Publications by authors named "Manuel Stark"

Background: Essential genes are absolutely required for the survival of an organism. The identification of essential genes, besides being one of the most fundamental questions in biology, is also of interest for the emerging science of synthetic biology and for the development of novel antimicrobials. New antimicrobial therapies are desperately needed to treat multidrug-resistant pathogens, such as members of the Burkholderia cepacia complex.

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Microorganisms often form symbiotic relationships with eukaryotes, and the complexity of these relationships can range from those with one single dominant symbiont to associations with hundreds of symbiont species. Microbial symbionts occupying equivalent niches in different eukaryotic hosts may share functional aspects, and convergent genome evolution has been reported for simple symbiont systems in insects. However, for complex symbiont communities, it is largely unknown how prevalent functional equivalence is and whether equivalent functions are conducted by evolutionarily convergent mechanisms.

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The above- and below-ground parts of rice plants create specific habitats for various microorganisms. In this study, we characterized the phyllosphere and rhizosphere microbiota of rice cultivars using a metaproteogenomic approach to get insight into the physiology of the bacteria and archaea that live in association with rice. The metaproteomic datasets gave rise to a total of about 4600 identified proteins and indicated the presence of one-carbon conversion processes in the rhizosphere as well as in the phyllosphere.

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The pathogen Salmonella Typhimurium is a common cause of diarrhea and invades the gut tissue by injecting a cocktail of virulence factors into epithelial cells, triggering actin rearrangements, membrane ruffling and pathogen entry. One of these factors is SopE, a G-nucleotide exchange factor for the host cellular Rho GTPases Rac1 and Cdc42. How SopE mediates cellular invasion is incompletely understood.

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An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein-protein interaction information can be error-prone and require considerable effort to annotate.

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Background: Shotgun sequencing of environmental DNA is an essential technique for characterizing uncultivated microbes in situ. However, the taxonomic and functional assignment of the obtained sequence fragments remains a pressing problem.

Results: Existing algorithms are largely optimized for speed and coverage; in contrast, we present here a software framework that focuses on a restricted set of informative gene families, using Maximum Likelihood to assign these with the best possible accuracy.

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Functional partnerships between proteins are at the core of complex cellular phenotypes, and the networks formed by interacting proteins provide researchers with crucial scaffolds for modeling, data reduction and annotation. STRING is a database and web resource dedicated to protein-protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a meta-database that maps all interaction evidence onto a common set of genomes and proteins.

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