The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

Meiosis Research in Orphan and Non-orphan Tropical Crops

Cooperación internacional e interdisciplinaria entre la Universidad de Costa Rica y The University of Texas Southwestern Medical Center.Plant breeding is directly linked to the development of crops that can effectively adapt to challenging conditions such as soil nutrient depletion, water pollution, drought, and anthropogenic climate change. These conditions are extremely relevant in developing countries already burdened with population growth and unchecked urban expansion, especially in the tropical global southern hemisphere. Engineering new crops thus has potential to enhance food security, prevent hunger, and spur sustainable agricultural growth. A major tool for the improvement of plant varieties in this context could be the manipulation of homologous recombination and genome haploidization during meiosis. The isolation or the design of mutations in key meiotic genes may facilitate DNA recombination and transmission of important genes quickly and efficiently. Genome haploidization through centromeric histone mutants could be an option to create new crosses rapidly. This review covers technical approaches to engineer key meiotic genes in tropical crops as a blueprint for future work and examples of tropical crops in which such strategies could be applied are given.Universidad de Costa Rica/[736-B6-602]/UCR/Costa RicaUniversidad de Costa Rica/[736-B5-A52]/UCR/Costa RicaUniversidad de Costa Rica/[814-B5-A49]/UCR/Costa RicaUniversidad de Costa Rica/[736-B7-801]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Agroalimentarias::Estación Experimental Agrícola Fabio Baudrit Moreno (EEAFBM)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Agroalimentarias::Jardín Botánico Lankester (JBL

Rapid genotyping with DNA micro-arrays for high-density linkage mapping and QTL mapping in common buckwheat (Fagopyrum esculentum Moench)

For genetic studies and genomics-assisted breeding, particularly of minor crops, a genotyping system that does not require a priori genomic information is preferable. Here, we demonstrated the potential of a novel array-based genotyping system for the rapid construction of high-density linkage map and quantitative trait loci (QTL) mapping. By using the system, we successfully constructed an accurate, high-density linkage map for common buckwheat (Fagopyrum esculentum Moench); the map was composed of 756 loci and included 8,884 markers. The number of linkage groups converged to eight, which is the basic number of chromosomes in common buckwheat. The sizes of the linkage groups of the P1 and P2 maps were 773.8 and 800.4 cM, respectively. The average interval between adjacent loci was 2.13 cM. The linkage map constructed here will be useful for the analysis of other common buckwheat populations. We also performed QTL mapping for main stem length and detected four QTL. It took 37 days to process 178 samples from DNA extraction to genotyping, indicating the system enables genotyping of genome-wide markers for a few hundred buckwheat plants before the plants mature. The novel system will be useful for genomics-assisted breeding in minor crops without a priori genomic information

On the usefulness of mock genomes to define heterotic pools, testers, and hybrid predictions in orphan crops

The advances in genomics in recent years have increased the accuracy and efficiency of breeding programs for many crops. Nevertheless, the adoption of genomic enhancement for several other crops essential in developing countries is still limited, especially for those that do not have a reference genome. These crops are more often called orphans. This is the first report to show how the results provided by different platforms, including the use of a simulated genome, called the mock genome, can generate in population structure and genetic diversity studies, especially when the intention is to use this information to support the formation of heterotic groups, choice of testers, and genomic prediction of single crosses. For that, we used a method to assemble a reference genome to perform the single-nucleotide polymorphism (SNP) calling without needing an external genome. Thus, we compared the analysis results using the mock genome with the standard approaches (array and genotyping-by-sequencing (GBS)). The results showed that the GBS-Mock presented similar results to the standard methods of genetic diversity studies, division of heterotic groups, the definition of testers, and genomic prediction. These results showed that a mock genome constructed from the population’s intrinsic polymorphisms to perform the SNP calling is an effective alternative for conducting genomic studies of this nature in orphan crops, especially those that do not have a reference genome

