28 research outputs found

    Barley sodium content is regulated by natural variants of the Na+ transporter HvHKT1;5

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    During plant growth, sodium (Na+) in the soil is transported via the xylem from the root to the shoot. While excess Na+ is toxic to most plants, non-toxic concentrations have been shown to improve crop yields under certain conditions, such as when soil K+ is low. We quantified grain Na+ across a barley genome-wide association study panel grown under non-saline conditions and identified variants of a Class 1 HIGH-AFFINITY-POTASSIUM-TRANSPORTER (HvHKT1;5)-encoding gene responsible for Na+ content variation under these conditions. A leucine to proline substitution at position 189 (L189P) in HvHKT1;5 disturbs its characteristic plasma membrane localisation and disrupts Na+ transport. Under low and moderate soil Na+, genotypes containing HvHKT1:5P189 accumulate high concentrations of Na+ but exhibit no evidence of toxicity. As the frequency of HvHKT1:5P189 increases significantly in cultivated European germplasm, we cautiously speculate that this non-functional variant may enhance yield potential in non-saline environments, possibly by offsetting limitations of low available K+

    A next-generation liquid xenon observatory for dark matter and neutrino physics

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    The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector

    long-read-tools.org: an interactive catalogue of analysis methods for long-read sequencing data

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    BACKGROUND: The data produced by long-read third-generation sequencers have unique characteristics compared to short-read sequencing data, often requiring tailored analysis tools for tasks ranging from quality control to downstream processing. The rapid growth in software that addresses these challenges for different genomics applications is difficult to keep track of, which makes it hard for users to choose the most appropriate tool for their analysis goal and for developers to identify areas of need and existing solutions to benchmark against. FINDINGS: We describe the implementation of long-read-tools.org, an open-source database that organizes the rapidly expanding collection of long-read data analysis tools and allows its exploration through interactive browsing and filtering. The current database release contains 478 tools across 32 categories. Most tools are developed in Python, and the most frequent analysis tasks include base calling, de novo assembly, error correction, quality checking/filtering, and isoform detection, while long-read single-cell data analysis and transcriptomics are areas with the fewest tools available. CONCLUSION: Continued growth in the application of long-read sequencing in genomics research positions the long-read-tools.org database as an essential resource that allows researchers to keep abreast of both established and emerging software to help guide the selection of the most relevant tool for their analysis needs

    Opportunities and challenges in long-read sequencing data analysis

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    Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain
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