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      The multi-omic landscape of sex chromosome abnormalities: current status and future directions

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          Abstract

          Sex chromosome abnormalities (SCAs) are chromosomal disorders with either a complete or partial loss or gain of sex chromosomes. The most frequent SCAs include Turner syndrome (45,X), Klinefelter syndrome (47,XXY), Trisomy X syndrome (47,XXX), and Double Y syndrome (47,XYY). The phenotype seen in SCAs is highly variable and may not merely be due to the direct genomic imbalance from altered sex chromosome gene dosage but also due to additive alterations in gene networks and regulatory pathways across the genome as well as individual genetic modifiers. This review summarizes the current insight into the genomics of SCAs. In addition, future directions of research that can contribute to decipher the genomics of SCA are discussed such as single-cell omics, spatial transcriptomics, system biology thinking, human-induced pluripotent stem cells, and animal models, and how these data may be combined to bridge the gap between genomics and the clinical phenotype.

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          Most cited references74

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              The zebrafish reference genome sequence and its relationship to the human genome.

              Zebrafish have become a popular organism for the study of vertebrate gene function. The virtually transparent embryos of this species, and the ability to accelerate genetic studies by gene knockdown or overexpression, have led to the widespread use of zebrafish in the detailed investigation of vertebrate gene function and increasingly, the study of human genetic disease. However, for effective modelling of human genetic disease it is important to understand the extent to which zebrafish genes and gene structures are related to orthologous human genes. To examine this, we generated a high-quality sequence assembly of the zebrafish genome, made up of an overlapping set of completely sequenced large-insert clones that were ordered and oriented using a high-resolution high-density meiotic map. Detailed automatic and manual annotation provides evidence of more than 26,000 protein-coding genes, the largest gene set of any vertebrate so far sequenced. Comparison to the human reference genome shows that approximately 70% of human genes have at least one obvious zebrafish orthologue. In addition, the high quality of this genome assembly provides a clearer understanding of key genomic features such as a unique repeat content, a scarcity of pseudogenes, an enrichment of zebrafish-specific genes on chromosome 4 and chromosomal regions that influence sex determination.

                Author and article information

                Journal
                Endocr Connect
                Endocr Connect
                EC
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                2049-3614
                30 June 2023
                03 July 2023
                01 September 2023
                : 12
                : 9
                : e230011
                Affiliations
                [1 ]Department of Molecular Medicine , Aarhus University Hospital, Aarhus, Denmark
                [2 ]Department of Molecular Biology and Genetics , Aarhus University, Aarhus, Denmark
                [3 ]Department of Clinical Medicine , Aarhus University Hospital, Aarhus, Denmark
                [4 ]Department of Gynaecology and Obstetrics , Aarhus University Hospital, Aarhus, Denmark
                [5 ]Department of Endocrinology , Aarhus University Hospital, Aarhus, Denmark
                [6 ]Department of Clinical Genetics , Aarhus University Hospital, Aarhus, Denmark
                Author notes
                Correspondence should be addressed to A Skakkebæk: asj@ 123456clin.au.dk
                Author information
                http://orcid.org/0000-0002-2968-283X
                http://orcid.org/0000-0002-1017-7702
                http://orcid.org/0000-0002-3825-0000
                http://orcid.org/0000-0001-6574-4893
                http://orcid.org/0000-0001-5924-1720
                http://orcid.org/0000-0001-9178-4901
                Article
                EC-23-0011
                10.1530/EC-23-0011
                10448593
                37399516
                91405947-a1a5-475f-a550-1020f0bf3e7f
                © the author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 06 January 2023
                : 30 June 2023
                Categories
                Review

                sex chromosome abnormalities,turner syndrome,klinefelter syndrome,genetics

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