DOI: 10.5061/dryad.mw6m905zj
title: Data from: Experimental demonstration and pan-structurome prediction of climate-associated riboSNitches in Arabidopsis

description: BackgroundGenome-Wide Association Studies (GWAS) aim to correlate
 phenotypic changes with genotypic variation. Single nucleotide variants
 (SNVs) within transcripts may alter mRNA structure, with potential impacts
 on transcript stability, macromolecular interactions and translation.
 However, no plant genomes have been yet assessed for the presence of these
 structure-altering polymorphisms or “riboSNitches”.ResultsWe
 experimentally demonstrate the presence of riboSNitches in transcripts of
 two Arabidopsis genes, ZINC RIBBON 3 (ZR3) and COTTON GOLGI-RELATED 3
 (CGR3), which are associated with continentality and temperature variation
 in the natural environment. These riboSNitches are associated with
 differences in the abundance of their respective transcripts, implying
 their role in regulating gene expression in adaptation to local climate
 conditions. We computationally predict transcriptome-wide riboSNitches in
 879 naturally inbred Arabidopsis accessions. We also characterize
 correlations between SNPs/riboSNitches in these accessions and 434 climate
 descriptors of local environments; suggesting the role of these variants
 in local adaptation. We integrate this information in CLIMtools V2.0 and
 provide a new web resource, T-CLIM, which allows users to determine the
 association of transcript abundance variation with climate
 variation.ConclusionsWe functionally validate two plant riboSNitches and,
 for the first time, demonstrate riboSNitch is conditionally dependent on
 temperature, coining the term conditional riboSNitch. We provide the first
 pan-genome wide prediction of riboSNitches in plants. We expand our
 previous CLIMtools web resource with riboSNitch information and with 1868
 additional Arabidopsis genomes and 269 additional climate conditions,
 which will facilitate in silico studies of natural genetic variation, its
 phenotypic consequences and its role in local adaptation.
description: Compiled geo-climatic variables were obtained through curation of
 databases. We extracted information on 473 climate variables (see
 description of environmental parameters) for a comprehensive description
 of the local environment of previously sequenced Arabidopsis accessions
 [1–7].<br><br>For the calculations of genotype × environmental
 associations (GEA), we used a GWAS approach with each of the numerical
 environmental parameters included in this study.The online tool GWAPP
 (http://gwas.gmi.oeaw.ac.at/) was employed using an accelerated mixed
 model (AMM) [8], which addresses the confounding effects of population
 stratification, family structure, and cryptic relatedness [9]. This method
 presents at the same time an issue with the introduction of false
 negatives. We also include here the results derived from GEA using a
 linear regression model that does not correct for population structure,
 also obtained using GWAPP [8].<br><br>Transcriptome-wide
 association (TWA) analysis of correlations between transcript abundance
 and each of the environmental variables included in this study using the
 set of 558 accessions within the set of 879 Eurasian accessions with
 available information on transcript abundance from a previous study [10]
 (GEO dataset with accession number GSE80744 and SRA study SRP074107).
 Spearman’s rank correlation coefficients between individual climate
 variables and individual transcript abundance values were calculated using
 the correlation function of the Hmisc package in R.<br><br>The
 SNPfold program [11] was used to identify predicted riboSNitches
 Genome-wide. SNPfold was applied to the 3,830,264 natural variants of
 protein-coding genes (including introns) within the 879 Eurasian
 Arabidopsis in our
 study.<br><br><em>References</em>
 1. Alonso-Blanco C, Andrade J, Becker C, Bemm F, Bergelson J,
 Borgwardt KM, et al. 1,135 genomes reveal the global pattern of
 polymorphism in Arabidopsis thaliana. Cell. 2016;166:481–91.
 2. Durvasula A, Fulgione A, Gutaker RM, Alacakaptan SI, Flood PJ,
 Neto C, et al. African genomes illuminate the early history and transition
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 Plant Sci. 2018;9:967. 4. Fulgione A, Koornneef M, Roux
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description: This Dataset includes the following files: 1.
