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Professor An-Ping Zeng, Leiter des Instituts für Bioprozess- und Biosystemtechnik der Technischen [...]
The PhD students Jan Herzog (IBB) and Sebastian Hofmann started the podcast "Füchse der [...]
Within the framework of the AK PAT-Kolloquium Dr. Johannes Möller was awarded the [...]

A.   Database

A biochemical reaction database in excel format and the programs in VBA (Visual Basic for Applications) (Ma and Zeng. Bioinformatics. 2003. 19:270-7)

A protein database of Klebsiella pneumoniae constructed from low-coverage genome sequences (Wang et al. Proteome Science. 2003. 1:6)

A genome sequence database of different strains of Streptococcus mutans sequenced during the BioInSys project

A Bioreaction database for genome-scale metabolic network reconstruction in excel format (Stelzer et al 2011. Integrative Biology.*DOI: 10.1039/C1IB00008J)

StrepCyc: A Genome database of Streptococcus

StrepReg: A Regulation database of Streptococcus

B.   Software

The program IdentiCS combines the identification of coding sequences from raw (esp. low coverage) genome sequences with the reconstruction, visualization and comparison of metbolic networks (Sun and Zeng. BMC Bioinformatics, 2004. 5:112)

A Trend Correlation (TC) Based New Method for Reverse Engineering Biological Network: We developed a new method to infer functional linkages and further biological network between genes from time-series expression data (He and Zeng, BMC Bioinformatics, 2006, 7:69). Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. We not only consider the qualitative information (i.e. the change trend) but also the quantitative information (the change level) in the original data. We seek to make the methods more noise-tolerant and at the same time to keep more useful information in the expression values. The main programs used in this method can be downloaded here. All these programs are written in Matlab. The first program TC_Linkage is used to calculate a maximal local alignment (sc) of change trend score for all the gene pairs in the array data. If you only want to infer functional linkages among some genes, the second program TC_Partial_Linkage is preferred, which is used to calculate a maximal local alignment (sc) of change trend score for the given gene pairs. Another program TC_CC is used to calculate cc (the correlated coefficient) between the maximal matched change levels of each gene pair.

C.   Other supplementary materials for publications

Supplementary materials for Ma and Zeng, 2003. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics. 19:270-277

Supplementary materials for Ma and Zeng, 2003. The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics. 19:1423-1430