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      Differential Defense Responses of Upland and Lowland Switchgrass Cultivars to a Cereal Aphid Pest

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          Abstract

          Yellow sugarcane aphid (YSA) ( Sipha flava, Forbes) is a damaging pest on many grasses. Switchgrass ( Panicum virgatum L.), a perennial C4 grass, has been selected as a bioenergy feedstock because of its perceived resilience to abiotic and biotic stresses. Aphid infestation on switchgrass has the potential to reduce the yields and biomass quantity. Here, the global defense response of switchgrass cultivars Summer and Kanlow to YSA feeding was analyzed by RNA-seq and metabolite analysis at 5, 10, and 15 days after infestation. Genes upregulated by infestation were more common in both cultivars compared to downregulated genes. In total, a higher number of differentially expressed genes (DEGs) were found in the YSA susceptible cultivar (Summer), and fewer DEGs were observed in the YSA resistant cultivar (Kanlow). Interestingly, no downregulated genes were found in common between each time point or between the two switchgrass cultivars. Gene co-expression analysis revealed upregulated genes in Kanlow were associated with functions such as flavonoid, oxidation-response to chemical, or wax composition. Downregulated genes for the cultivar Summer were found in co-expression modules with gene functions related to plant defense mechanisms or cell wall composition. Global analysis of defense networks of the two cultivars uncovered differential mechanisms associated with resistance or susceptibility of switchgrass in response to YSA infestation. Several gene co-expression modules and transcription factors correlated with these differential defense responses. Overall, the YSA-resistant Kanlow plants have an enhanced defense even under aphid uninfested conditions.

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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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                27 October 2020
                November 2020
                : 21
                : 21
                : 7966
                Affiliations
                [1 ]Department of Entomology, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; lise.pingault@ 123456unl.edu (L.P.); kyle.koch@ 123456unl.edu (K.G.K.); thengmoss2@ 123456unl.edu (T.H.-M.); jbradshaw2@ 123456unl.edu (J.D.B.)
                [2 ]Wheat, Sorghum, and Forage Research Unit, USDA-ARS, Lincoln, NE 68583, USA; nathan.palmer@ 123456usda.gov
                [3 ]Redox Biology Center, Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA; jseravalli1@ 123456unl.edu
                [4 ]Biology Department, University of Nebraska-Kearney, Kearney, NE 68849, USA; twiggp@ 123456unk.edu
                [5 ]Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
                Author notes
                [* ]Correspondence: joelouis@ 123456unl.edu (J.L.); Gautam.Sarath@ 123456usda.gov (G.S.); Tel.: +1-402-472-8098 (J.L.); +1-402-472-4204 (G.S.)
                [†]

                Authors contributed equally to this study.

                Author information
                https://orcid.org/0000-0001-7137-8797
                Article
                ijms-21-07966
                10.3390/ijms21217966
                7672581
                33120946
                cce4a84a-4d43-4ddf-89d8-055494851085
                © 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/).

                History
                : 29 August 2020
                : 24 October 2020
                Categories
                Article

                Molecular biology
                switchgrass,panicum virgatum,yellow sugarcane aphid,sipha flava,rna-seq,transcriptome,gene network,metabolites

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