A Planetary Health Perspective on Agroforestry in Sub-Saharan Africa

Human activities change the structure and function of the environment with cascading impacts on human health, a concept known as “planetary health.” Agroforestry—the management of trees with crops and livestock—alters microclimates, hydrology, biogeochemistry, and biodiversity. Besides the nutritional benefits of increased fruit consumption, however, the ways agroforestry affects human health are rarely articulated. This review makes that link. We analyze the pathways through which tree-based farm and landscape change affect food and nutrition security, the spread of infectious disease, the prevalence of non-communicable diseases, and human migration in Sub-Saharan Africa. The available evidence suggests that, despite some increased risks of infectious disease, agroforestry is likely to improve a diverse range of pressing health concerns. We therefore examine the factors determining agroforestry use and identify three drivers of social and environmental change that will determine the future uptake of agroforestry in the region. Thirty percent of Sub-Saharan Africa's cropland has at least 10% tree cover. The available evidence indicates that agroforestry drives environmental change, which can improve a diverse range of pressing health concerns such as malnutrition, spread of infectious disease, prevalence of non-communicable disease, and human migration. This, however, does not always apply: transdisciplinary, participatory approaches are needed to dive more deeply into specific land-management systems to identify synergies and tradeoffs among health outcomes

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

Plataforma de supercomputación para bioinformática

En el año 2007 la Universidad de Málaga amplió y trasladó sus recursos de cálculo a un nuevo centro dedicado exclusivamente a la investigación: el edificio de Supercomputación y Bioinnovación sito en el Parque Tecnológico de Andalucía. Este edificio albergaría también la Plataforma Andaluza de Bioinformática junto con otras unidades y laboratorios con instrumentación muy especializada. Desde aquel momento he trabajado como administrador de los recursos de supercomputación del centro y como parte del equipo bioinformático para proporcionar soporte a un gran número de investigadores en sus tareas diarias. Teniendo una visión de ambas partes, fue fácil detectar las carencias existentes en la bioinformática que podían ser cubiertas con una aplicación adecuada de los recursos de cálculo disponibles, y ahí es donde surgió la semilla que nos llevó a comenzar los primeros trabajos que componen este estudio. Al haberse realizado en un entorno tan orientado a la resolución de problemas como el que hemos descrito, esta tesis tendrá un carácter eminentemente práctico, donde cada aportación realizada lleva un importante estudio teórico detrás, pero que culmina en un resultado práctico concreto que puede aplicarse a problemas cotidianos de la bioinformática o incluso de otras áreas de la investigación. Así, con el objetivo de facilitar el acceso a los recursos de supercomputación para los bioinformáticos, hemos creado un generador automático de interfaces web para programas que se ejecutan en línea de comandos, que permite ejecutar los trabajos utilizando recursos de supercomputación de forma transparente para el usuario. Además aportamos un sistema de escritorios virtuales que permiten el acceso remoto a un conjunto de programas ya instalados que proporcionan interfaces visuales para analizar pequeños conjuntos de datos o visualizar los resultados más complejos que hayan sido generados con recursos de supercomputación. Para optimizar el uso de los recursos de supercomputación hemos diseñado un nuevo algoritmo para la ejecución distribuida de tareas, que puede utilizarse tanto en el diseño de nuevas herramientas como para optimizar la ejecución de programas ya existentes. Por otra parte, preocupados por el incremento en la cantidad de datos producidos por las técnicas de ultrasecuenciación, aportamos un nuevo formato de compresión de secuencias, que además de reducir el espacio de almacenamiento utilizado, permite buscar y extraer rápidamente cualquier secuencia almacenada sin necesidad de descomprimir el archivo completo. En el desarrollo de nuevos algoritmos para resolver problemas biológicos concretos, proporcionamos cuatro herramientas nuevas que abarcan la búsqueda de regiones divergentes en alineamientos, el preprocesamiento y limpieza de lecturas obtenidas mediante técnicas de ultrasecuenciación, el análisis de transcriptomas de especies no modelo obtenidos mediante ensamblajes de novo y un prototipo para anotar secuencias genómicas incompletas. Como solución para la difusión y el almacenamiento a largo plazo de resultados obtenidos en diversas investigaciones, se ha desarrollado un sistema genérico de máquinas virtuales para bases de datos de transcriptómica que ya está siendo utilizado en varios proyectos. Además, con el ánimo de difundir los resultados de nuestro trabajo, todos los algoritmos y herramientas productos de esta tesis se han publicado como código abierto en https://github.com/dariogf