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      X Chromosome and Autosome Dosage Responses in Drosophila melanogaster Heads

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          Abstract

          X chromosome dosage compensation is required for male viability in Drosophila. Dosage compensation relative to autosomes is two-fold, but this is likely to be due to a combination of homeostatic gene-by-gene regulation and chromosome-wide regulation. We have baseline values for gene-by-gene dosage compensation on autosomes, but not for the X chromosome. Given the evolutionary history of sex chromosomes, these baseline values could differ. We used a series of deficiencies on the X and autosomes, along with mutations in the sex-determination gene transformer-2, to carefully measure the sex-independent X-chromosome response to gene dosage in adult heads by RNA sequencing. We observed modest and indistinguishable dosage compensation for both X chromosome and autosome genes, suggesting that the X chromosome is neither inherently more robust nor sensitive to dosage change.

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          Most cited references 37

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          Synthetic spike-in standards for RNA-seq experiments.

          High-throughput sequencing of cDNA (RNA-seq) is a widely deployed transcriptome profiling and annotation technique, but questions about the performance of different protocols and platforms remain. We used a newly developed pool of 96 synthetic RNAs with various lengths, and GC content covering a 2(20) concentration range as spike-in controls to measure sensitivity, accuracy, and biases in RNA-seq experiments as well as to derive standard curves for quantifying the abundance of transcripts. We observed linearity between read density and RNA input over the entire detection range and excellent agreement between replicates, but we observed significantly larger imprecision than expected under pure Poisson sampling errors. We use the control RNAs to directly measure reproducible protocol-dependent biases due to GC content and transcript length as well as stereotypic heterogeneity in coverage across transcripts correlated with position relative to RNA termini and priming sequence bias. These effects lead to biased quantification for short transcripts and individual exons, which is a serious problem for measurements of isoform abundances, but that can partially be corrected using appropriate models of bias. By using the control RNAs, we derive limits for the discovery and detection of rare transcripts in RNA-seq experiments. By using data collected as part of the model organism and human Encyclopedia of DNA Elements projects (ENCODE and modENCODE), we demonstrate that external RNA controls are a useful resource for evaluating sensitivity and accuracy of RNA-seq experiments for transcriptome discovery and quantification. These quality metrics facilitate comparable analysis across different samples, protocols, and platforms.
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            Paucity of genes on the Drosophila X chromosome showing male-biased expression.

            Sex chromosomes are primary determinants of sexual dimorphism in many organisms. These chromosomes are thought to arise via the divergence of an ancestral autosome pair and are almost certainly influenced by differing selection in males and females. Exploring how sex chromosomes differ from autosomes is highly amenable to genomic analysis. We examined global gene expression in Drosophila melanogaster and report a dramatic underrepresentation of X-chromosome genes showing high relative expression in males. Using comparative genomics, we find that these same X-chromosome genes are exceptionally poorly conserved in the mosquito Anopheles gambiae. These data indicate that the X chromosome is a disfavored location for genes selectively expressed in males.
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              Is Open Access

              Microarrays, deep sequencing and the true measure of the transcriptome

              Microarrays first made the analysis of the transcriptome possible, and have produced much important information. Today, however, researchers are increasingly turning to direct high-throughput sequencing - RNA-Seq - which has considerable advantages for examining transcriptome fine structure - for example in the detection of allele-specific expression and splice junctions. In this article, we discuss the relative merits of the two techniques, the inherent biases in each, and whether all of the vast body of array work needs to be revisited using the newer technology. We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                7 April 2015
                June 2015
                : 5
                : 6
                : 1057-1063
                Affiliations
                Section of Developmental Genomics, Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892
                Author notes
                [1 ]Corresponding author: Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 50 South Drive, Bethesda, MD 20892. E-mail: briano@ 123456helix.nih.gov
                Article
                GGG_017632
                10.1534/g3.115.017632
                4478536
                25850426
                Copyright © 2015 Chen and Oliver

                This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 7
                Product
                Categories
                Genetics of Sex

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