1,721,036 research outputs found
Searching for repetitions in biological networks: methods, resources and tools
Full text linkWe present here a compact overview of the data, models and methods proposed for the analysis of biological networks based on the search for significant repetitions. In particular, we concentrate on three problems widely studied in the literature: 'network alignment', 'network querying' and 'network motif extraction'. We provide (i) details of the experimental techniques used to obtain the main types of interaction data, (ii) descriptions of the models and approaches introduced to solve such problems and (iii) pointers to both the available databases and software tools. The intent is to lay out a useful roadmap for identifying suitable strategies to analyse cellular data, possibly based on the joint use of different interaction data types or analysis techniques
In vitro evolution of recognition specificity mediated by SH3 domains reveals target recognition rules
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A technique to search functional similarities in PPI networks
No full textWe describe a method to search for similarities across protein-protein interaction networks of different organisms. The technique core consists in computing a maximum weight matching of bipartite graphs resulting from comparing the neighbourhoods of proteins belonging to different networks. Both quantitative and reliability information are exploited. We tested the method on the networks of S. cerevisiae, D. melanogaster and C. elegans. The experiments showed that the technique is able to detect functional orthologs when the sole sequence similarity does not prove itself sufficient. They also demonstrated the capability of our approach in discovering common biological processes involving uncharacterised proteins
Bi-GRAPPIN: Bipartite graph based protein-protein interaction networks similarity search
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Asymmetric Comparison and Querying of Biological Networks
No full textComparing and querying the protein-protein interaction (PPI) networks of different organisms is important to infer
knowledge about conservation across species. Known methods that perform these tasks operate symmetrically, i.e., they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how the corresponding organism is biologically well characterized. In this paper a new idea is developed, that is, to exploit differences in the characterization of organisms at hand in order to devise methods for comparing their PPI networks. We use the PPI network (called Master) of the best characterized organism as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master network. Technically, this is obtained by generating from the Master a finite automaton, called alignment model, which is then fed with (a linearization of) the Slave for the
purpose of extracting, via the Viterbi algorithm, matching subgraphs. We propose an approach able to perform global alignment and network querying, and we apply it on PPI networks. We tested our method showing that the results it returns are biologically relevant
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