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      Genome‐wide host RNA signatures of infectious diseases: discovery and clinical translation

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          Summary

          The use of whole blood gene expression to derive diagnostic biomarkers capable of distinguishing between phenotypically similar diseases holds great promise but remains a challenge. Differential gene expression analysis is used to identify the key genes that undergo changes in expression relative to healthy individuals, as well as to patients with other diseases. These key genes can act as diagnostic, prognostic and predictive markers of disease. Gene expression ‘signatures’ in the blood hold the potential to be used for the diagnosis of infectious diseases, where current diagnostics are unreliable, ineffective or of limited potential. For diagnostic tests based on RNA signatures to be useful clinically, the first step is to identify the minimum set of gene transcripts that accurately identify the disease in question. The second requirement is rapid and cost‐effective detection of the gene expression levels. Signatures have been described for a number of infectious diseases, but ‘clinic‐ready’ technologies for RNA detection from clinical samples are limited, though existing methods such as RT‐PCR are likely to be superseded by a number of emerging technologies, which may form the basis of the translation of gene expression signatures into routine diagnostic tests for a range of disease states.

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          Most cited references51

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          Direct multiplexed measurement of gene expression with color-coded probe pairs.

          We describe a technology, the NanoString nCounter gene expression system, which captures and counts individual mRNA transcripts. Advantages over existing platforms include direct measurement of mRNA expression levels without enzymatic reactions or bias, sensitivity coupled with high multiplex capability, and digital readout. Experiments performed on 509 human genes yielded a replicate correlation coefficient of 0.999, a detection limit between 0.1 fM and 0.5 fM, and a linear dynamic range of over 500-fold. Comparison of the NanoString nCounter gene expression system with microarrays and TaqMan PCR demonstrated that the nCounter system is more sensitive than microarrays and similar in sensitivity to real-time PCR. Finally, a comparison of transcript levels for 21 genes across seven samples measured by the nCounter system and SYBR Green real-time PCR demonstrated similar patterns of gene expression at all transcript levels.
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            The potential and challenges of nanopore sequencing.

            A nanopore-based device provides single-molecule detection and analytical capabilities that are achieved by electrophoretically driving molecules in solution through a nano-scale pore. The nanopore provides a highly confined space within which single nucleic acid polymers can be analyzed at high throughput by one of a variety of means, and the perfect processivity that can be enforced in a narrow pore ensures that the native order of the nucleobases in a polynucleotide is reflected in the sequence of signals that is detected. Kilobase length polymers (single-stranded genomic DNA or RNA) or small molecules (e.g., nucleosides) can be identified and characterized without amplification or labeling, a unique analytical capability that makes inexpensive, rapid DNA sequencing a possibility. Further research and development to overcome current challenges to nanopore identification of each successive nucleotide in a DNA strand offers the prospect of 'third generation' instruments that will sequence a diploid mammalian genome for approximately $1,000 in approximately 24 h.
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              Computational methods for transcriptome annotation and quantification using RNA-seq.

              High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications.
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                Author and article information

                Contributors
                m.kaforou@imperial.ac.uk
                Journal
                Immunology
                Immunology
                10.1111/(ISSN)1365-2567
                IMM
                Immunology
                John Wiley and Sons Inc. (Hoboken )
                0019-2805
                1365-2567
                24 October 2017
                February 2018
                24 October 2017
                : 153
                : 2 ( doiID: 10.1111/imm.2018.153.issue-2 )
                : 171-178
                Affiliations
                [ 1 ] London Centre for Nanotechnology University College London London UK
                [ 2 ] Department of Medicine Imperial College London London UK
                Author notes
                [*] [* ] Correspondence: Myrsini Kaforou, Division of Infectious Diseases, Department of Medicine, Imperial College London, Norfolk Place, London W2 1PG, UK. Email: m.kaforou@ 123456imperial.ac.uk

                Senior author: Myrsini Kaforou

                Author information
                http://orcid.org/0000-0002-9623-4252
                http://orcid.org/0000-0001-6941-6491
                http://orcid.org/0000-0003-2767-6919
                http://orcid.org/0000-0001-9878-4007
                Article
                IMM12841
                10.1111/imm.12841
                5765383
                28921535
                348e15ef-1898-4c3e-b8cd-63d79b71ac4e
                © 2017 The Authors. Immunology Published by John Wiley & Sons Ltd

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 15 May 2017
                : 11 September 2017
                : 11 September 2017
                Page count
                Figures: 1, Tables: 0, Pages: 8, Words: 6977
                Categories
                Review Article
                Review Articles
                Custom metadata
                2.0
                imm12841
                February 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.8 mode:remove_FC converted:12.01.2018

                Immunology
                bacterial,bioinformatics,infection,transcriptomics,viral
                Immunology
                bacterial, bioinformatics, infection, transcriptomics, viral

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