@misc{ChildsNikoloskiMayetal.2009,
  author    = {Childs, Liam H. and Nikoloski, Zoran and May, Patrick and Walther, Dirk},
  title     = {Identification and classification of ncRNA molecules using graph properties},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-45192},
  year      = {2009},
  abstract  = {The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets.},
  language  = {en}
}
@misc{MayChristianKempaetal.2009,
  author    = {May, Patrick and Christian, Jan-Ole and Kempa, Stefan and Walther, Dirk},
  title     = {ChlamyCyc : an integrative systems biology database and web-portal for Chlamydomonas reinhardtii},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-44947},
  year      = {2009},
  abstract  = {Background: The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and plant growth. In the era of modern highthroughput technologies there is an imperative need to integrate large-scale data sets from highthroughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism. Results: In the framework of the German Systems Biology initiative GoFORSYS, a pathway database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification. Conclusion: ChlamyCyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de.},
  language  = {en}
}
@article{ChristianMayKempaetal.2009,
  author    = {Christian, Nils and May, Patrick and Kempa, Stefan and Handorf, Thomas and Ebenhoeh, Oliver},
  title     = {An integrative approach towards completing genome-scale metabolic networks},
  issn      = {1742-206X},
  doi       = {10.1039/B915913b},
  year      = {2009},
  abstract  = {Genome-scale metabolic networks which have been automatically derived through sequence comparison techniques are necessarily incomplete. We propose a strategy that incorporates genomic sequence data and metabolite profiles into modeling approaches to arrive at improved gene annotations and more complete genome-scale metabolic networks. The core of our strategy is an algorithm that computes minimal sets of reactions by which a draft network has to be extended in order to be consistent with experimental observations. A particular strength of our approach is that alternative possibilities are suggested and thus experimentally testable hypotheses are produced. We carefully evaluate our strategy on the well-studied metabolic network of Escherichia coli, demonstrating how the predictions can be improved by incorporating sequence data. Subsequently, we apply our method to the recently sequenced green alga Chlamydomonas reinhardtii. We suggest specific genes in the genome of Chlamydomonas which are the strongest candidates for coding the responsible enzymes.},
  language  = {en}
}
@article{NikoloskiMaySelbig2009,
  author    = {Nikoloski, Zoran and May, Patrick and Selbig, Joachim},
  title     = {A new network model explains the evolution of plant-specific metabolic networks},
  issn      = {1095-6433},
  doi       = {10.1016/j.cbpa.2009.04.567},
  year      = {2009},
  language  = {en}
}