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      Disentangling the relationship between sex-biased gene expression and X-linkage

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          Abstract

          X chromosomes are preferentially transmitted through females, which may favor the accumulation of X-linked alleles/genes with female-beneficial effects. Numerous studies have shown that genes with sex-biased expression are under- or over-represented on the X chromosomes of a wide variety of organisms. The patterns, however, vary between different animal species, and the causes of these differences are unresolved. Additionally, genes with sex-biased expression tend to be narrowly expressed in a limited number of tissues, and narrowly expressed genes are also non-randomly X-linked in a taxon-specific manner. It is therefore unclear whether the unique gene content of the X chromosome is the result of selection on genes with sex-biased expression, narrowly expressed genes, or some combination of the two. To address this problem, we measured sex-biased expression in multiple Drosophila species and at different developmental time points. These data were combined with available expression measurements from Drosophila melanogaster and mouse to reconcile the inconsistencies in X-chromosome content among taxa. Our results suggest that most of the differences between Drosophila and mammals are confounded by disparate data collection/analysis approaches as well as the correlation between sex bias and expression breadth. Both the Drosophila and mouse X chromosomes harbor an excess of genes with female-biased expression after controlling for the confounding factors, suggesting that the asymmetrical transmission of the X chromosome favors the accumulation of female-beneficial mutations in X-linked genes. However, some taxon-specific patterns remain, and we provide evidence that these are in part a consequence of constraints imposed by the dosage compensation mechanism in Drosophila.

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          Most cited references68

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Journal
                Genome Res
                Genome Res
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                July 2012
                : 22
                : 7
                : 1255-1265
                Affiliations
                [1 ]Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA;
                [2 ]Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA
                Author notes
                [3]

                Present address: Department of Biological Science, Florida State University, Tallahassee, FL 32306, USA.

                [4]

                These authors contributed equally to this work.

                [5 ]Corresponding author E-mail meisel@ 123456cornell.edu
                Article
                9518021
                10.1101/gr.132100.111
                3396367
                22499666
                2a825c14-5134-4a14-a51e-37d471a3fd7e
                © 2012, Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/.

                History
                : 16 September 2011
                : 6 April 2012
                Categories
                Research

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