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      Ovarian transcriptional response to Wolbachia infection in D. melanogaster in the context of between-genotype variation in gene expression

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          Abstract

          Wolbachia is a maternally transmitted endosymbiotic bacteria that infects a wide variety of arthropod and nematode hosts. The effects of Wolbachia on host biology are far-reaching and include changes in host gene expression. However, previous work on the host transcriptional response has generally been investigated in the context of a single host genotype. Thus, the relative effect of Wolbachia infection versus vs. host genotype on gene expression is unknown. Here, we explicitly test the relative roles of Wolbachia infection and host genotype on host gene expression by comparing the ovarian transcriptomes of 4 strains of Drosophila melanogaster ( D. melanogaster) infected and uninfected with Wolbachia. Our data suggest that infection explains a small amount of transcriptional variation, particularly in comparison to variation in gene expression among strains. However, infection specifically affects genes related to cell cycle, translation, and metabolism. We also find enrichment of cell division and recombination processes among genes with infection-associated differential expression. Broadly, the transcriptomic changes identified in this study provide novel understanding of the relative magnitude of the effect of Wolbachia infection on gene expression in the context of host genetic variation and also point to genes that are consistently differentially expressed in response to infection among multiple genotypes.

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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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              Software for Computing and Annotating Genomic Ranges

              We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                2160-1836
                May 2023
                01 March 2023
                01 March 2023
                : 13
                : 5
                : jkad047
                Affiliations
                Institute of Ecology and Evolution, University of Oregon , Eugene, OR, 97403USA
                Institute of Ecology and Evolution, University of Oregon , Eugene, OR, 97403USA
                Presidential Initiative in Data Science, University of Oregon , Eugene, OR, 97403USA
                Institute of Ecology and Evolution, University of Oregon , Eugene, OR, 97403USA
                Presidential Initiative in Data Science, University of Oregon , Eugene, OR, 97403USA
                Institute of Ecology and Evolution, University of Oregon , Eugene, OR, 97403USA
                Author notes
                Corresponding author: Email: nsingh@ 123456uoregon.edu

                Conflicts of interest The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0003-1615-7590
                https://orcid.org/0000-0002-3496-8074
                https://orcid.org/0000-0001-6506-7001
                Article
                jkad047
                10.1093/g3journal/jkad047
                10151400
                36857313
                b197e9b6-03c8-46b8-adf5-b96e604833e1
                © The Author(s) 2023. Published by Oxford University Press on behalf of the Genetics Society of America.

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

                History
                : 22 August 2022
                : 07 January 2023
                : 28 March 2023
                Page count
                Pages: 11
                Funding
                Funded by: NDS;
                Categories
                Investigation
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140

                Genetics
                wolbachia,transcriptomics,host–microbe interactions,ovary,drosophila melanogaster,wolbachia pipientis

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