MEMOSys 2.0: an update of the bioinformatics database for genome-scale models and genomic data

Stephan Pabinger,Zlatko Trajanoski,Andreas Dander,Michaela Willi,Timo Hardiman,Rene Snajder

doi:10.1093/database/bau004

Abstract

The MEtabolic MOdel research and development System (MEMOSys) is a versatile database for the management, storage and development of genome-scale models (GEMs). Since its initial release, the database has undergone major improvements, and the new version introduces several new features. First, the novel concept of derived models allows users to create model hierarchies that automatically propagate modifications along their order. Second, all stored components can now be easily enhanced with additional annotations that can be directly extracted from a supplied Systems Biology Markup Language (SBML) file. Third, the web application has been substantially revised and now features new query mechanisms, an easy search system for reactions and new link-out services to publicly available databases. Fourth, the updated database now contains 20 publicly available models, which can be easily exported into standardized formats for further analysis. Fifth, MEMOSys 2.0 is now also available as a fully configured virtual image and can be found online at http://www.icbi.at/memosys and http://memoys.i-med.ac.at.Database URL: http://memosys.i-med.ac.at

Highlights

The extensive use of next-generation sequencing (NGS) technology in biological research has spurred the creation of genome-scale metabolic models for a multitude of organisms (1)
An important cornerstone of MEtabolic MOdel research and development System (MEMOSys) is the compliance with the Systems Biology Markup Language (SBML)
Import and export of models are based on the native JAVA library JSBML (14), improving the overall performance of the system and considerably facilitating the installation of MEMOSys 2.0

Summary

Introduction

The extensive use of next-generation sequencing (NGS) technology in biological research has spurred the creation of genome-scale metabolic models for a multitude of organisms (1). The use of existing models for creating new GEMs might lead to bias where components of the novel organisms are underrepresented. New publicly available models were integrated into the database, and the complete MEMOSys system is available as a fully configured virtual machine image.

Results

Conclusion