UNLABELLED: NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. AVAILABILITY: The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch.

BACKGROUND: Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by multiple codons. The common approach to overcome this difficulty is based on imitation of codon usage within the target species. RESULTS: This paper describes EasyBack, a new parameter-free, fully-automated software for backtranslation using Hidden Markov Models. EasyBack is not based on imitation of codon usage within the target species, but instead uses a sequence-similarity criterion. The model is trained with a set of proteins with known cDNA coding sequences, constructed from the input protein by querying the NCBI databases with BLAST. Unlike existing software, the proposed method allows the quality of prediction to be estimated. When tested on a group of proteins that show different degrees of sequence conservation, EasyBack outperforms other published methods in terms of precision. CONCLUSION: The prediction quality of a protein backtranslation methis markedly increased by replacing the criterion of most used codon in the same species with a Hidden Markov Model trained with a set of most similar sequences from all species. Moreover, the proposed method allows the quality of prediction to be estimated probabilistically.

In this paper a simple and efficient distributed version of the introduced antipole clustering algorithm for general metric spaces is proposed. This combines ideas from the M-tree, the multi-vantage point structure and the FQ-tree to create a new structure in the "bisector tree" class, called the antipole tree. Bisection is based on the proximity to an "antipole" pair of elements generated by a suitable linear randomized tournament. The final winners (A, B) of such a tournament are far enough apart to approximate the diameter of the splitting set. A simple linear algorithm computing antipoles in Euclidean spaces with exponentially small approximation ratio is proposed. The antipole tree clustering has been shown to be very effective in important applications such as range and k-nearest neighbor searching, mobile objects clustering in centralized wireless networks with movable base stations and multiple alignment of biological sequences. In many of such applications an efficient distributed clustering algorithm is needed. In the proposed distributed versions of antipole clustering the amount of data passed from one node to another is either constant or proportional to the number of nodes in the network. The distributed antipole tree is equipped with additional information in order to perform efficient range search and dynamic clusters management. This is achieved by adding to the randomized tournaments technique, methodologies taken from established systems such as BFR and BIRCH*. Experiments show the good performance of the proposed algorithms on both real and synthetic data