Resultados de investigación

Leaves of Prunus dulcis cv Texas, Prunus persica cv Early Gold and one interspecific F1 hybrid were collected. For the analysis of DNA methylation, we used two biological replicates per genotype. For RNA-seq analysis, we used three biological replicates per genotype. -- Organisms: Prunus dulcis; Prunus persica; Prunus persica x Prunus dulcis. -- Experiment type Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing., Resources available on the publisher's site: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE198152, We analysed the DNA methylation and transcription levels of transposable elements and genes in leaves of Prunus persica and Prunus dulcis and in their F1 hybrid using high-throughput sequencing tecnhologies. We can conclude that the merging of the two parental genomes in the P. persica x P. dulcis hybrid does not result in a “genomic shock” with significant changes in the DNA methylation or in the transcription., Peer reviewed

Comparative gene expression profiling analysis of RNA-seq data of rice plants treated either with an control Fe or high Fe nutrient solution and infected or not with Magnaporthe oryza. -- Organism: Oryza sativa. -- Experiment type: Expression profiling by high throughput sequencing., Resources available on the publisher's site: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE202997, To investigate the role of iron excess in rice immune responses to Magnaporthe oryzae infection. Gene expression profiling analysis were performed using data obtained from RNA-seq of rice plants grown in differential iron supply and challenged with Magnaporthe oryzae spores., Peer reviewed

1 table., List of Gene Ontology (GO) terms related to DEGs according to n-6/n-3 PUFA ratio., Peer reviewed

Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. -- This repository contains the following files: budget_components_ENS.nc Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. If required the individual data sets used for the ensemble, please contact the author. -- masks.nc netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. -- dmaps_trend.pkl (and .xlsx) Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl). -- som_trend.pkl (and .xlsx). Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl), This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/., budget_components_ENS.nc, dmaps_trends.pkl, dmaps_trends.xlsx, masks.nc, SOM_trends.pkl, SOM_trends.xlsx, Peer reviewed

This repository contains the following files: budget_components_ENS.nc. Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). If required the individual data sets used for the ensemble, please contact the author. -- masks.nc: netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. -- dmaps_trend.pkl (and .xlsx): Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl). -- som_trend.pkl (and .xlsx): Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl), This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022, submited)., budget_components_ENS.nc, dmaps_trends.pkl, dmaps_trends.xlsx, masks.nc, SOM_trends.pkl, SOM_trends.xlsx, Peer reviewed

[EN] It contains a netCDF file by variable and hydrological year with daily data for the variables Liquid Precipitation, Total Precipitation, Relative Humidity, Wind Speed and Temperature. Hourly data are available by contacting the author. [ES] Contiene un fichero netCDF por variable y año hidrológico con los datos diarios de las variables Precipitación líquida, Precipitación total, Humedad relativa, Velocidad del Viento y Temperatura. Los datos horarios están disponibles contactando con el autor., [EN] Daily gridded dataset of screen-level meteorological variables created with the aim to force hydrological and land-surface models. The grid has a spatial resolution of 2.5 km and was created using the SAFRAN algorithm. SAFRAN uses optimal interpolation to combine a first guess (ERA5) and meteorological observations from AEMET and Météo-France. The dataset contains the following variables: precipitation, wind speed, temperature, potential evapotranspiration (Penman-Monteith) and relative humidity. The dataset was generated within the project EFA210/16 PIRAGUA ("Evaluation and prospective of the water resources of the Pyrenees in a context of climate change, and adaptation measures with impact on the territory")., [ES] Conjunto de datos diarios en retícula de variables meteorológicas cerca de la superficie creado con el objetivo de forzar modelos hidrológicos y de superficie terrestre (LSM). La malla tiene una resolución de 2,5 km, y se creó utilizando el algoritmo SAFRAN. SAFRAN utiliza interpolación óptima para combinar una primera estimación (ERA5) y las observaciones meteorológicas de AEMET y Météo-France. El conjunto de datos contiene las siguientes variables: precipitación, velocidad del viento, temperatura, evapotranspiración potencial (Penman-Monteith) y humedad relativa. El conjunto de datos se generó en el proyecto EFA210/16 PIRAGUA ("Evaluación y prospectiva de los recursos hídricos de los Pirineos en un contexto de cambio climático, y medidas de adaptación con impacto en el territorio")., [FR] Ensemble de données maillées de variables météorologiques proches de la surface créées dans le but de forcer des modèles hydrologiques et de surface terrestre (LSM). Le maillage a une résolution de 2,5 km et a été créé à l'aide de l'algorithme SAFRAN. SAFRAN utilise une interpolation optimale pour combiner une première estimation (ERA5) et des observations météorologiques d'AEMET et de Météo-France. L'ensemble de données contient les variables suivantes : précipitations, vitesse du vent, température, évapotranspiration potentielle (Penman-Monteith) et humidité relative. Le jeu de données a été généré dans le cadre du projet EFA210/16 PIRAGUA ("Evaluation et prospective des ressources en eau des Pyrénées dans un contexte de changement climatique, et mesures d'adaptation ayant un impact sur le territoire")., This dataset was developed within the project EFA210/16 PIRAGUA (“Evaluación y prospectiva de los recursos hídricos de los Pirineos en un contexto de cambio climático, y medidas de adaptación con impacto en el territorio / Evaluation et prospective des ressources en eau des Pyrénées dans un contexte de changement climatique, et mesures d’adaptation avec un impact sur le territoire”), co-funded by the European Regional Development Fund (ERDF) through the Interreg V-A Spain France Andorra program (POCTEFA 2014-2020) (65%) and the project’s partners: CSIC, UPV/EHU, UB, OE, IGME, CNRS, BRGM, INRAE and OBSA (35%)., No

