Bioinformatics & Systems Biology
Today, massively parallel DNA sequencing or hybridization approaches allow the identification of not only the gene repertoire but also the gene regulatory networks of an organism. The huge amounts of data acquired from such experiments can only be handled with intensive bioinformatics support that has to provide an adequate infrastructure for storing and analyzing these data. Thus, bioinformatics has to deliver efficient data analysis algorithms, user-friendly tools and software applications, as well as extensive hardware infrastructure for answering such questions.
As part of the Bielefeld-Giessen Resource Center for Microbial Bioinformatics (BiGi), a service unit of the 'German Network for Bioinformatics Infrastructure – de.NBI', the group is focused on data management for genome and post-genome research projects that require new software solutions for systematic data acquisition, secure data storage of structured information, and high-throughput data analysis. Bioinformatics training and education and the cooperation within the German bioinformatics community is a main scope of the group.
- Recent publications
We are happy to announce our most recent article published in NAR about the expansion and re-classification of the extracytoplasmic function sigma factor family.
Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scoresPlasmids play a vital role in the environmental adaptation of bacteria. Due to potential mobilization or conjugation capabilities, they are important genetic vehicles for antimicrobial resistance genes and virulence factors with huge clinical implications. To comprehensively characterize plasmids via NGS methods, Platon allows the identification and characterization of plasmid-borne contigs from bacterial short-read draft assemblies achieving both high accuracy and balanced classifications in terms of sensitivity and specificity. The software follows a new approach to this problem exploiting the natural distribution bias of protein-coding genes between chromosomes and plasmids represented by a new metric: the replicon distribution score (RDS). To further increase the achieved sensitivity, platon applies several heuristics taking into account plasmid-specific contig characterizations.