Plasmids are important members of the bacterial mobile gene pool, and are among the most important contributors to horizontal gene transfer between bacteria. They typically harbour a wide spectrum of host beneficial traits, such as antibiotic resistance, inserted into their backbones. Although these inserted elements have drawn considerable interest, evolutionary information about the plasmid backbones, which encode plasmid related traits, is sparse. Here we analyse 25 complete backbone genomes from the broad-host-range IncP-1 plasmid family. Phylogenetic analysis reveals seven clades, in which two plasmids that we isolated from a marine biofilm represent a novel clade. We also found that homologous recombination is a prominent feature of the plasmid backbone evolution. Analysis of genomic signatures indicates that the plasmids have adapted to different host bacterial species. Globally circulating IncP-1 plasmids hence contain mosaic structures of segments derived from several parental plasmids that have evolved in, and adapted to, different, phylogenetically very distant host bacterial species.
This paper presents a novel method for entity disambiguation in anonymized graphs using local neighborhood structure. Most existing approaches leverage node information, which might not be available in several contexts due to privacy concerns, or information about the sources of the data. We consider this problem in the supervised setting where we are provided only with a base graph and a set of nodes labelled as ambiguous or unambiguous. We characterize the similarity between two nodes based on their local neighborhood structure using graph kernels; and solve the resulting classification task using SVMs. We give empirical evidence on two real-world datasets, comparing our approach to a state-of-the-art method, highlighting the advantages of our approach. We show that using less information, our method is significantly better in terms of either speed or accuracy or both. We also present extensions of two existing graphs kernels, namely, the direct product kernel and the shortestpath kernel, with significant improvements in accuracy. For the direct product kernel, our extension also provides significant computational benefits. Moreover, we design and implement the algorithms of our method to work in a distributed fashion using the GraphLab framework, ensuring high scalability.
This paper considers the problem of finding large dense subgraphs in relational graphs, i.e., a set of graphs which share a common vertex set. We present an approximation algorithm for finding the densest common subgraph in a relational graph set based on an extension of Charikar's method for finding the densest subgraph in a single graph. We also present a simple greedy heuristic which can be implemented efficiently for analysis of larger graphs. We give graph dependent bounds on the quality of the solutions returned by our methods. Lastly, we show by empirical evaluation on several benchmark datasets that our method out-performs existing approaches.
The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.
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