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      De novo assembly of bacterial transcriptomes from RNA-seq data

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      Genome Biology
      BioMed Central

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          Abstract

          Transcriptome assays are increasingly being performed by high-throughput RNA sequencing (RNA-seq). For organisms whose genomes have not been sequenced and annotated, transcriptomes must be assembled de novo from the RNA-seq data. Here, we present novel algorithms, specific to bacterial gene structures and transcriptomes, for analysis of bacterial RNA-seq data and de novo transcriptome assembly. The algorithms are implemented in an open source software system called Rockhopper 2. We find that Rockhopper 2 outperforms other de novo transcriptome assemblers and offers accurate and efficient analysis of bacterial RNA-seq data. Rockhopper 2 is available at http://cs.wellesley.edu/~btjaden/Rockhopper.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-014-0572-2) contains supplementary material, which is available to authorized users.

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          Most cited references10

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs

            RNA-Seq provides an unbiased way to study a transcriptome, including both coding and non-coding genes. To date, most RNA-Seq studies have critically depended on existing annotations, and thus focused on expression levels and variation in known transcripts. Here, we present Scripture, a method to reconstruct the transcriptome of a mammalian cell using only RNA-Seq reads and the genome sequence. We apply it to mouse embryonic stem cells, neuronal precursor cells, and lung fibroblasts to accurately reconstruct the full-length gene structures for the vast majority of known expressed genes. We identify substantial variation in protein-coding genes, including thousands of novel 5′-start sites, 3′-ends, and internal coding exons. We then determine the gene structures of over a thousand lincRNA and antisense loci. Our results open the way to direct experimental manipulation of thousands of non-coding RNAs, and demonstrate the power of ab initio reconstruction to render a comprehensive picture of mammalian transcriptomes.
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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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                Author and article information

                Contributors
                btjaden@wellesley.edu
                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                13 January 2015
                13 January 2015
                2015
                : 16
                : 1
                : 1
                Affiliations
                Computer Science Department, Wellesley College, Wellesley, MA 02481 USA
                Article
                572
                10.1186/s13059-014-0572-2
                4316799
                25583448
                deaaf163-5afd-4a0a-8097-317fa085a17e
                © Tjaden; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 August 2014
                : 15 December 2014
                Categories
                Software
                Custom metadata
                © The Author(s) 2015

                Genetics
                Genetics

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