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      Transcriptional regulation of dosage compensation in Carica papaya

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          Abstract

          Sex chromosome evolution results in the disparity in gene content between heterogametic sex chromosomes and creates the need for dosage compensation to counteract the effects of gene dose imbalance of sex chromosomes in males and females. It is not known at which stage of sex chromosome evolution dosage compensation would evolve. We used global gene expression profiling in male and female papayas to assess gene expression patterns of sex-linked genes on the papaya sex chromosomes. By analyzing expression ratios of sex-linked genes to autosomal genes and sex-linked genes in males relative to females, our results showed that dosage compensation was regulated on a gene-by-gene level rather than whole sex-linked region in papaya. Seven genes on the papaya X chromosome exhibited dosage compensation. We further compared gene expression ratios in the two evolutionary strata. Y alleles in the older evolutionary stratum showed reduced expression compared to X alleles, while Y alleles in the younger evolutionary stratum showed elevated expression compared to X alleles. Reduced expression of Y alleles in the older evolutionary stratum might be caused by accumulation of deleterious mutations in regulatory regions or transposable element-mediated methylation spreading. Most X-hemizygous genes exhibited either no or very low expression, suggesting that gene silencing might play a role in maintaining transcriptional balance between females and males.

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          Most cited references38

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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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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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              Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

              High-throughput sequencing of messenger RNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate, and flexible software to reduce the raw read data to comprehensible results. HISAT, StringTie, and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample, and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol’s execution time depends on the computing resources, but typically takes under 45 minutes of computer time. Pertea et al. describe a protocol to analyze RNA-seq data using HISAT, StringTie, and Ballgown (the “new Tuxedo” package). The protocol can be used for assembly of transcripts, quantification of gene expression levels and differential expression analysis.
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                Author and article information

                Contributors
                qyu@ag.tamu.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 March 2021
                12 March 2021
                2021
                : 11
                : 5854
                Affiliations
                [1 ]GRID grid.256111.0, ISNI 0000 0004 1760 2876, Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology, , Fujian Agriculture and Forestry University, ; Fuzhou, Fujian Province China
                [2 ]GRID grid.264763.2, ISNI 0000 0001 2112 019X, Texas A&M AgriLife Research Center at Dallas, , Texas A&M University System, ; Dallas, TX USA
                [3 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Department of Plant Biology, , University of Illinois at Urbana-Champaign, ; Urbana, IL USA
                [4 ]GRID grid.264756.4, ISNI 0000 0004 4687 2082, Department of Plant Pathology and Microbiology, , Texas A&M University, ; College Station, TX USA
                Article
                85480
                10.1038/s41598-021-85480-3
                7971000
                33712672
                a74287bd-dcb3-4b82-979f-e4e1b7f31fa1
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 November 2020
                : 22 February 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DBI-1546890
                Award ID: DBI-1546890
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100005825, National Institute of Food and Agriculture;
                Award ID: Hatch Project TEX0-2-9374
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                plant evolution,molecular evolution,genomics,evolution,plant sciences
                Uncategorized
                plant evolution, molecular evolution, genomics, evolution, plant sciences

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