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BiDaS: a web-based Monte Carlo BioData Simulator based on sequence/feature characteristics

BiDaS is a web-application that can generate massive Monte Carlo simulated sequence or numerical feature data sets (e.g. dinucleotide content, composition, transition, distribution properties) based on small user-provided data sets. BiDaS server enables users to analyze their data and generate large...

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Detalles Bibliográficos
Autores principales: Paraskevopoulou, Maria D., Vlachos, Ioannis S., Athanasiadis, Emmanouil, Spyrou, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3692108/
https://www.ncbi.nlm.nih.gov/pubmed/23716644
http://dx.doi.org/10.1093/nar/gkt420
Descripción
Sumario:BiDaS is a web-application that can generate massive Monte Carlo simulated sequence or numerical feature data sets (e.g. dinucleotide content, composition, transition, distribution properties) based on small user-provided data sets. BiDaS server enables users to analyze their data and generate large amounts of: (i) Simulated DNA/RNA and aminoacid (AA) sequences following practically identical sequence and/or extracted feature distributions with the original data. (ii) Simulated numerical features, presenting identical distributions, while preserving the exact 2D or 3D between-feature correlations observed in the original data sets. The server can project the provided sequences to multidimensional feature spaces based on: (i) 38 DNA/RNA features describing conformational and physicochemical nucleotide sequence features from the B-DNA-VIDEO database, (ii) 122 DNA/RNA features based on conformational and thermodynamic dinucleotide properties from the DiProDB database and (iii) Pseudo-aminoacid composition of the initial sequences. To the best of our knowledge, this is the first available web-server that allows users to generate vast numbers of biological data sets with realistic characteristics, while keeping between-feature associations. These data sets can be used for a wide variety of current biological problems, such as the in-depth study of gene, transcript, peptide and protein groups/families; the creation of large data sets from just a few available members and the strengthening of machine learning classifiers. All simulations use advanced Monte Carlo sampling techniques. The BiDaS web-application is available at http://bioserver-3.bioacademy.gr/Bioserver/BiDaS/.