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Sökning: WFRF:(Murphy Shawn N)

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1.
  • Fresard, Laure, et al. (författare)
  • Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts
  • 2019
  • Ingår i: Nature Medicine. - : NATURE PUBLISHING GROUP. - 1078-8956 .- 1546-170X. ; 25:6, s. 911-919
  • Tidskriftsartikel (refereegranskat)abstract
    • It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene(1). The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches(2-5). For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases(6-8). This includes muscle biopsies from patients with undiagnosed rare muscle disorders(6,9), and cultured fibroblasts from patients with mitochondrial disorders(7). However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.
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2.
  • Ali, Ahmed, et al. (författare)
  • Single cell metabolism : current and future trends
  • 2022
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 18:10
  • Forskningsöversikt (refereegranskat)abstract
    • Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
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