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      Comparative transcriptome analysis of venom glands from Cotesia vestalis and Diadromus collaris, two endoparasitoids of the host Plutella xylostella

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          Abstract

          Venoms secreted by the venom gland (VG) of parasitoid wasp help ensure successful parasitism by host immune suppression and developmental regulation. Cotesia vestalis, a larval endoparasitoid, and Diadromus collaris, a pupal endoparasitoid, parasitize the diamondback moth (DBM), Plutella xylostella. To explore and compare the venom components of two endoparasitoids, we sequenced transcriptomes of the VGs and wasp bodies without VGs (BWVGs) of the two endoparasitoids. Statistically enriched GO terms and KEGG pathways of the two VGs compared to respective whole-body background were similar and reflected active protein biosynthesis activities in the two VGs. 1,595 VG specific genes of the D. collaris VG and 1,461 VG specific genes of the C. vestalis VG were identified by comparative transcript profiling. A total of 444 and 513 genes encoding potential secretory proteins were identified and defined as putative venom genes in D. collaris VG and C. vestalis VG, respectively. The putative venom genes of the two wasps showed no significant similarity or convergence. More venom genes were predicted in D. collaris VG than C. vestalis VG, especially hydrolase-coding genes. Differences in the types and quantities of putative venom genes shed light on different venom functions.

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          TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets.

          Geo M Pertea, Xiaoqiu Huang, Feng Liang … (2003)
          TGICL is a pipeline for analysis of large Expressed Sequence Tags (EST) and mRNA databases in which the sequences are first clustered based on pairwise sequence similarity, and then assembled by individual clusters (optionally with quality values) to produce longer, more complete consensus sequences. The system can run on multi-CPU architectures including SMP and PVM.
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            HMMER web server: 2015 update

            Robert D. Finn, Jody Clements, William Arndt … (2015)
            The HMMER website, available at http://www.ebi.ac.uk/Tools/hmmer/, provides access to the protein homology search algorithms found in the HMMER software suite. Since the first release of the website in 2011, the search repertoire has been expanded to include the iterative search algorithm, jackhmmer. The continued growth of the target sequence databases means that traditional tabular representations of significant sequence hits can be overwhelming to the user. Consequently, additional ways of presenting homology search results have been developed, allowing them to be summarised according to taxonomic distribution or domain architecture. The taxonomy and domain architecture representations can be used in combination to filter the results according to the needs of a user. Searches can also be restricted prior to submission using a new taxonomic filter, which not only ensures that the results are specific to the requested taxonomic group, but also improves search performance. The repertoire of profile hidden Markov model libraries, which are used for annotation of query sequences with protein families and domains, has been expanded to include the libraries from CATH-Gene3D, PIRSF, Superfamily and TIGRFAMs. Finally, we discuss the relocation of the HMMER webserver to the European Bioinformatics Institute and the potential impact that this will have.
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              A new method to measure the semantic similarity of GO terms.

              James Wang, Zhidian Du, Rapeeporn Payattakool … (2007)
              Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME. http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.
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                Author and article information

                Contributors
                Xue-xin Chen: xxchen@zju.edu.cn
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 2 May 2017
                Publication date PMC-release: 2 May 2017
                Publication date Collection: 2017
                Volume: 7
                Electronic Location Identifier: 1298
                Affiliations
                [1 ]ISNI 0000 0004 1759 700X, GRID grid.13402.34, Institute of Insect Sciences, , Zhejiang University, ; 866 Yuhangtang Road, Hangzhou, 310058 China
                [2 ]ISNI 0000 0004 1759 700X, GRID grid.13402.34, Ministry of Agriculture Key Lab of Molecular Biology of Crop Pathogens and Insect Pests, , Zhejiang University, ; 866 Yuhangtang Road, Hangzhou, 310058 China
                [3 ]ISNI 0000 0004 1759 700X, GRID grid.13402.34, State Key Lab of Rice Biology, , Zhejiang University, ; 866 Yuhangtang Road, Hangzhou, 310058 China
                Article
                Publisher ID: 1383
                DOI: 10.1038/s41598-017-01383-2
                PMC ID: 5431001
                PubMed ID: 28465546
                SO-VID: 5af8bdfc-045c-438c-ad78-9a1a772c6873
                Copyright © © The Author(s) 2017
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 2 December 2016
                Date accepted : 29 March 2017
                Categories
                Subject: Article
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                issue-copyright-statement © The Author(s) 2017

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