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      DUX4 double whammy: The transcription factor that causes a rare muscular dystrophy also kills the precursors of the human nose

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          Abstract

          SMCHD1 mutations cause congenital arhinia (absent nose) and a muscular dystrophy called FSHD2. In FSHD2, loss of SMCHD1 repressive activity causes expression of double homeobox 4 (DUX4) in muscle tissue, where it is toxic. Studies of arhinia patients suggest a primary defect in nasal placode cells (human nose progenitors). Here, we show that upon SMCHD1 ablation, DUX4 becomes derepressed in H9 human embryonic stem cells (hESCs) as they differentiate toward a placode cell fate, triggering cell death. Arhinia and FSHD2 patient-derived induced pluripotent stem cells (iPSCs) express DUX4 when converted to placode cells and demonstrate variable degrees of cell death, suggesting an environmental disease modifier. HSV-1 may be one such modifier as herpesvirus infection amplifies DUX4 expression in SMCHD1 KO hESC and patient iPSC. These studies suggest that arhinia, like FSHD2, is due to compromised SMCHD1 repressive activity in a cell-specific context and provide evidence for an environmental modifier.

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          Abstract

          Loss of epigenetic silencing by the chromatin remodeler SMCHD1 unleashes the DUX4 muscle toxin which kills cranial placode cells.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing - review & editing
                Role: MethodologyRole: Validation
                Role: InvestigationRole: Visualization
                Role: InvestigationRole: Writing - review & editing
                Role: InvestigationRole: Writing - review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Validation
                Role: InvestigationRole: MethodologyRole: Resources
                Role: Formal analysisRole: Writing - review & editing
                Role: Data curationRole: Formal analysis
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Journal
                Sci Adv
                Sci Adv
                sciadv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                February 2023
                17 February 2023
                : 9
                : 7
                : eabq7744
                Affiliations
                [ 1 ]Pediatric Neuroendocrinology Group, Clinical Research Branch, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA.
                [ 2 ]Integrative Bioinformatics, NIEHS, Research Triangle Park, NC, USA.
                [ 3 ]Signal Transduction Laboratory, NIEHS, Research Triangle Park, NC, USA.
                [ 4 ]Department of Pathology, University of Iowa Carver College of Medicine and Senator Paul D. Wellstone Muscular Dystrophy Specialized Research Center, Iowa City, IA, USA.
                [ 5 ]University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
                [ 6 ]Viral Vector Core, NIEHS, Research Triangle Park, NC, USA.
                Author notes
                [* ]Corresponding author. Email: natalie.shaw@ 123456nih.gov
                Author information
                https://orcid.org/0000-0002-3615-6192
                https://orcid.org/0000-0003-4102-807X
                https://orcid.org/0000-0001-9504-1611
                https://orcid.org/0000-0002-5444-6628
                https://orcid.org/0000-0003-3166-8989
                https://orcid.org/0000-0002-6487-081X
                https://orcid.org/0000-0002-0847-9170
                Article
                abq7744
                10.1126/sciadv.abq7744
                9937577
                36800423
                d6448817-4ae3-44fe-8ae6-1e5f77a08f23
                Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 29 April 2022
                : 12 January 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: ZIAES103327-05
                Categories
                Research Article
                Biomedicine and Life Sciences
                SciAdv r-articles
                Molecular Biology
                Custom metadata
                Karla Peñamante

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