36 research outputs found

    Expression Atlas: gene and protein expression across multiple studies and organisms

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    Expression Atlas (http://www.ebi.ac.uk/gxa) is an added value database that provides information about gene and protein expression in different species and contexts, such as tissue, developmental stage, disease or cell type. The available public and controlled access data sets from different sources are curated and re-analysed using standardized, open source pipelines and made available for queries, download and visualization. As of August 2017, Expression Atlas holds data from 3,126 studies across 33 different species, including 731 from plants. Data from large-scale RNA sequencing studies including Blueprint, PCAWG, ENCODE, GTEx and HipSci can be visualized next to each other. In Expression Atlas, users can query genes or gene-sets of interest and explore their expression across or within species, tissues, developmental stages in a constitutive or differential context, representing the effects of diseases, conditions or experimental interventions. All processed data matrices are available for direct download in tab-delimited format or as R-data. In addition to the web interface, data sets can now be searched and downloaded through the Expression Atlas R package. Novel features and visualizations include the on-the-fly analysis of gene set overlaps and the option to view gene co-expression in experiments investigating constitutive gene expression across tissues or other conditions

    Tools for building de novo transcriptome assembly

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    The availability of RNA-Seq method allows researchers to capture the spatial or temporal profile of transcriptomes from various types of biological samples. The transcriptome data from a species can be analyzed in the context of its sequenced genomes or closely related genome to score biological sample-specific transcript isoforms, novel transcribed regions and to refine gene models including identification of new genes, in addition to the differential gene expression analysis. However, many plant species of importance currently lack a sequenced genome or a closely related reference genome and thus, rely on the de novo methods for generating transcript models and transcriptome assemblies. Here we describe various tools used for de novo transcriptome assembly and discuss the data management practices and standards

    Loss of a Premature Stop Codon in the Rice Wall-Associated Kinase 91 (<i>WAK91</i>) Gene Is a Candidate for Improving Leaf Sheath Blight Disease Resistance

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    Leaf sheath blight disease (SB) of rice caused by the soil-borne fungus Rhizoctonia solani results in 10–30% global yield loss annually and can reach 50% under severe outbreaks. Many disease resistance genes and receptor-like kinases (RLKs) are recruited early on by the host plant to respond to pathogens. Wall-associated receptor kinases (WAKs), a subfamily of receptor-like kinases, have been shown to play a role in fungal defense. The rice gene WAK91 (OsWAK91), co-located in the major SB resistance QTL region on chromosome 9, was identified by us as a candidate in defense against rice sheath blight. An SNP mutation T/C in the WAK91 gene was identified in the susceptible rice variety Cocodrie (CCDR) and the resistant line MCR010277 (MCR). The consequence of the resistant allele C is a stop codon loss, resulting in an open reading frame with extra 62 amino acid carrying a longer protein kinase domain and additional phosphorylation sites. Our genotype and phenotype analysis of the parents CCDR and MCR and the top 20 individuals of the double haploid SB population strongly correlate with the SNP. The susceptible allele T is present in the japonica subspecies and most tropical and temperate japonica lines. Multiple US commercial rice varieties with a japonica background carry the susceptible allele and are known for SB susceptibility. This discovery opens the possibility of introducing resistance alleles into high-yielding commercial varieties to reduce yield losses incurred by the sheath blight disease

    De Novo Transcriptome Assembly and Analyses of Gene Expression during Photomorphogenesis in Diploid Wheat Triticum monococcum

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    Citation: Fox, S., Geniza, M., Hanumappa, M., . . . Jaiswal, P. (2014). De Novo Transcriptome Assembly and Analyses of Gene Expression during Photomorphogenesis in Diploid Wheat Triticum monococcum. PLoS One, 9(5), e96855. https://doi.org/10.1371/journal.pone.0096855Background: Triticum monococcum (2n) is a close ancestor of T. urartu, the A-genome progenitor of cultivated hexaploid wheat, and is therefore a useful model for the study of components regulating photomorphogenesis in diploid wheat. In order to develop genetic and genomic resources for such a study, we constructed genome-wide transcriptomes of two Triticum monococcum subspecies, the wild winter wheat T. monococcum ssp. aegilopoides (accession G3116) and the domesticated spring wheat T. monococcum ssp. monococcum (accession DV92) by generating de novo assemblies of RNA-Seq data derived from both etiolated and green seedlings. Principal Findings: The de novo transcriptome assemblies of DV92 and G3116 represent 120,911 and 117,969 transcripts, respectively. We successfully mapped ~90% of these transcripts from each accession to barley and ~95% of the transcripts to T. urartu genomes. However, only ~77% transcripts mapped to the annotated barley genes and ~85% transcripts mapped to the annotated T. urartu genes. Differential gene expression analyses revealed 22% more light up-regulated and 35% more light down-regulated transcripts in the G3116 transcriptome compared to DV92. The DV92 and G3116 mRNA sequence reads aligned against the reference barley genome led to the identification of ~500,000 single nucleotide polymorphism (SNP) and ~22,000 simple sequence repeat (SSR) sites. Conclusions: De novo transcriptome assemblies of two accessions of the diploid wheat T. monococcum provide new empirical transcriptome references for improving Triticeae genome annotations, and insights into transcriptional programming during photomorphogenesis. The SNP and SSR sites identified in our analysis provide additional resources for the development of molecular markers
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