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P173 - GCB 2010 - German Conference on Bioinformatics 2010

https://dl.gi.de/handle/20.500.12116/21226
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Auflistung P173 - GCB 2010 - German Conference on Bioinformatics 2010 nach Titel

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  • Konferenzbeitrag
    CASOP GS: computing intervention strategies targeted at production improvement in genome-scale metabolic networks
    (German Conference on Bioinformatics 2010, 2010) Bohl, Katrin; Figueiredo, Luís F. de; Hädicke, Oliver; Klamt, Steffen; Kost, Christian; Schuster, Stefan; Kaleta, Christoph
    Metabolic engineering aims to improve the production of desired biochemicals and proteins in organisms and therefore, plays a central role in Biotechnology. However, the design of overproducing strains is not straightforward due to the complexity of metabolic and regulatory networks. Thus, theoretical tools supporting the design of such strains have been developed. One particular method, CASOP, uses the set of elementary flux modes (EFMs) of a reaction network to propose strategies for the overproduction of a target compound. The advantage of CASOP over other approaches is that it does not consider a single specific flux distribution within the network but the whole set of possible flux distributions represented by the EFMs of the network. Moreover, its application results not only in the identification of candidate loci that can be knocked out, but additionally proposes overexpression candidates. However, the utilization of CASOP was restricted to small and medium scale metabolic networks so far, since the entire set of EFMs cannot be enumerated in such networks. This work presents an approach that allows to use CASOP even in genome-scale networks. This approach is based on an estimation of the score utilized in CASOP through a sample of EFMs within a genome-scale network. Using EFMs from the genome-scale metabolic network gives a more reliable picture of the metabolic capabilities of an organism required for the design of overproducing strains. We applied our new method to identify strategies for the overproduction of succinate and histidine in Escherichia coli. The succinate case study, in particular, proposes engineering targets which resemble known strategies already applied in E. coli. Availability: Source code and an executable are available upon request.
  • Konferenzbeitrag
    Efficient sequence clustering for RNA-seq data without a reference genome
    (German Conference on Bioinformatics 2010, 2010) Battke, Florian; Körner, Stephan; Hüttner, Steffen; Nieselt, Kay
    New deep-sequencing technologies are applied to transcript sequencing (RNA-seq) for transcriptomic studies. However, current approaches are based on the availability of a reference genome sequence for read mapping. We present Passage, a method for efficient read clustering in the absence of a reference genome that allows sequencing-based comparative transcriptomic studies for currently unsequenced organisms. If the reference genome is available, our method can be used to reduce the computational effort involved in read mapping. Comparisons to microarray data show a correlation of 0.69, proving the validity of our approach.
  • Konferenzbeitrag
    Efficient similarity retrieval of protein binding sites based on histogram comparison
    (German Conference on Bioinformatics 2010, 2010) Fober, Thomas; Mernberger, Marco; Klebe, Gerhard; Hüllermeier, Eyke
    We propose a method for comparing protein structures or, more specifically, protein binding sites using a histogram-based representation that captures important geometrical and physico-chemical properties. In comparison to hitherto existing approaches in structural bioinformatics, especially methods from graph theory and computational geometry, our approach is computationally much more efficient. Moreover, despite its simplicity, it appears to capture and recover functional similarities surprisingly well.
  • Konferenzbeitrag
    Finding optimal sets of enriched regions in chip-seq data
    (German Conference on Bioinformatics 2010, 2010) Gogol-Döring, Andreas; Chen, Wei
    The main challenge when analyzing ChIP-Seq data is the identification of DNA-protein binding sites by finding genomic regions that are enriched with sequencing reads. We present a new tool called qips especially suited for processing ChIP-Seq data containing broader enriched regions. Our tool certainly finds all enriched regions that are not exceeded by higher significant alternatives.
  • Editiertes Buch
  • Konferenzbeitrag
    Learning pathway-based decision rules to classify microarray cancer samples
    (German Conference on Bioinformatics 2010, 2010) Glaab, Enrico; Garibaldi, Jonathan M.; Krasnogor, Natalio
    Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninformative genes. Combining microarray data with cellular pathway data by using new integrative analysis methods could help to alleviate some of these problems and provide new biological insights. We present a method for learning simple decision rules for class prediction from pairwise comparisons of cellular pathways in terms of gene set expression levels representing the upand downregulation of pathway members. The procedure generates compact and comprehensible sets of rules, describing changes in the relative ranks of gene expression levels in pairs of pathways across different biological conditions. Re- sults for two large-scale microarray studies, containing samples from prostate cancer and B-cell lymphoma patients, show that the method provides robust and accurate rule sets and new insights on differentially regulated pathway pairs. However, the main benefit of these predictive models in comparison to other classification methods like support vector machines lies not in the attained accuracy levels but in the ease of interpretation and the insights they provide on the relative regulation of cellular pathways in the biological conditions under consideration.
