Resultados de investigación

1. Supplementary Data: -Genes involved in pollen development -Pectin Methyl-Esterases and biotic stress -MYB14 transcription factor modulated genes Supplementary table 1: A selection of few example queries using GraphQL, pyCOMPASS and rCOMPASS Supplementary Table 2: Comparison table between VESPUCCI v1 and v2 main features Supplementary Table 3: Comparison table between several on-line resources for grapevine and other plant species. Supplementary Figure 1. Bar Plot of VESPUCCI v1 sample distributions based on their condition annotation in Vitis vinifera (left) and non-vinifera (right) experiments. Samples are divided by tissues (X-axis) and their abundances in square root scale (Y-axis). In the left plot, different colors are used to denote untreated samples, biotic-treated samples and abiotic-treated samples. In the right plot, colors are used to differentiate between different Vitis species, subspecies and cross species. The great majority of samples (50%) come from Vitis vinifera untreated fruit samples taken at different developmental stages. Non-vinifera species and hybrids samples represent 7.5% of the dataset while 8.5% of the total are stress-related (1.4% being fruit) and 34% are Vitis vinifera untreated non-fruit samples., Peer reviewed

Supplementary Material 8: Variable values describing each of the ecogeographic categories of the Ecogeographic Land Characterization map for lentil wild species (Bio 1 values expressed in ºC). Mean, median, maximum, minimum and standard deviation values shown., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary Material 9: Populations of wild relatives of lentils in Europe selected through the Environmental Filtering Method of the Predictive Characterization technique and potentially tolerant to following abiotic stresses: Drought, soil salinity and waterlogging. Description of each field can be found in the Legend sheet., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary Material 10: True Statistic Skill values obtained for the nine tested algorithms (75% training data, 100 runs per algorithm) for lentil rust resistance., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

First of all, we must run the script called “Packages” in order to install all the packages that are needed. The rest of the code is structured in six folders, one folder for each model compiled. The structure of these folders is presented below. - Folder (1). It is called “1)Data” and it contains the datasets used as input at the folders from (2) to (6). - Folders from (2) to (6). They have one folder for each dataset and a txt file where the model is written using JAGS language. At each folder we find two R scripts, one where the model is estimated and the other for the prediction. The outputs of the estimating script are used as input for the predicting script. - Folder (2). It also contains an additional folder called “FemaleFamilies_DifferentSPBal”. Here we have estimate the proposed model using different selected principal balances to compare the results when extracting different percentage of variance to the data. This folder contains one folder for each combination of selected principal balances analysed., Peer reviewed

Supplementary Material 11: Subset of populations of wild relatives of lentils in Europe selected through the Calibration Method of the Predictive Characterization technique and potentially resistant to lentil rust (Uromyces vicia-fabae (Pers.) Schröt)., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed

Supplementary information. Peer Review File. Source Data, Peer reviewed

Peer reviewed

Genomic architectures of the plant viruses, detection by RT-qPCR, additional results of Cas12a-based detection, nuclease expression and purification scheme, additional results of Cas13a/d-based detection, nucleic acid sequences, and numerical data., Peer reviewed

The dataset contains 6 growing seasons of a local winter wheat cultivar ‘Wakanui’ at two farms located in the Canterbury Region of New Zealand. The data of the experiment was used in the AgMIP-Wheat Phase 4 project to evaluate the performance of an ensemble of 29 crop models to predict the effect of changing sowing dates and rates on yield and yield components, in a high-yielding environment. The treatments were managed for non-stress conditions. Data include local daily weather, soil characteristics and initial soil N conditions, crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, and yield components), and cultivar information. Simulations include both daily in-season and end-of-season results from 29 wheat crop models., Peer reviewed