a source of phenotypic variation for natural and agricultural species. The common approach to map and to quantify gene expression from genetically distinct individuals is to assign their RNA-seq reads to a single reference genome. However, RNA-seq reads from alleles dissimilar to this reference genome may fail to map correctly, causing transcript levels to be underestimated. Presently, the extent of this mapping problem is not clear, particularly in highly diverse species. We investigated if mapping bias occurred and if chromosomal features associated with mapping bias. Zea mays presents a model species to assess these questions, given it has genotypically distinct and well-studied genetic lines. RESULTS: In Zea mays, the inbred B73 genome is the standard reference genome and template for RNA-seq read assignments. In the absence of mapping bias, B73 and a second inbred line, Mo17, would each have an approximately equal number of regulatory alleles that increase gene expression. Remarkably, Mo17 had 2–4 times fewer such positively acting alleles than did B73 when RNA-seq reads were aligned to the B73 reference genome. Reciprocally, over one-half of the B73 alleles that increased gene expression were not detected when reads were aligned to the Mo17 genome template. Genes at dissimilar chromosomal ends were strongly affected by mapping bias, and genes at more similar pericentromeric regions were less affected. Biased transcript estimates were higher in untranslated regions and lower in splice junctions. Bias occurred across software and alignment parameters. CONCLUSIONS: Mapping bias very strongly affects gene transcript abundance estimates in maize, and bias varies across chromosomal features. Individual genome or transcriptome templates are likely necessary for accurate transcript estimation across genetically variable individuals in maize and other species. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-07577-3. BioMed Central 2021-04-20 /pmc/articles/PMC8056621/ /pubmed/33874908 http://dx.doi.org/10.1186/s12864-021-07577-3 Text en © The Author(s) 2021 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 Article Zhan, Shuhua Griswold, Cortland Lukens, Lewis Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias |
title | Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias |
title_full | Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias |
title_fullStr | Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias |
title_full_unstemmed | Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias |
title_short | Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias |
title_sort | zea mays rna-seq estimated transcript abundances are strongly affected by read mapping bias |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056621/ https://www.ncbi.nlm.nih.gov/pubmed/33874908 http://dx.doi.org/10.1186/s12864-021-07577-3 |
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