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Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets

Studying how different plant groups deal with heavy metal exposure is crucial to improve our understanding of the diversity of molecular mechanisms involved in plant stress response. Here, we used RNA sequencing (RNA-seq) and epigenotyping by sequencing (epiGBS) to assess gene expression and DNA met...

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Autores principales: Boquete, M. Teresa, Schmid, Marc W., Wagemaker, Niels C.A.M., Carey, Sarah B., McDaniel, Stuart F., Richards, Christina L., Alonso, Conchita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679722/
https://www.ncbi.nlm.nih.gov/pubmed/36426070
http://dx.doi.org/10.1016/j.dib.2022.108710
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author Boquete, M. Teresa
Schmid, Marc W.
Wagemaker, Niels C.A.M.
Carey, Sarah B.
McDaniel, Stuart F.
Richards, Christina L.
Alonso, Conchita
author_facet Boquete, M. Teresa
Schmid, Marc W.
Wagemaker, Niels C.A.M.
Carey, Sarah B.
McDaniel, Stuart F.
Richards, Christina L.
Alonso, Conchita
author_sort Boquete, M. Teresa
collection PubMed
description Studying how different plant groups deal with heavy metal exposure is crucial to improve our understanding of the diversity of molecular mechanisms involved in plant stress response. Here, we used RNA sequencing (RNA-seq) and epigenotyping by sequencing (epiGBS) to assess gene expression and DNA methylation changes respectively in plants from four populations of the metallophyte moss Scopelophila cataractae treated with Cd or Cu in the laboratory. We built RNA-seq and epiGBS sequencing libraries from control and treated samples from each population and sequenced them using Illumina HiSeq 3000 (PE-150 bp) and Illumina HiSeq X-Ten System (PE-150 bp) respectively. For the RNA-seq data, we performed a read quality filter, mapped the reads to the de novo transcriptome created with Trinity, and estimated transcript abundance for each sample. For the epiGBS data, we used a custom pipeline (https://doi.org/10.5281/zenodo.7040291) to map the reads to a de novo reference genome and performed strand-specific nucleotide (single nucleotide polymorphisms, SNPs) and methylation (single cytosine methylation polymorphisms, SMPs) variant calling. We filtered out SNPs and SMPs with low coverage within (positions with <10 sequencing reads per sample) and across samples (positions with poor representation on the full set of samples). Finally, we performed pairwise comparisons between control and treated samples from each population and identified differentially expressed genes and differentially methylated cytosines associated to heavy metal exposure. We payed particular attention to the different responses of the more and the less tolerant populations of S. cataractae. These datasets could contribute to future comparative studies of abiotic stress response across plant groups.
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spelling pubmed-96797222022-11-23 Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets Boquete, M. Teresa Schmid, Marc W. Wagemaker, Niels C.A.M. Carey, Sarah B. McDaniel, Stuart F. Richards, Christina L. Alonso, Conchita Data Brief Data Article Studying how different plant groups deal with heavy metal exposure is crucial to improve our understanding of the diversity of molecular mechanisms involved in plant stress response. Here, we used RNA sequencing (RNA-seq) and epigenotyping by sequencing (epiGBS) to assess gene expression and DNA methylation changes respectively in plants from four populations of the metallophyte moss Scopelophila cataractae treated with Cd or Cu in the laboratory. We built RNA-seq and epiGBS sequencing libraries from control and treated samples from each population and sequenced them using Illumina HiSeq 3000 (PE-150 bp) and Illumina HiSeq X-Ten System (PE-150 bp) respectively. For the RNA-seq data, we performed a read quality filter, mapped the reads to the de novo transcriptome created with Trinity, and estimated transcript abundance for each sample. For the epiGBS data, we used a custom pipeline (https://doi.org/10.5281/zenodo.7040291) to map the reads to a de novo reference genome and performed strand-specific nucleotide (single nucleotide polymorphisms, SNPs) and methylation (single cytosine methylation polymorphisms, SMPs) variant calling. We filtered out SNPs and SMPs with low coverage within (positions with <10 sequencing reads per sample) and across samples (positions with poor representation on the full set of samples). Finally, we performed pairwise comparisons between control and treated samples from each population and identified differentially expressed genes and differentially methylated cytosines associated to heavy metal exposure. We payed particular attention to the different responses of the more and the less tolerant populations of S. cataractae. These datasets could contribute to future comparative studies of abiotic stress response across plant groups. Elsevier 2022-10-28 /pmc/articles/PMC9679722/ /pubmed/36426070 http://dx.doi.org/10.1016/j.dib.2022.108710 Text en © 2022 Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Boquete, M. Teresa
Schmid, Marc W.
Wagemaker, Niels C.A.M.
Carey, Sarah B.
McDaniel, Stuart F.
Richards, Christina L.
Alonso, Conchita
Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets
title Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets
title_full Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets
title_fullStr Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets
title_full_unstemmed Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets
title_short Heavy metal tolerance in Scopelophila cataractae: Transcriptomic and epigenetic datasets
title_sort heavy metal tolerance in scopelophila cataractae: transcriptomic and epigenetic datasets
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679722/
https://www.ncbi.nlm.nih.gov/pubmed/36426070
http://dx.doi.org/10.1016/j.dib.2022.108710
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