Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

Somatic uniparental disomy of Chromosome 16p in hemimegalencephaly

Read this article at

ScienceOpenPublisherPMC
Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Abstract

      Hemimegalencephaly (HME) is a heterogeneous cortical malformation characterized by enlargement of one cerebral hemisphere. Somatic variants in mammalian target of rapamycin (mTOR) regulatory genes have been implicated in some HME cases; however, ∼70% have no identified genetic etiology. Here, we screened two HME patients to identify disease-causing somatic variants. DNA from leukocytes, buccal swabs, and surgically resected brain tissue from two HME patients were screened for somatic variants using genome-wide genotyping arrays or sequencing of the protein-coding regions of the genome. Functional studies were performed to evaluate the molecular consequences of candidate disease-causing variants. Both HME patients evaluated were found to have likely disease-causing variants in DNA extracted from brain tissue but not in buccal swab or leukocyte DNA, consistent with a somatic mutational mechanism. In the first case, a previously identified disease-causing somatic single nucleotide in MTOR was identified. In the second case, we detected an overrepresentation of the alleles inherited from the mother on Chromosome 16 in brain tissue DNA only, indicative of somatic uniparental disomy (UPD) of the p-arm of Chromosome 16. Using methylation analyses, an imprinted locus on 16p spanning ZNF597 was identified, which results in increased expression of ZNF597 mRNA and protein in the brain tissue of the second case. Enhanced mTOR signaling was observed in tissue specimens from both patients. We speculate that overexpression of maternally expressed ZNF597 led to aberrant hemispheric development in the patient with somatic UPD of Chromosome 16p possibly through modulation of mTOR signaling.

      Related collections

      Most cited references 34

      • Record: found
      • Abstract: found
      • Article: found

      TopHat: discovering splice junctions with RNA-Seq

      Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: not found

        Integrative Genomics Viewer

        To the Editor Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole genome sequencing, epigenetic surveys, expression profiling of coding and non-coding RNAs, SNP and copy number profiling, and functional assays. Analysis of these large, diverse datasets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large datasets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data poses a significant challenge to the development of such tools. To address this challenge we developed the Integrative Genomics Viewer (IGV), a lightweight visualization tool that enables intuitive real-time exploration of diverse, large-scale genomic datasets on standard desktop computers. It supports flexible integration of a wide range of genomic data types including aligned sequence reads, mutations, copy number, RNAi screens, gene expression, methylation, and genomic annotations (Figure S1). The IGV makes use of efficient, multi-resolution file formats to enable real-time exploration of arbitrarily large datasets over all resolution scales, while consuming minimal resources on the client computer (see Supplementary Text). Navigation through a dataset is similar to Google Maps, allowing the user to zoom and pan seamlessly across the genome at any level of detail from whole-genome to base pair (Figure S2). Datasets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic datasets alongside publicly available data from, for example, The Cancer Genome Atlas (TCGA) 1 , 1000 Genomes (www.1000genomes.org/), and ENCODE 2 (www.genome.gov/10005107) projects. In addition, IGV allows collaborators to load and share data locally or remotely over the Web. IGV supports concurrent visualization of diverse data types across hundreds, and up to thousands of samples, and correlation of these integrated datasets with clinical and phenotypic variables. A researcher can define arbitrary sample annotations and associate them with data tracks using a simple tab-delimited file format (see Supplementary Text). These might include, for example, sample identifier (used to link different types of data for the same patient or tissue sample), phenotype, outcome, cluster membership, or any other clinical or experimental label. Annotations are displayed as a heatmap but more importantly are used for grouping, sorting, filtering, and overlaying diverse data types to yield a comprehensive picture of the integrated dataset. This is illustrated in Figure 1, a view of copy number, expression, mutation, and clinical data from 202 glioblastoma samples from the TCGA project in a 3 kb region around the EGFR locus 1, 3 . The investigator first grouped samples by tumor subtype, then by data type (copy number and expression), and finally sorted them by median copy number over the EGFR locus. A shared sample identifier links the copy number and expression tracks, maintaining their relative sort order within the subtypes. Mutation data is overlaid on corresponding copy number and expression tracks, based on shared participant identifier annotations. Several trends in the data stand out, such as a strong correlation between copy number and expression and an overrepresentation of EGFR amplified samples in the Classical subtype. IGV’s scalable architecture makes it well suited for genome-wide exploration of next-generation sequencing (NGS) datasets, including both basic aligned read data as well as derived results, such as read coverage. NGS datasets can approach terabytes in size, so careful management of data is necessary to conserve compute resources and to prevent information overload. IGV varies the displayed level of detail according to resolution scale. At very wide views, such as the whole genome, IGV represents NGS data by a simple coverage plot. Coverage data is often useful for assessing overall quality and diagnosing technical issues in sequencing runs (Figure S3), as well as analysis of ChIP-Seq 4 and RNA-Seq 5 experiments (Figures S4 and S5). As the user zooms below the ~50 kb range, individual aligned reads become visible (Figure 2) and putative SNPs are highlighted as allele counts in the coverage plot. Alignment details for each read are available in popup windows (Figures S6 and S7). Zooming further, individual base mismatches become visible, highlighted by color and intensity according to base call and quality. At this level, the investigator may sort reads by base, quality, strand, sample and other attributes to assess the evidence of a variant. This type of visual inspection can be an efficient and powerful tool for variant call validation, eliminating many false positives and aiding in confirmation of true findings (Figures S6 and S7). Many sequencing protocols produce reads from both ends (“paired ends”) of genomic fragments of known size distribution. IGV uses this information to color-code paired ends if their insert sizes are larger than expected, fall on different chromosomes, or have unexpected pair orientations. Such pairs, when consistent across multiple reads, can be indicative of a genomic rearrangement. When coloring aberrant paired ends, each chromosome is assigned a unique color, so that intra- (same color) and inter- (different color) chromosomal events are readily distinguished (Figures 2 and S8). We note that misalignments, particularly in repeat regions, can also yield unexpected insert sizes, and can be diagnosed with the IGV (Figure S9). There are a number of stand-alone, desktop genome browsers available today 6 including Artemis 7 , EagleView 8 , MapView 9 , Tablet 10 , Savant 11 , Apollo 12 , and the Integrated Genome Browser 13 . Many of them have features that overlap with IGV, particularly for NGS sequence alignment and genome annotation viewing. The Integrated Genome Browser also supports viewing array-based data. See Supplementary Table 1 and Supplementary Text for more detail. IGV focuses on the emerging integrative nature of genomic studies, placing equal emphasis on array-based platforms, such as expression and copy-number arrays, next-generation sequencing, as well as clinical and other sample metadata. Indeed, an important and unique feature of IGV is the ability to view all these different data types together and to use the sample metadata to dynamically group, sort, and filter datasets (Figure 1 above). Another important characteristic of IGV is fast data loading and real-time pan and zoom – at all scales of genome resolution and all dataset sizes, including datasets comprising hundreds of samples. Finally, we have placed great emphasis on the ease of installation and use of IGV, with the goal of making both the viewing and sharing of their data accessible to non-informatics end users. IGV is open source software and freely available at http://www.broadinstitute.org/igv/, including full documentation on use of the software. Supplementary Material 1
          Bookmark
          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

          Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.
            Bookmark

            Author and article information

            Affiliations
            [1 ]Institute for Genomic Medicine, Columbia University, New York, New York 10032, USA;
            [2 ]Duke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina 27710, USA;
            [3 ]Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina 27710, USA;
            [4 ]Department of Pathology, Duke University School of Medicine, Durham, North Carolina 27710, USA;
            [5 ]Department of Neurosurgery, Stanford University School of Medicine, Stanford, California 94305, USA;
            [6 ]Division of Pediatric Neurology, Duke University Medical Center, Durham, North Carolina 27710, USA;
            [7 ]Department of Neurobiology, Duke University, Durham, North Carolina 27708, USA;
            [8 ]Duke University Health System, Durham, North Carolina 27710, USA;
            [9 ]Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27710, USA;
            [10 ]Department of Neurology, University of Maryland, School of Medicine, Baltimore, Maryland 21201, USA;
            [11 ]Department of Pathology and Cell Biology, Columbia University, New York, New York 10032, USA
            Author notes
            Corresponding author: eh2682@ 123456cumc.columbia.edu
            Journal
            Cold Spring Harb Mol Case Stud
            Cold Spring Harb Mol Case Stud
            cshmcs
            cshmcs
            cshmcs
            Cold Spring Harbor Molecular Case Studies
            Cold Spring Harbor Laboratory Press
            2373-2873
            September 2017
            : 3
            : 5
            28864461
            5593155
            10.1101/mcs.a001735
            GriffinMCS001735

            This article is distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits reuse and redistribution, except for commercial purposes, provided that the original author and source are credited.

            Counts
            Pages: 11
            Product
            Funding
            Funded by: The Ellison Medical Foundation New Scholar award
            Award ID: AG-NS-0441-08
            Funded by: National Institute of Mental Health , open-funder-registry 10.13039/100000025;
            Award ID: K01MH098126
            Award ID: R01MH099216
            Award ID: R01MH097993
            Funded by: National Institute of Allergy and Infectious Diseases , open-funder-registry 10.13039/100000060;
            Award ID: 1R56AI098588-01A1
            Funded by: National Human Genome Research Institute , open-funder-registry 10.13039/100000051;
            Award ID: U01HG007672
            Funded by: National Institute of Neurological Disorders and Stroke , open-funder-registry 10.13039/100000065;
            Award ID: U01-NS077303
            Award ID: U01-NS053998
            Funded by: National Institute of Allergy and Infectious Diseases Center
            Award ID: U19-AI067854
            Award ID: UM1-AI100645
            Funded by: NINDS , open-funder-registry 10.13039/100000065;
            Award ID: R21NS078657
            Award ID: R01NS094596
            Award ID: R01NS082343-01
            Funded by: Citizens United for Research in Epilepsy , open-funder-registry 10.13039/100002736;
            Categories
            Research Report

            pachygyria, hemimegalencephaly

            Comments

            Comment on this article