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      Genome sequence of the Asian Tiger mosquito,Aedes albopictus, reveals insights into its biology, genetics, and evolution

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          Abstract

          The Asian tiger mosquito, Aedes albopictus, is a highly successful invasive species that transmits a number of human viral diseases, including dengue and Chikungunya fevers. This species has a large genome with significant population-based size variation. The complete genome sequence was determined for the Foshan strain, an established laboratory colony derived from wild mosquitoes from southeastern China, a region within the historical range of the origin of the species. The genome comprises 1,967 Mb, the largest mosquito genome sequenced to date, and its size results principally from an abundance of repetitive DNA classes. In addition, expansions of the numbers of members in gene families involved in insecticide-resistance mechanisms, diapause, sex determination, immunity, and olfaction also contribute to the larger size. Portions of integrated flavivirus-like genomes support a shared evolutionary history of association of these viruses with their vector. The large genome repertory may contribute to the adaptability and success of Ae. albopictus as an invasive species.

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          Identification of novel transcripts in annotated genomes using RNA-Seq.

          Adam Roberts, Harold Pimentel, Cole Trapnell … (2011)
          We describe a new 'reference annotation based transcript assembly' problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation. The methods described in this article are implemented in the Cufflinks suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks. The software is released under the BOOST license. cole@broadinstitute.org; lpachter@math.berkeley.edu Supplementary data are available at Bioinformatics online.
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            Ab initio gene finding in Drosophila genomic DNA.

            A. A. Salamov, V. V. Solovyev (2000)
            Ab initio gene identification in the genomic sequence of Drosophila melanogaster was obtained using (human gene predictor) and Fgenesh programs that have organism-specific parameters for human, Drosophila, plants, yeast, and nematode. We did not use information about cDNA/EST in most predictions to model a real situation for finding new genes because information about complete cDNA is often absent or based on very small partial fragments. We investigated the accuracy of gene prediction on different levels and designed several schemes to predict an unambiguous set of genes (annotation CGG1), a set of reliable exons (annotation CGG2), and the most complete set of exons (annotation CGG3). For 49 genes, protein products of which have clear homologs in protein databases, predictions were recomputed by Fgenesh+ program. The first annotation serves as the optimal computational description of new sequence to be presented in a database. Reliable exons from the second annotation serve as good candidates for selecting the PCR primers for experimental work for gene structure verification. Our results shows that we can identify approximately 90% of coding nucleotides with 20% false positives. At the exon level we accurately predicted 65% of exons and 89% including overlapping exons with 49% false positives. Optimizing accuracy of prediction, we designed a gene identification scheme using Fgenesh, which provided sensitivity (Sn) = 98% and specificity (Sp) = 86% at the base level, Sn = 81% (97% including overlapping exons) and Sp = 58% at the exon level and Sn = 72% and Sp = 39% at the gene level (estimating sensitivity on std1 set and specificity on std3 set). In general, these results showed that computational gene prediction can be a reliable tool for annotating new genomic sequences, giving accurate information on 90% of coding sequences with 14% false positives. However, exact gene prediction (especially at the gene level) needs additional improvement using gene prediction algorithms. The program was also tested for predicting genes of human Chromosome 22 (the last variant of Fgenesh can analyze the whole chromosome sequence). This analysis has demonstrated that the 88% of manually annotated exons in Chromosome 22 were among the ab initio predicted exons. The suite of gene identification programs is available through the WWW server of Computational Genomics Group at http://genomic.sanger.ac.uk/gf. html.
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              Evolutionary dynamics of immune-related genes and pathways in disease-vector mosquitoes.

              Robert Waterhouse, Evgenia Kriventseva, Stephan Meister … (2007)
              Mosquitoes are vectors of parasitic and viral diseases of immense importance for public health. The acquisition of the genome sequence of the yellow fever and Dengue vector, Aedes aegypti (Aa), has enabled a comparative phylogenomic analysis of the insect immune repertoire: in Aa, the malaria vector Anopheles gambiae (Ag), and the fruit fly Drosophila melanogaster (Dm). Analysis of immune signaling pathways and response modules reveals both conservative and rapidly evolving features associated with different functional gene categories and particular aspects of immune reactions. These dynamics reflect in part continuous readjustment between accommodation and rejection of pathogens and suggest how innate immunity may have evolved.
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                Author and article information

                Journal
                Title: Proceedings of the National Academy of Sciences
                Abbreviated Title: Proc Natl Acad Sci USA
                Publisher: Proceedings of the National Academy of Sciences
                ISSN (Print): 0027-8424
                ISSN (Electronic): 1091-6490
                Publication date Created: November 03 2015
                Publication date Created: November 03 2015
                Publication date (Print): November 03 2015
                Publication date (Electronic): October 19 2015
                Volume: 112
                Issue: 44
                Pages: E5907-E5915
                Article
                DOI: 10.1073/pnas.1516410112
                PubMed ID: 26483478
                SO-VID: d49b865a-3c5d-4bf9-91a0-667bdf61d6d0
                Copyright © © 2015
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