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      QTL-seq Identifies Pokkali-Derived QTLs and Candidate Genes for Salt Tolerance at Seedling Stage in Rice (Oryza sativa L.)

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          Abstract

          Rice is a staple food crop that plays a pivotal role in global food security, feeding more than half of the world’s population. Soil salinity is one of the most important global problems affecting rice productivity. Salt stress at the seedling stage inhibits root growth, impairs nutrient and water uptake, and affects overall plant vigor, resulting in poor establishment and reduced growth. Therefore, acquiring salt tolerance, especially at the seedling stage, is critical for successful rice production in salinity-affected areas. In this study, 160 RILs derived from a cross between Pokkali and KDML105 were evaluated for their salt tolerance at the seedling stage. QTL-seq analysis with this population identified nine QTLs associated with salt tolerance. Through a comprehensive examination of the effects of coding sequence variants of the 360 annotated genes within the QTLs and gene expression under salt stress, 47 candidate genes were prioritized. In particular, Os01g0200700 (metallothionein-like protein) and Os12g0625000 (O-acetylserine (thiol)lyase) were suggested as potential candidates based on annotated functions and expression data. The results provide valuable insights for improving rice productivity and resistance under salt stress conditions during the critical seedling stage.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

            We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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              Differential expression analysis for sequence count data

              High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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                Author and article information

                Contributors
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                Journal
                ABSGFK
                Agriculture
                Agriculture
                MDPI AG
                2077-0472
                August 2023
                August 12 2023
                : 13
                : 8
                : 1596
                Article
                10.3390/agriculture13081596
                177f92cd-68c3-42e7-a5fb-4937481d6052
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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