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      A deletion in Eml1 leads to bilateral subcortical heterotopia in the tish rat

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          Abstract

          Children with malformations of cortical development (MCD) are at risk for epilepsy, developmental delays, behavioral disorders, and intellectual disabilities. For a subset of these children, antiseizure medications or epilepsy surgery may result in seizure freedom. However, there are limited options for treating or curing the other conditions, and epilepsy surgery is not an option in all cases of pharmacoresistant epilepsy. Understanding the genetic and neurobiological mechanisms underlying MCD is a necessary step in elucidating novel therapeutic targets. The tish ( telencephalic internal structural heterotopia) rat is a unique model of MCD with spontaneous seizures, but the underlying genetic mutation(s) have remained unknown. DNA and RNA-sequencing revealed that a deletion encompassing a previously unannotated first exon markedly diminished Eml1 transcript and protein abundance in the tish brain. Developmental electrographic characterization of the tish rat revealed early-onset of spontaneous spike-wave discharge (SWD) bursts beginning at postnatal day (P) 17. A dihybrid cross demonstrated that the mutant Eml1 allele segregates with the observed dysplastic cortex and the early-onset SWD bursts in monogenic autosomal recessive frequencies. Our data link the development of the bilateral, heterotopic dysplastic cortex of the tish rat to a deletion in Eml1.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Journal
                9500169
                20475
                Neurobiol Dis
                Neurobiol Dis
                Neurobiology of disease
                0969-9961
                1095-953X
                11 January 2021
                13 March 2020
                July 2020
                19 January 2021
                : 140
                : 104836
                Affiliations
                [a ]Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, United States
                [b ]Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, United States
                [c ]Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA, United States
                [d ]Department of Neuroscience, University of Virginia School of Medicine, Charlottesville, VA, United States
                [e ]Department of Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA, United States
                [f ]Center for Brain Immunology and Glia, University of Virginia School of Medicine, Charlottesville, VA, United States
                [g ]Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, United States
                [h ]Center for Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, United States
                Author notes
                [* ]Correspondence to: M.J. McConnell, Department of Biochemistry and Molecular Genetics, 1340 JPA Pinn Hall Room 6042, Charlottesville, VA 22908, United States. mikemc@ 123456virginia.edu (M.J. McConnell)
                [** ]Correspondence to: H.P. Goodkin, Department of Neurology, P.O. Box 801330, Charlottesville, VA 22908, United States. hpg9v@ 123456virginia.edu (H.P. Goodkin).
                Article
                NIHMS1660689
                10.1016/j.nbd.2020.104836
                7814471
                32179177
                2a20c663-bbaa-4d55-960d-cdffbbf44256

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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                Article

                Neurosciences
                dysplasia,epilepsy,eeg
                Neurosciences
                dysplasia, epilepsy, eeg

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