 "EURASIAN_ACCESSIONS.TAIR10_52_RiboSNitch_prediction.csv": 
 Genome-wide riboSNitch prediction. Output from the SNPfold program to
 identify predicted riboSNitches Genome-wide. SNPfold was applied to the
 3,830,264 natural variants of protein-coding genes (including introns)
 within the 879 Eurasian Arabidopsis in our study. Column labels in this
 file represent:<br>        -chr: chromosome.<br>        -pos:
 position.<br>        -REF: reference allele.<br>        -ALT:
 alternative allele.<br>        -INFO: SNP effect
 prediction.<br>        -SYMBOL: symbols for the gene where the SNP
 is located.<br>        -LOCUS_ID: for the gene where the SNP is
 located.<br>        -TRANSCRIPT: transcript ID for the gene where
 the SNP is located.<br>        -ANNOTATION_CHECK: column confirming
 that the input file came from the .gff file for the Arabidopsis TAIR10_52
 annotation
 (http://ftp.ensemblgenomes.org/pub/plants/release-52/gff3/arabidopsis_thaliana/). All values in this column should read "gff."<br>        -LOCATION: column confirming that the input file came from the gene body. All values in this column should read "body."<br>        -ADDED: Nucleotide window used for the prediction on each SNP. All values should read "40."<br>        -STEP: Used to describe the difference in length of the left and right flanks. All values should read “0”.<br>        -CC_BPRPOB: correlation coefficients for base-pairing probability.<br>        -CC_SHANNON: Shannon entropy change for all possible single mutations in the RNA. 2. "Environmental_data.csv": environmental data for the accessions included in this study. The column "accession_id" includes the identification of each accession. The column names identify individual environmental variables. In this dataset, we provide another file "Description_of_environmental_variables.xlsx" that can be used to identify any of these descriptive names in the column named as "ID" and obtain more descriptive information on each environmental variable. <br>    <br>3. "CLIMATE_TWAS.csv": Spearman’s rank correlation coefficients between individual climate variables (column names) and individual transcript abundance value (row names) genome-wide. The first column, named "LOCUS_ID" provides the locus ID, while column names provides a descriptive name for individual environmental variables. In this dataset, we provide another file "Description_of_environmental_variables.xlsx" that can be used to identify any of these descriptive names in the column named as "ID" and obtain more descriptive information on each environmental variable. <br>  <br>4. "Environmental_GWAS_2021_LM.zip": We include here the full pre-annotated and pre-filtered results derived from Genome–environment associations using a linear regression model that does not correct for population structure that we obtained using GWAPP [8]. The name of the files coincides with the "ID" provided in the "Description_of_environmental_variables.xlsx" file, where the user can obtain more detailed information on any particular environmental variable. The list of files included in the compressed folder is provided in the file "File_names_Environmental_GWAS_2021_LM.csv"   For each of the files in this compressed folder, the   <br>        -chr: chromosome.<br>        -pos: position.<br>        -score: association strength between any given environmental variable and individual SNPs (negative logarithm of the P-value).<br>        -maf: minimum allele frequency for individual SNPs (frequency).<br>        -mac: minimum allele count (count).<br>        -GVE: Genetic variance explained. 5. "Environmental_GWAS_2021_AMM.zip": We include here the full pre-annotated and pre-filtered results derived from Genome–environment associations using a mixed model that corrects for population structure that we obtained using GWAPP [8]. The name of the files coincides with the "ID" provided in the "Description_of_environmental_variables.xlsx" file, where the user can obtain more detailed information on any particular environmental variable. The list of files included in the compressed folder is provided in the file "File_names_Environmental_GWAS_2021_AMM.csv"     <br>        -chr: chromosome.<br>        -pos: position.<br>        -score: association strength between any given environmental variable and individual SNPs (negative logarithm of the P-value).<br>        -maf: minimum allele frequency for individual SNPs( frequency).<br>        -mac: minimum allele count (count).<br>        -GVE: Genetic variance explained. 6. "Description_of_environmental_variables.xlsx": This file contains the description of environmental variables used in this study. The name in the "ID" column coincides with the ID provided for these environmental variables in the "CLIMATE_TWAS.csv" as well as the individual file name provided for GWAS analysis in both compressed folders that include the GWAS output for these analyses using either GWAS analysis method. For each "ID," this file provides a description of the source, the variable, its units, the analyzed timeframe, the link where the source files were obtained from to extract environmental variables, the date in which data was accessed, type of variable, and descriptive statistics, including the number of missing values per variable.<br><br>7. "File_names_Environmental_GWAS_2021_LM.csv": list of files included in the "Environmental_GWAS_2021_LM.zip" folder.<br><br>8. "File_names_Environmental_GWAS_2021_AMM.csv": list of files included in the "Environmental_GWAS_2021_AMM.zip" folder.<br><br>8. README.txt    <br><br>9. "AraCLIM-V2.zip": Code and data for AracLIM V2.0. For the latest version visit (https://github.com/CLIMtools/AraCLIM-V2).<br><br>10. "GenoCLIM-V2.zip": Code and data for GenoCLIM V2.0. For the latest version (https://github.com/CLIMtools/GenoCLIM-V2).<br><br>11. "CLIMGeno-V2.zip ": Code and data for CLIMGeno V2.0. For the latest version (https://github.com/CLIMtools/CLIMGeno-V2).<br><br>12. "T-CLIM.zip ": Code and data for T-CLIM V2.0. For the latest version (https://github.com/CLIMtools/T-CLIM).