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      A new genome allows the identification of genes associated with natural variation in aluminium tolerance in Brachiaria grasses

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          Abstract

          Toxic concentrations of aluminium cations and low phosphorus availability are the main yield-limiting factors in acidic soils, which represent half of the potentially available arable land. Brachiaria grasses, which are commonly sown as forage in the tropics because of their resilience and low demand for nutrients, show greater tolerance to high concentrations of aluminium cations (Al 3+) than most other grass crops. In this work, we explored the natural variation in tolerance to Al 3+ between high and low tolerant Brachiaria species and characterized their transcriptional differences during stress. We identified three QTLs (quantitative trait loci) associated with root vigour during Al 3+ stress in their hybrid progeny. By integrating these results with a new Brachiaria reference genome, we identified 30 genes putatively responsible for Al 3+ tolerance in Brachiaria. We observed differential expression during stress of genes involved in RNA translation, response signalling, cell wall composition, and vesicle location homologous to aluminium-induced proteins involved in limiting uptake or localizing the toxin. However, there was limited regulation of malate transporters in Brachiaria, which suggests that exudation of organic acids and other external tolerance mechanisms, common in other grasses, might not be relevant in Brachiaria. The contrasting regulation of RNA translation and response signalling suggests that response timing is critical in high Al 3+-tolerant Brachiaria.

          Abstract

          We identified QTLs, genes, and molecular responses in high and low tolerant Brachiaria grasses associated with aspects of the response to aluminium stress, such as regulation, cell wall composition, and active transport.

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          Most cited references 81

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              Cutadapt removes adapter sequences from high-throughput sequencing reads

               Marcel Martin (2011)
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                Author and article information

                Contributors
                Role: Editor
                Journal
                J Exp Bot
                J Exp Bot
                exbotj
                Journal of Experimental Botany
                Oxford University Press (UK )
                0022-0957
                1460-2431
                02 February 2021
                16 October 2020
                16 October 2020
                : 72
                : 2
                : 302-319
                Affiliations
                [1 ] International Center for Tropical Agriculture (CIAT) , Cali, Colombia
                [2 ] Earlham Institute, Norwich Research Park , Norwich, UK
                [3 ] Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University , Aberystwyth, UK
                [4 ] Pontificia Universidad Católica de Chile , Chile
                Author notes
                Present address: Department of Horticulture, University of Arkansas, 306 Plant Sciences Bldg, Fayetteville, AR 72701, USA.
                Present address: NIAB, Huntingdon Road, Cambridge CB3 0LE, UK.
                Article
                eraa469
                10.1093/jxb/eraa469
                7853602
                33064149
                801cab4a-0f0a-430c-a811-ef9b3f61c3ce
                © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Experimental Biology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 16
                Product
                Funding
                Funded by: BBSRC’s Global Challenge Research;
                Award ID: BB/P028098/1
                Funded by: BBSRC’s Newton Fund Postdoctoral Twinning;
                Award ID: BBS/OS/NW/000011
                Categories
                Research Papers
                Crop Molecular Genetics
                AcademicSubjects/SCI01210

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