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      A novel variant in TAF1 affects gene expression and is associated with X-linked TAF1 intellectual disability syndrome

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          We investigated the genome of a 5-year-old male who presented with global developmental delay (motor, cognitive, and speech), hypotonia, possibly ataxia, and cerebellar hypoplasia of unknown origin. Whole genome sequencing (WGS) and mRNA sequencing (RNA-seq) were performed on a family having an affected proband, his unaffected parents, and maternal grandfather. To explore the molecular and functional consequences of the variant, we performed cell proliferation assays, quantitative real-time PCR (qRT-PCR) array, immunoblotting, calcium imaging, and neurite outgrowth experiments in SH-SY5Y neuroblastoma cells to compare the properties of the wild-type TATA-box-binding protein factor 1 ( TAF1), deletion of TAF1, and TAF1 variant p.Ser1600Gly samples. The whole genome data identified several gene variants. However, the genome sequence data failed to implicate a candidate gene as many of the variants were of unknown significance. By combining genome sequence data with transcriptomic data, a probable candidate variant, p.Ser1600Gly, emerged in TAF1. Moreover, the RNA-seq data revealed a 90:10 extremely skewed X-chromosome inactivation (XCI) in the mother. Our results showed that neuronal ion channel genes were differentially expressed between TAF1 deletion and TAF1 variant p.Ser1600Gly cells, when compared with their respective controls, and that the TAF1 variant may impair neuronal differentiation and cell proliferation. Taken together, our data suggest that this novel variant in TAF1 plays a key role in the development of a recently described X-linked syndrome, TAF1 intellectual disability syndrome, and further extends our knowledge of a potential link between TAF1 deficiency and defects in neuronal cell function.

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          Most cited references 43

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          TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

          TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at
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            Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

            Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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              RNA-Seq: a revolutionary tool for transcriptomics.

              RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.

                Author and article information

                Neuronal Signal.
                Neuronal Signaling
                Neuronal Signal.
                Portland Press Ltd.
                25 June 2018
                16 July 2018
                28 September 2018
                : 2
                : 3
                [1 ]Department of Pathology, University of Arizona College of Medicine, Tucson, AZ 85724, U.S.A.
                [2 ]Department of Pharmacology, University of Arizona College of Medicine, Tucson, AZ 85724, U.S.A.
                [3 ]Department of Cellular and Molecular Medicine, University of Arizona College of Medicine, Tucson, AZ 85724, U.S.A.
                [4 ]Department of Pediatrics, University of Arizona College of Medicine, Tucson, AZ 85724, U.S.A.
                [5 ]Center for Rare Childhood Disorders, The Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, U.S.A.
                [6 ]Department of Family and Community Medicine, University of Arizona College of Medicine, Tucson, AZ 85724, U.S.A.
                [7 ]Arizona Research Laboratories Division of Biotechnology, University of Arizona, Tucson, AZ 85721, U.S.A.
                [8 ]Department of Pharmacology, University of Washington, School of Medicine Seattle, WA 98195, U.S.A.
                Author notes
                Correspondence: Mark A. Nelson ( mnelson@ )
                © 2018 The Author(s).

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

                Pages: 17
                Self URI (journal page):
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                Research Article


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