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      RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

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          Abstract

          Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

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          The transcriptional landscape of the yeast genome defined by RNA sequencing.

          The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3'-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.
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            Next-generation transcriptome assembly.

            Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies - along with some perspectives on transcriptome assembly in the near future.
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              Is Open Access

              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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                Author and article information

                Journal
                Annual Review of Biomedical Data Science
                Annu. Rev. Biomed. Data Sci.
                Annual Reviews
                2574-3414
                2574-3414
                July 20 2019
                July 20 2019
                : 2
                : 1
                : 139-173
                Affiliations
                [1 ]Bioinformatics Institute Ghent and Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
                [2 ]Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland;
                [3 ]Department of Biostatistics and Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27514, USA
                [4 ]Department of Computer Science, Stony Brook University, Stony Brook, New York 11794, USA
                Article
                10.1146/annurev-biodatasci-072018-021255
                © 2019

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