  • Konferenzbeitrag
    METAtarget – extracting key enzymes of metabolic regulation from high-throughput metabolomics data using KEGG REACTION information
    (German Conference on Bioinformatics 2010, 2010) Budczies, Jan; Denkert, Carsten; Müller, Berit M.; Brockmöller, Scarlet F.; Dietel, Manfred; Griffin, Jules L.; Oresic, Matej; Fiehn, Oliver
    METAtarget is a new method for reverse engineering of metabolic networks and the detection of targets enzymes from high-throughput metabolomics data. Using KEGG REACTION, reactant partners are identified and the ratio of product to substrate metabolite concentrations is employed as surrogate for the reaction activity. A test statistics is introduced to assess changes in the activity of reactions between different disease states. In an application of METAtarget to breast cancer, we investigate the dependence of tumor metabolism on hormone receptor status. To this end, we analyze metabolomics data that were generated within the METAcancer project and compare the identified reactions with data on enzyme expression that are obtained from publicly available breast cancer gene expression series. As result, deregulation of key enzymes and reactions of glycolysis, glutaminolysis and other metabolic pathways are detected.
  • Konferenzbeitrag
    Predicting miRNA targets utilizing an extended profile HMM
    (German Conference on Bioinformatics 2010, 2010) Grau, Jan; Arend, Daniel; Grosse, Ivo; Hatzigeorgiou, Artemis G.; Keilwagen, Jens; Maragkakis, Manolis; Weinholdt, Claus; Posch, Stefan
    The regulation of many cellular processes is influenced by miRNAs, and bioinformatics approaches for predicting miRNA targets evolve rapidly. Here, we propose conditional profile HMMs that learn rules of miRNA-target site interaction automatically from data. We demonstrate that conditional profile HMMs detect the rules implemented into existing approaches from their predictions. And we show that a simple UTR model utilizing conditional profile HMMs predicts target genes of miR- NAs with a precision that is competitive compared to leading approaches, although it does not exploit cross-species conservation.
  • Konferenzbeitrag
    Quantitative comparison of genomic-wide protein domain distributions
    (German Conference on Bioinformatics 2010, 2010) Parikesit, Arli A.; Stadler, Peter F.; Prohaska, Sonja J.
    Investigations into the origins and evolution of regulatory mechanisms require quantitative estimates of the abundance and co-occurrence of functional protein domains among distantly related genomes. Currently available databases, such as the SUPERFAMILY, are not designed for quantitative comparisons since they are built upon transcript and protein annotations provided by the various different genome annotation projects. Large biases are introduced by the differences in genome annotation protocols, which strongly depend on the availability of transcript information and well-annotated closely related organisms. Here we show that the combination of de novo gene predictors and subsequent HMM-based annotation of SCOP domains in the predicted peptides leads to consistent estimates with acceptable accuracy that in particular can be utilized for systematic studies of the evolution of protein domain occurrences and co-occurrences. As an application, we considered four major classes of DNA binding domains: zink-finger, leucine-zipper, winged-helix, and HMG-box. We found that different types of DNA binding domains systematically avoid each other throughout the evolution of Eukarya. In contrast, DNA binding domains belonging to the same superfamily readily co-occur in the same protein.
  • Konferenzbeitrag
    Repeat-aware comparative genome assembly
    (German Conference on Bioinformatics 2010, 2010) Husemann, Peter; Stoye, Jens
    The current high-throughput sequencing technologies produce gigabytes of data even when prokaryotic genomes are processed. In a subsequent assembly phase, the generated overlapping reads are merged, ideally into one contiguous sequence. Often, however, the assembly results in a set of contigs which need to be stitched together with additional lab work. One of the reasons why the assembly produces several distinct contigs are repetitive elements in the newly sequenced genome. While knowing order and orientation of a set of non-repetitive contigs helps to close the gaps between them, special care has to be taken for repetitive contigs. Here we propose an algorithm that orders a set of contigs with respect to a related reference genome while treating the repetitive contigs in an appropriate way.