69,849 research outputs found

    GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data

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    Background: With its simple library preparation and robust approach to genome reduction, genotyping-by-sequencing (GBS) is a flexible and cost-effective strategy for SNP discovery and genotyping, provided an appropriate reference genome is available. For resource-limited curation, research, and breeding programs of underutilized plant genetic resources, however, even low-depth references may not be within reach, despite declining sequencing costs. Such programs would find value in an open-source bioinformatics pipeline that can maximize GBS data usage and perform high-density SNP genotyping in the absence of a reference. Results: The GBS SNP-Calling Reference Optional Pipeline (GBS-SNP-CROP) developed and presented here adopts a clustering strategy to build a population-tailored “Mock Reference” from the same GBS data used for downstream SNP calling and genotyping. Designed for libraries of paired-end (PE) reads, GBS-SNP-CROP maximizes data usage by eliminating unnecessary data culling due to imposed read-length uniformity requirements. Using 150 bp PE reads from a GBS library of 48 accessions of tetraploid kiwiberry (Actinidia arguta), GBS-SNP-CROP yielded on average three times as many SNPs as TASSEL-GBS analyses (32 and 64 bp tag lengths) and over 18 times as many as TASSEL-UNEAK, with fewer genotyping errors in all cases, as evidenced by comparing the genotypic characterizations of biological replicates. Using the published reference genome of a related diploid species (A. chinensis), the reference-based version of GBS-SNP-CROP behaved similarly to TASSEL-GBS in terms of the number of SNPs called but had an improved read depth distribution and fewer genotyping errors. Our results also indicate that the sets of SNPs detected by the different pipelines above are largely orthogonal to one another; thus GBS-SNP-CROP may be used to augment the results of alternative analyses, whether or not a reference is available. Conclusions: By achieving high-density SNP genotyping in populations for which no reference genome is available, GBS-SNP-CROP is worth consideration by curators, researchers, and breeders of under-researched plant genetic resources. In cases where a reference is available, especially if from a related species or when the target population is particularly diverse, GBS-SNP-CROP may complement other reference-based pipelines by extracting more information per sequencing dollar spent. The current version of GBS-SNP-CROP is available at https://github.com/halelab/GBS-SNP-CROP.gi

    Capability of APSIM-Oryza to stimulate lowland rice-based farming systems under nitrogen treatments in a tropical climate

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    Rice is the most important crop in Asia and the staple food for most of the world’s population. Due to the overwhelming importance of this crop, modelling rice-based farming systems will provide valuable help to compare experimental research findings across regions, extrapolate field experimental data to wider environments, develop management recommendations and decision-support systems, explore effects of climate change and adaptation options, and prediction of crop yield. There is an increasing demand for the capability to simulate rice-based cropping systems, especially in Asia. Such a system capability will allow expanded investigation of nitrogen dynamics, crop sequencing, intercropping, crop residue management and soil and water management. Incorporation of the ORYZA2000 rice model(Bouman and van Laar, 2006) into APSIM (Agricultural Production Systems Simulator (APSIM-Oryza) together with recent work on carbon and nitrogen dynamics in transitional flooded/non-flooded systems(Gaydon et al., 2009) has facilitated long-term simulation of lowland rice-based farming systems scenarios. However, the capability of APSIM-Oryza to simulate rice-based crop sequences involving other crops has undergone limited testing to this point and under a variety of crop management practices and cropping systems. In this paper, we detail testing of the APSIM-Oryza simulation model against an experimental dataset involving lowland rice-rice-soybean crop rotation in West Nusa Tenggara Province(NTB) Indonesi

    A Vavilovian approach to discovering crop-associated microbes with potential to enhance plant immunity

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    Through active associations with a diverse community of largely non-pathogenic microbes, a plant may be thought of as possessing an “extended genotype,” an interactive cross-organismal genome with potential, exploitable implications for plant immunity. The successful enrichment of plant microbiomes with beneficial species has led to numerous commercial applications, and the hunt for new biocontrol organisms continues. Increasingly flexible and affordable sequencing technologies, supported by increasingly comprehensive taxonomic databases, make the characterization of non-model crop-associated microbiomes a widely accessible research method toward this end; and such studies are becoming more frequent. A summary of this emerging literature reveals, however, the need for a more systematic research lens in the face of what is already a metagenomics data deluge. Considering the processes and consequences of crop evolution and domestication, we assert that the judicious integration of in situ crop wild relatives into phytobiome research efforts presents a singularly powerful tool for separating signal from noise, thereby facilitating a more efficient means of identifying candidate plant-associated microbes with the potential for enhancing the immunity and fitness of crop species

