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      Transcriptome Analysis Based on RNA-Seq in Understanding Pathogenic Mechanisms of Diseases and the Immune System of Fish: A Comprehensive Review

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          Abstract

          In recent years, with the advent of next-generation sequencing along with the development of various bioinformatics tools, RNA sequencing (RNA-Seq)-based transcriptome analysis has become much more affordable in the field of biological research. This technique has even opened up avenues to explore the transcriptome of non-model organisms for which a reference genome is not available. This has made fish health researchers march towards this technology to understand pathogenic processes and immune reactions in fish during the event of infection. Recent studies using this technology have altered and updated the previous understanding of many diseases in fish. RNA-Seq has been employed in the understanding of fish pathogens like bacteria, virus, parasites, and oomycetes. Also, it has been helpful in unraveling the immune mechanisms in fish. Additionally, RNA-Seq technology has made its way for future works, such as genetic linkage mapping, quantitative trait analysis, disease-resistant strain or broodstock selection, and the development of effective vaccines and therapies. Until now, there are no reviews that comprehensively summarize the studies which made use of RNA-Seq to explore the mechanisms of infection of pathogens and the defense strategies of fish hosts. This review aims to summarize the contemporary understanding and findings with regard to infectious pathogens and the immune system of fish that have been achieved through RNA-Seq technology.

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          Most cited references139

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          Improving RNA-Seq expression estimates by correcting for fragment bias

          The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels, and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies.
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            Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

            Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.
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              A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications

              RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology—the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                15 January 2018
                January 2018
                : 19
                : 1
                : 245
                Affiliations
                [1 ]Clinical Division of Fish Medicine, University of Veterinary Medicine, Vienna 1210, Austria; Arun.Sudhagar@ 123456vetmeduni.ac.at (A.S.); Gokhlesh.Kumar@ 123456vetmeduni.ac.at (G.K.)
                [2 ]Central Institute of Fisheries Education, Rohtak Centre, Haryana 124411, India
                Author notes
                [* ]Correspondence: Mansour.El-Matbouli@ 123456vetmeduni.ac.at ; Tel.: +43-125-077-5151
                Author information
                https://orcid.org/0000-0002-1958-2579
                Article
                ijms-19-00245
                10.3390/ijms19010245
                5796193
                29342931
                d4792fb8-fab5-418d-bdb9-43be986e8538
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 November 2017
                : 10 January 2018
                Categories
                Review

                Molecular biology
                fish disease,immune system,next-generation sequencing,transcriptome
                Molecular biology
                fish disease, immune system, next-generation sequencing, transcriptome

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