en
Doctoral thesis
Open access
English

Bioinformatics tools to assist drug candidate discovery in venom gland transcriptomes

Defense date2012-10-01
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.

eng
Keywords
  • Séquençage de nouvelle génération
  • Transcriptome
  • Venin
  • Toxine
  • Peptide
  • Protéine
  • Activité pharmacologique
  • Bioinformatique
  • Famille de protéines
  • Profils généralisés
  • Modèles de Markov cachés
  • Analyse de séquence
  • Annotation automatique
  • Environnement intégré
  • Outil web
  • Next-generation sequencing techniques
  • Transcriptome analysis
  • Venom
  • Toxin
  • Peptide
  • Protein
  • Drug discovery
  • Bioinformatics
  • Protein family
  • Generalised profiles
  • Hidden Markov models
  • Sequence annotation
  • Web-based tool
  • Integrated platform
Citation (ISO format)
KOUA, Dominique Kadio. Bioinformatics tools to assist drug candidate discovery in venom gland transcriptomes. 2012. doi: 10.13097/archive-ouverte/unige:23951
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Technical informations

Creation11/12/2012 1:35:00 PM
First validation11/12/2012 1:35:00 PM
Update time03/30/2023 10:14:29 AM
Status update03/30/2023 10:14:28 AM
Last indexation01/29/2024 7:36:09 PM
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