    Integrated analysis of root microbiomes of soybean and wheat from agricultural fields

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    Root associated bacteria are critical for plant growth and health. Understanding the composition and role of root microbiota is crucial toward agricultural practices that are less dependent on chemical fertilization, which has known negative effects on the environment and human health. Here we analyzed the root-associated microbiomes of soybean and wheat under agricultural field conditions. We took samples from 11 different production fields across a large geographic area. We used 16S rRNA pyrosequencing to explore root microbial communities and also obtained 2,007 bacterial isolates from rhizospheres, which were tested for the presence of plant growth promoting (PGP) traits in-vitro. We observed that pH and nitrate content correlated with beta diversity variability of rhizospheric bacterial communities despite the variable field conditions. We described the dominant bacterial groups associated to roots from both crops at a large geographic scale and we found that a high proportion of them (60-70%) showed more than 97% similarity to bacteria from the isolated collection. Moreover, we observed that 55% of the screened isolates presented PGP activities in vitro. These results are a significant step forward in understanding crop-associated microbiomes and suggest that new directions can be taken to promote crop growth and health by modulating root microbiomes.Fil: Rascovan, Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Agrobiotecnología de Rosario; ArgentinaFil: Carbonetto, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Agrobiotecnología de Rosario; ArgentinaFil: Perrig, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Rizobacter Argentina S. A.; ArgentinaFil: Diaz, Marisa. Rizobacter Argentina S. A.; ArgentinaFil: Canciani, Wilter. Rizobacter Argentina S. A.; ArgentinaFil: Abalo, Matías. Rizobacter Argentina S. A.; ArgentinaFil: Alloati, Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Agrobiotecnología de Rosario; ArgentinaFil: González Anta, Gustavo. Rizobacter Argentina S. A.; ArgentinaFil: Vazquez, Martin Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Instituto de Agrobiotecnología de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentin

    Syngenta -- The Genome Giant?

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    Swiss gene giant Syngenta, the world's largest agrochemical corporation and third largest seed company (see tables) has applied for patents that could effectively allow the company to monopolize key gene sequences that are vital for rice breeding as well as dozens of other plant species. While the Genome Giant "donates" rice germplasm and information to public researchers with one hand, it is attempting to monopolize rice resources with the other. Governments, public sector researchers and the United Nations must re-evaluate and reform their cozy connections to companies like Syngenta

    Genetic mapping of legume orthologs reveals high conservation of synteny between lentil species and the sequenced genomes of Medicago and chickpea.

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    Lentil (Lens culinaris Medik.) is a global food crop with increasing importance for food security in south Asia and other regions. Lens ervoides, a wild relative of cultivated lentil, is an important source of agronomic trait variation. Lens is a member of the galegoid clade of the Papilionoideae family, which includes other important dietary legumes such as chickpea (Cicer arietinum) and pea (Pisum sativum), and the sequenced model legume Medicago truncatula. Understanding the genetic structure of Lens spp. in relation to more fully sequenced legumes would allow leveraging of genomic resources. A set of 1107 TOG-based amplicons were identified in L. ervoides and a subset thereof used to design SNP markers for mapping. A map of L. ervoides consisting of 377 SNP markers spread across seven linkage groups was developed using a GoldenGate genotyping array and single SNP marker assays. Comparison with maps of M. truncatula and L. culinaris documented considerable shared synteny and led to the identification of a few major translocations and a major inversion that distinguish Lens from M. truncatula, as well as a translocation that distinguishes L. culinaris from L. ervoides. The identification of chromosome-level differences among Lens spp. will aid in the understanding of introgression of genes from L. ervoides into cultivated L. culinaris, furthering genetic research and breeding applications in lentil

    Selection Mapping Identifies Loci Underpinning Autumn Dormancy in Alfalfa (Medicago sativa).

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    Autumn dormancy in alfalfa (Medicago sativa) is associated with agronomically important traits including regrowth rate, maturity, and winter survival. Historical recurrent selection experiments have been able to manipulate the dormancy response. We hypothesized that artificial selection for dormancy phenotypes in these experiments had altered allele frequencies of dormancy-related genes. Here, we follow this hypothesis and analyze allele frequency changes using genome-wide polymorphisms in the pre- and postselection populations from one historical selection experiment. We screened the nondormant cultivar CUF 101 and populations developed by three cycles of recurrent phenotypic selection for taller and shorter plants in autumn with markers derived from genotyping-by-sequencing (GBS). We validated the robustness of our GBS-derived allele frequency estimates using an empirical approach. Our results suggest that selection mapping is a powerful means of identifying genomic regions associated with traits, and that it can be exploited to provide regions on which to focus further mapping and cloning projects
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