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      Uncovering the novel characteristics of Asian honey bee, Apis cerana, by whole genome sequencing

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          Abstract

          Background

          The honey bee is an important model system for increasing understanding of molecular and neural mechanisms underlying social behaviors relevant to the agricultural industry and basic science. The western honey bee, Apis mellifera, has served as a model species, and its genome sequence has been published. In contrast, the genome of the Asian honey bee, Apis cerana, has not yet been sequenced. A. cerana has been raised in Asian countries for thousands of years and has brought considerable economic benefits to the apicultural industry. A cerana has divergent biological traits compared to A. mellifera and it has played a key role in maintaining biodiversity in eastern and southern Asia. Here we report the first whole genome sequence of A. cerana.

          Results

          Using de novo assembly methods, we produced a 238 Mbp draft of the A. cerana genome and generated 10,651 genes. A.cerana-specific genes were analyzed to better understand the novel characteristics of this honey bee species. Seventy-two percent of the A. cerana-specific genes had more than one GO term, and 1,696 enzymes were categorized into 125 pathways. Genes involved in chemoreception and immunity were carefully identified and compared to those from other sequenced insect models. These included 10 gustatory receptors, 119 odorant receptors, 10 ionotropic receptors, and 160 immune-related genes.

          Conclusions

          This first report of the whole genome sequence of A. cerana provides resources for comparative sociogenomics, especially in the field of social insect communication. These important tools will contribute to a better understanding of the complex behaviors and natural biology of the Asian honey bee and to anticipate its future evolutionary trajectory.

          Electronic supplementary material

          The online version of this article (doi:10.1186/1471-2164-16-1) contains supplementary material, which is available to authorized users.

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          Most cited references127

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

              Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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                Author and article information

                Contributors
                tkfdmdgoz@gmail.com
                jjwf82@snu.ac.kr
                euphra@snu.ac.kr
                murukarthick@snu.ac.kr
                integer@snu.ac.kr
                limjs1@snu.ac.kr
                yeisooyu@gmail.com
                beechoi@korea.kr
                mllee6@korea.kr
                ypark@ksu.edu
                choii@snu.ac.kr
                tjyang@snu.ac.kr
                Owain.Edwards@csiro.au
                gjnah@snu.ac.kr
                biomodeling@snu.ac.kr
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                2 January 2015
                2015
                : 16
                : 1
                : 1
                Affiliations
                [ ]Biomodulation Major, Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, 151-921 Republic of Korea
                [ ]National Instrumentation Center for Environmental Management, College of Agriculture and Life Sciences, Seoul National University, Seoul, 151-742 Republic of Korea
                [ ]Department of Plant Science, College of Agriculture and Life Sciences, Seoul National University, Seoul, 151-921 Republic of Korea
                [ ]Arizona Genomics Institute, University of Arizona, Tucson, Arizona 85721 USA
                [ ]National Institute of Agricultural Biotechnology, Rural development Administration, Suwon, 441-707 Republic of Korea
                [ ]Department of Entomology, Kansas State University, Manhattan, Kansas USA
                [ ]CSIRO Ecosystem Sciences, Centre for Environment and Life Sciences, Underwood Avenue, Floreat, WA 6014 Australia
                Article
                6994
                10.1186/1471-2164-16-1
                4326529
                25553907
                fa5eccc8-8c16-4d47-8844-e935d0cfd3aa
                © Park et al.; licensee BioMed Central. 2015

                This article is published under license to BioMed Central Ltd. 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
                : 1 August 2014
                : 2 December 2014
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

                Genetics
                apis cerana,asian honey bee,genome,social insect,chemosensory receptors,honey bee immunity
                Genetics
                apis cerana, asian honey bee, genome, social insect, chemosensory receptors, honey bee immunity

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