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Abstract Single nucleotide polymorphisms (SNPs) can alter RNA structure by changing the proportions of existing conformations or leading to new conformations in the structural ensemble. Such structure-changing SNPs, or riboSNitches, have been associated with diseases in humans and climate adaptation in plants. While several computational tools are available for predicting whether an SNP is a riboSNitch, these tools were generally developed to analyze individual RNAs and are not optimized for genome-wide analyses. To fill this gap, we developed VariantFoldRNA, a flexible, containerized, and automated pipeline for genome-wide prediction of riboSNitches. Our pipeline automatically installs all dependencies, can be run locally or on high-performance clusters, and is modular, enabling the user to customize the analysis for the research question of interest. VariantFoldRNA can predict riboSNitches genome-wide at user-specified temperatures and splicing conditions, opening the door to novel analyses. The pipeline is an open-source command-line tool that is freely available at https://github.com/The-Bevilacqua-Lab/variantfoldrna. 

Modern crop varieties display a degree of mismatch between their current distributions and the suitability of the local climate for their productivity. To address this issue, we present Oryza CLIMtools (https:// gramene.org/CLIMtools/oryza_v1.0/), the first resource for pan-genome prediction of climate-associated genetic variants in a crop species. Oryza CLIMtools consists of interactive web-based databases that enable the user to (1) explore the local environments of traditional rice varieties (landraces) in South- East Asia and (2) investigate the environment by genome associations for 658 Indica and 283 Japonica rice landrace accessions collected from georeferenced local environments and included in the 3K Rice Genomes Project. We demonstrate the value of these resources by identifying an interplay between flowering time and temperature in the local environment that is facilitated by adaptive natural variation in OsHD2 and disrupted by a natural variant in OsSOC1. Prior quantitative trait locus analysis has suggested the importance of heterotrimeric G proteins in the control of agronomic traits. Accordingly, we analyzed the climate associations of natural variants in the different heterotrimeric G protein subunits. We identified a coordinated role of G proteins in adaptation to the prevailing potential evapotranspiration gradient and revealed their regulation of key agronomic traits, including plant height and seed and panicle length. We conclude by highlighting the prospect of targeting heterotrimeric G proteins to produce climate-resilient crops.