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      Analysis of Stress-Responsive Transcriptome in the Intestine of Asian Seabass (Lates calcarifer) using RNA-Seq

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          Abstract

          Identification of differentially expressed genes (DEGs) and regulated pathways in response to stressors using a whole-genome approach is critical to understanding the mechanisms underlying stress responses. We challenged Asian seabass with lipopolysaccharide (LPS), Vibrio harveyi, high salinity and fasting, and sequenced six cDNA libraries of intestine samples using Roche 454 RNA-seq. Over 1 million reads (average size: 516 bp) were obtained. The de novo assembly obtained 83 911 unisequences with an average length of 747 bp. In total, 62.3% of the unisequences were annotated. We observed overall similar expression profiles among different challenges, while a number of DEGs and regulated pathways were identified under specific challenges. More than 1000 DEGs and over 200 regulated pathways for each stressor were identified. Thirty-seven genes were differentially expressed in response to all challenges. Our data suggest that there is a global coordination and fine-tuning of gene regulation during different challenges. In addition, we detected dramatic immune responses in intestines under different stressors. This study is the first step towards the comprehensive understanding of the mechanisms underlying stress responses and supplies significant transcriptome resources for studying biological questions in non-model fish species.

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          Most cited references 56

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          Mapping and quantifying mammalian transcriptomes by RNA-Seq.

          We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.
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            Improved prediction of signal peptides: SignalP 3.0.

            We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions has increased notably for all three organism groups, eukaryotes, Gram-negative and Gram-positive bacteria. The accuracy of cleavage site prediction has increased in the range 6-17% over the previous version, whereas the signal peptide discrimination improvement is mainly due to the elimination of false-positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has been benchmarked against other available methods. Predictions can be made at the publicly available web server
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              KAAS: an automatic genome annotation and pathway reconstruction server

              The number of complete and draft genomes is rapidly growing in recent years, and it has become increasingly important to automate the identification of functional properties and biological roles of genes in these genomes. In the KEGG database, genes in complete genomes are annotated with the KEGG orthology (KO) identifiers, or the K numbers, based on the best hit information using Smith–Waterman scores as well as by the manual curation. Each K number represents an ortholog group of genes, and it is directly linked to an object in the KEGG pathway map or the BRITE functional hierarchy. Here, we have developed a web-based server called KAAS (KEGG Automatic Annotation Server: http://www.genome.jp/kegg/kaas/) i.e. an implementation of a rapid method to automatically assign K numbers to genes in the genome, enabling reconstruction of KEGG pathways and BRITE hierarchies. The method is based on sequence similarities, bi-directional best hit information and some heuristics, and has achieved a high degree of accuracy when compared with the manually curated KEGG GENES database.
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                Author and article information

                Journal
                DNA Res
                DNA Res
                dnares
                dnares
                DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes
                Oxford University Press
                1340-2838
                1756-1663
                October 2013
                10 June 2013
                10 June 2013
                : 20
                : 5
                : 449-460
                dst022
                10.1093/dnares/dst022
                3789556
                23761194
                © The Author 2013. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Genetics

                rna-seq, intestine, stress, disease, nutrition

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