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Bioinformatics tools to assist drug candidate discovery in venom gland transcriptomes

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Defense Thèse de doctorat : Univ. Genève, 2012 - Sc. 4471 - 2012/10/01
Abstract Current pharmaceutical research is actively exploring the field of natural peptides. Venomics addresses this issue with the study of toxins. The concomitant development of sequencing techniques is opening new perspectives of understanding biological mechanisms. Transcriptome sequencing of specific tissues is undertaken to better understand and characterize the context of gene expression. In this framework, transcriptomic data made available require automated processing workflows and user-friendly interfaces for data exploitation and comprehension. We present TATools, a bioinformatic platform that provides a unique management environment for understanding transcriptome data by merging results of diverse classical sequence analysis. Additional features and dedicated viewer pages makes TATools a valuable solution for highlighting novelty in a single transcriptome as well as cross-analysis of several transcriptomes in the same environment. TATools is validated in the context of venomics. This thesis reports the genesis of the design of TATools as exposed in two published articles and a manuscript (at this stage under revision) and it describes the final outcome of this work with the support of a submitted manuscript detailing the analysis workflow. The use of TATools is illustrated with the study of the Conus consors venom gland transcriptome and subsequent conopeptide identification and classification. Other applications of parts of the TATools platform are shown in another two published articles.
Keywords Séquençage de nouvelle générationTranscriptomeVeninToxinePeptideProtéineActivité pharmacologiqueBioinformatiqueFamille de protéinesProfils généralisésModèles de Markov cachésAnalyse de séquenceAnnotation automatiqueEnvironnement intégréOutil webNext-generation sequencing techniquesTranscriptome analysisVenomToxinPeptideProteinDrug discoveryBioinformaticsProtein familyGeneralised profilesHidden Markov modelsSequence annotationWeb-based toolIntegrated platform
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URN: urn:nbn:ch:unige-239511
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Research group Swiss Institute of Bioinformatics
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KOUA, Dominique Kadio. Bioinformatics tools to assist drug candidate discovery in venom gland transcriptomes. Université de Genève. Thèse, 2012. https://archive-ouverte.unige.ch/unige:23951

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Deposited on : 2012-11-12

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