Marine Genomics Europe

Marine Genomics Europe - MGE

 

mge_logo2_ok"Marine Genomics Europe" is a crossroads between Life Sciences, Ecology, Environment, BioInformatics and High technologies within a multicultural European network. The overall aim of this network was to set up and develop a European Network of Excellence, referred to as "Marine Genomic Europe" for the development, implementation, utilization and spreading of high-throughput approaches and genomic approaches for the investigation of the biology of marine organisms.

MGE was funded under the framework programme FP6 by the European commission. The aim of Marine Genomics Europe was to promote, develop and spread throughout the European Union a  broad range of genomic approaches, to investigate a wide range of questions related to the functioning of marine ecysystems and to the biology of marine organisms. With this aim in view, experts in genomics, proteomics and bioinformatics from several Centres of Excellence in genomics in Europe have networked with marine biologists who can make use of high-throughput genomics data. This involved the decication and development of common research infrastructures, both in genomics and in marine biology. Joining together these distinct scientific communities has established Europe's lead in marine genomics.

MGE united 44 institutions from 16 countries (within and outside Europe). MGE was aimed at promoting, developing and spreading throughout Europe a better understanding of the functioning of marine ecosystems and the biology of marine organisms. Moreover, MGE has established databases of marine resources through large scale biodiversity studies. About 450 scientists were involved in MGE.

 

Data system used within Marine Genomics Europe:

logo-MGE-portal

The Marine Genomics Europe network brings high-throughput approaches to the study of marine organisms. Within the platform, the MGE Bioinformatics Portal provides a central point of reference for all data sets and tools. It provides data storage, data analysis and data integration. The Bioinformatics platform also offers training in several areas. To ensure data exchange we suggest a naming convention for sequence data.

To access the portal a login is required. All MGE members already have an individual login (account) to access the MGE bioinformatics portal. The data sets are organized into distinct projects. All members of MGE can access the MGE project enabling them to use the tool collection and data visible to the MGE as a whole.

 

Available tools within the MGE Bioinformatics Portal

GenDB_logo      SAMS_logo      EMMA_logo

GenDB is a genome annotation system for prokaryotic genomes. The system has been developed as an extensible and user friendly framework for both bioinformatics researchers and biologists to use in their genome projects. The GenDB annotation engine will automatically identify, classify and annotate genes using a large collection of software tools. Many groups view this automatic annotation as the first step that needs to be followed by expert annotation of the genome.

 

Every genome project generates thousands of ESTs or shotgun reads. Users have high interest in a first look at the DNA sequence content of the individual reads, before they are assembled or clustered. Several steps are necessary to provide the researcher high quality sequences, as well as an overview of their content. For all these purposes we have implemented some additional extensions to GenDB within the SAMS system.
SAMS - Sequence Analysis and Management System - is a simple, easy to install and maintain open source system that provides the mechanisms to run a variety of tools on each read/EST, presenting the results in a web form.

 

As a high throughput technique, microarray experiments produce large data sets, consisting of measured data, laboratory protocols, and experimental settings. We have implemented the open source platform EMMA to store and analyze these data. EMMA gives access to all the transcriptomics data sets stored in the ArrayLIMS and provides automated pipelines for data processing, allowing an automated or manual analysis of expression profiles.