@article{DandekarLiangKrueger2013, author = {Dandekar, Thomas and Liang, Chunguang and Kr{\"u}ger, Beate}, title = {GoSynthetic database tool to analyse natural and engineered molecular processes}, series = {Database}, journal = {Database}, doi = {10.1093/database/bat043}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-97023}, year = {2013}, abstract = {An essential topic for synthetic biologists is to understand the structure and function of biological processes and involved proteins and plan experiments accordingly. Remarkable progress has been made in recent years towards this goal. However, efforts to collect and present all information on processes and functions are still cumbersome. The database tool GoSynthetic provides a new, simple and fast way to analyse biological processes applying a hierarchical database. Four different search modes are implemented. Furthermore, protein interaction data, cross-links to organism-specific databases (17 organisms including six model organisms and their interactions), COG/KOG, GO and IntAct are warehoused. The built in connection to technical and engineering terms enables a simple switching between biological concepts and concepts from engineering, electronics and synthetic biology. The current version of GoSynthetic covers more than one million processes, proteins, COGs and GOs. It is illustrated by various application examples probing process differences and designing modifications.}, language = {en} } @article{SchulzeTillichDandekaretal.2013, author = {Schulze, Katja and Tillich, Ulrich M. and Dandekar, Thomas and Frohme, Marcus}, title = {PlanktoVision - an automated analysis system for the identification of phytoplankton}, series = {BMC Bioinformatics}, journal = {BMC Bioinformatics}, doi = {10.1186/1471-2105-14-115}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-96395}, year = {2013}, abstract = {Background Phytoplankton communities are often used as a marker for the determination of fresh water quality. The routine analysis, however, is very time consuming and expensive as it is carried out manually by trained personnel. The goal of this work is to develop a system for an automated analysis. Results A novel open source system for the automated recognition of phytoplankton by the use of microscopy and image analysis was developed. It integrates the segmentation of the organisms from the background, the calculation of a large range of features, and a neural network for the classification of imaged organisms into different groups of plankton taxa. The analysis of samples containing 10 different taxa showed an average recognition rate of 94.7\% and an average error rate of 5.5\%. The presented system has a flexible framework which easily allows expanding it to include additional taxa in the future. Conclusions The implemented automated microscopy and the new open source image analysis system - PlanktoVision - showed classification results that were comparable or better than existing systems and the exclusion of non-plankton particles could be greatly improved. The software package is published as free software and is available to anyone to help make the analysis of water quality more reproducible and cost effective.}, language = {en} }