The figwort family, Scrophulariaceae, comprises c. 1800 species whose evolutionary relationships at the tribal level have proven difficult to resolve, hindering our ability to understand their origin and diversification. We designed a specific probe kit for Scrophulariaceae, targeting 849 nuclear loci and obtaining plastid regions as by-products. We sampled c. 80% of the genera described in the family and use the nuclear dataset to estimate evolutionary relationships, timing of diversification and biogeographic patterns. Ten tribes, including two new tribes, Androyeae and Camptolomeae, are supported and the phylogenetic positions of Androya, Camptoloma and Phygelius are unveiled. Our study reveals a major diversification at c. 60 Ma in the Paleogene, where two different lineages diversified, one of which gave rise to nearly 81% of extant species. A southern African origin is estimated for most modern-day tribes, with two exceptions, the American Leucophylleae and the mainly Australian Myoporeae. The rapid mid-Eocene diversification is aligned with geographic expansion in Africa in most tribes, from southern to tropical Africa, followed by multiple dispersals out-of-Africa. Our robust phylogeny provides a framework for future studies aimed at understanding the role of macroevolutionary patterns and processes that generated Scrophulariaceae diversity., Spanish Ministry of Science and Innovation (CGL2015-67849-P), USA National Science Foundation (DEB, 131111), Peer reviewed

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2

4 files, Additional data derived from "Bivalves present the largest and most diversified repertoire of Toll-like receptors in the animal kingdom, suggesting broad-spectrum pathogen recognition in marine waters". -TLR sequences indetified in the annotation of 85 metazoan genomes -TLR sequences de novo indentified in the genomes of three mussel species -Snakemake pipeline and script to calculate saturation curves from the salmon output of the mapping of different mussel SRA transcriptomic samples to the mussel genome -Pipeline used to calculate the modulated transcripts for different mussel transcriptomes, Peer reviewed

Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas for the impact-relevant variables and indices., The IPCC WG1 Interactive Atlas is an online tool that provides interactive visualizations and geospatial data related to the physical scientific basis of climate change. This platform allows users to explore and visualize geographical information interactively and dynamically. It presents data using maps, charts, and other visualizations, enabling users to understand complex information spatially and temporally. The interactive Atlas includes climate data for relevant variables, key climate indicators, and trends, all derived from climate model simulations., The IPCC-WGI AR6 Interactive Atlas dataset comprises monthly gridded data from global (CMIP5, CMIP6) and regional (CORDEX) model projections for the impact-relevant variables and indices featured in the IPCC Interactive Atlas (https://interactive-atlas.ipcc.ch)., Peer reviewed, 2