BRIGEP—the BRIDGE-based genome–transcriptome–proteome browser

The growing amount of information resulting from the increasing number of publicly available genomes and experimental results thereof necessitates the development of comprehensive systems for data processing and analysis. In this paper, we describe the current state and latest developments of our BRIGEP bioinformatics software system consisting of three web-based applications: GenDB, EMMA and ProDB. These applications facilitate the processing and analysis of bacterial genome, transcriptome and proteome data and are actively used by numerous international groups. We are currently in the process of extensively interconnecting these applications. BRIGEP was developed in the Bioinformatics Resource Facility of the Center for Biotechnology at Bielefeld University and is freely available. A demo project with sample data and access to all three tools is available at . Code bundles for these and other tools developed in our group are accessible on our FTP server at

Is Bayesian Imitation Learning the Route to Believable Gamebots?

Thurau C, Paczian T, Bauckhage C. Is Bayesian Imitation Learning the Route to Believable Gamebots? In: Proc. GAME-ON North America. 2005: 3-9.As it strives to imitate observably successful actions, imitation learning allows for a quick acquisition of proven behaviors. Recent work from psychology and robotics suggests that Bayesian probability theory provides a mathematical framework for imitation learning. In this paper, we investigate the use of Bayesian imitation learning in realizing more life-like computer game characters. Following our general strategy of analyzing the network traffic of multi-player online games, we will present experiments in automatic imitation of behaviors contained in human generated data. Our results show that the Bayesian framework indeed leads to game agent behavior that appears very much human-like

Bayesian Imitation Learning in Game Characters

Thurau C, Paczian T, Bauckhage C. Bayesian Imitation Learning in Game Characters. In: International Workshop on Automatic Learning and Real-Time (ALaRT’05). 2005: 143-151.As it strives to imitate observably successful actions, imitation learning allows for a quick acquisition of proven behaviors. Recent work from psychology and robotics suggests that Bayesian probability theory provides a mathematical framework for imitation learning. In this paper, we investigate the use of Bayesian imitation learning in realizing more life-like computer game characters. Following our general strategy of analyzing the network traffic of multi-player online games, we will present experiments in automatic imitation of behaviors contained in human generated data. Our results show that the Bayesian framework indeed leads to game agent behavior that appears

A Metagenomics Portal for a Democratized Sequencing World

The democratized world of sequencing is leading to numerous data analysis challenges; MG-RAST addresses many of these challenges for diverse datasets, including amplicon datasets, shotgun metagenomes, and metatranscriptomes. The changes from version 2 to version 3 include the addition of a dedicated gene calling stage using FragGenescan, clustering of predicted proteins at 90% identity, and the use of BLAT for the computation of similarities. Together with changes in the underlying software infrastructure, this has enabled the dramatic scaling up of pipeline throughput while remaining on a limited hardware budget. The Web-based service allows upload, fully automated analysis, and visualization of results. As a result of the plummeting cost of sequencing and the readily available analytical power of MG-RAST, over 78,000 metagenomic datasets have been analyzed, with over 12,000 of them publicly available in MG-RAST