@article{KoetschanFoersterKelleretal.2010,
  author    = {Koetschan, Christian and Foerster, Frank and Keller, Alexander and Schleicher, Tina and Ruderisch, Benjamin and Schwarz, Roland and Mueller, Tobias and Wolf, Matthias and Schultz, Joerg},
  title     = {The ITS2 Database III-sequences and structures for phylogeny},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68390},
  year      = {2010},
  abstract  = {The internal transcribed spacer 2 (ITS2) is a widely used phylogenetic marker. In the past, it has mainly been used for species level classifications. Nowadays, a wider applicability becomes apparent. Here, the conserved structure of the RNA molecule plays a vital role. We have developed the ITS2 Database (http://its2.bioapps .biozentrum.uni-wuerzburg.de) which holds information about sequence, structure and taxonomic classification of all ITS2 in GenBank. In the new version, we use Hidden Markov models (HMMs) for the identification and delineation of the ITS2 resulting in a major redesign of the annotation pipeline. This allowed the identification of more than 160 000 correct full ength and more than 50 000 partial structures. In the web interface, these can now be searched with a modified BLAST considering both sequence and structure, enabling rapid taxon sampling. Novel sequences can be annotated using the HMM based approach and modelled according to multiple template structures. Sequences can be searched for known and newly identified motifs. Together, the database and the web server build an exhaustive resource for ITS2 based phylogenetic analyses.},
  subject      = {Biologie},
  language  = {en}
}
@article{KellerFoersterMuelleretal.2010,
  author    = {Keller, Alexander and Foerster, Frank and Mueller, Tobias and Dandekar, Thomas and Schultz, Joerg and Wolf, Matthias},
  title     = {Including RNA secondary structures improves accuracy and robustness in reconstruction of phylogenetic trees},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-67832},
  year      = {2010},
  abstract  = {Background: In several studies, secondary structures of ribosomal genes have been used to improve the quality of phylogenetic reconstructions. An extensive evaluation of the benefits of secondary structure, however, is lacking. Results: This is the first study to counter this deficiency. We inspected the accuracy and robustness of phylogenetics with individual secondary structures by simulation experiments for artificial tree topologies with up to 18 taxa and for divergency levels in the range of typical phylogenetic studies. We chose the internal transcribed spacer 2 of the ribosomal cistron as an exemplary marker region. Simulation integrated the coevolution process of sequences with secondary structures. Additionally, the phylogenetic power of marker size duplication was investigated and compared with sequence and sequence-structure reconstruction methods. The results clearly show that accuracy and robustness of Neighbor Joining trees are largely improved by structural information in contrast to sequence only data, whereas a doubled marker size only accounts for robustness. Conclusions: Individual secondary structures of ribosomal RNA sequences provide a valuable gain of information content that is useful for phylogenetics. Thus, the usage of ITS2 sequence together with secondary structure for taxonomic inferences is recommended. Other reconstruction methods as maximum likelihood, bayesian inference or maximum parsimony may equally profit from secondary structure inclusion. Reviewers: This article was reviewed by Shamil Sunyaev, Andrea Tanzer (nominated by Frank Eisenhaber) and Eugene V. Koonin. Open peer review: Reviewed by Shamil Sunyaev, Andrea Tanzer (nominated by Frank Eisenhaber) and Eugene V. Koonin. For the full reviews, please go to the Reviewers' comments section.},
  subject      = {Phylogenie},
  language  = {en}
}
@article{PinkertSchultzReichardt2010,
  author    = {Pinkert, Stefan and Schultz, Joerg and Reichardt, Joerg},
  title     = {Protein Interaction Networks-More Than Mere Modules},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68426},
  year      = {2010},
  abstract  = {It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a ''module'' in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN) by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a ''module''. In a self-consistent manner, proteins are grouped into ''functional roles'' if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD) and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network's structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function prediction algorithms.},
  subject      = {Netzwerk},
  language  = {en}
}