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      Comparative Transcriptome Analysis Revealing the Different Germination Process in Aryloxyphenoxypropionate-Resistant and APP-Susceptible Asia Minor Bluegrass ( Polypogon fugax)

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          Abstract

          Herbicide-resistant mutations are predicted to exhibit fitness cost under herbicide-free conditions. Asia minor bluegrass ( Polypogon fugax) is a common weed species in the winter crops. Our previous study established a P. fugax accession (LR) resistant to aryloxyphenoxypropionate (APP) herbicides, which also exhibited germination delay relative to the susceptible accession (LS). A comparative transcriptome was conducted to analyze the gene expression profile of LS and LR at two germination time points. A total of 11,856 and 23,123 differentially expressed genes (DEGs) were respectively identified in LS and LR. Most DEGs were involved in lipid metabolism, carbohydrate metabolism, amino acid metabolism, and secondary metabolites biosynthesis. Twenty-four genes involved in carbohydrate and fatty acid metabolism had higher relative expression levels in LS than LR during germination. Nine genes involved in gibberellin (GA) and abscisic acid (ABA) signal transduction showed different expression patterns in LS and LR, consistent with their different sensitivity to exogenous hormones treatments. This study first provided insight into transcriptional changes and interaction in the seed germination process of P. fugax. It compared the differential expression profile between APP herbicides resistance and susceptible accessions during germination, which contributed to understanding the association between herbicide resistance and fitness cost.

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          Most cited references 53

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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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            A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding

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              Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

              Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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                Author and article information

                Journal
                Plants (Basel)
                Plants (Basel)
                plants
                Plants
                MDPI
                2223-7747
                12 September 2020
                September 2020
                : 9
                : 9
                Affiliations
                State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 311400, China; yuxiaoyue@ 123456caas.cn (X.Y.); tangwei@ 123456caas.cn (W.T.); yangyongjie@ 123456caas.cn (Y.Y.); zhangjieping@ 123456caas.cn (J.Z.)
                Author notes
                [* ]Correspondence: luyongliang@ 123456caas.cn ; Tel.: +86-0571-63370333
                Article
                plants-09-01191
                10.3390/plants9091191
                7569813
                32932586
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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