UNLABELLED: PiNGO is a tool to screen biological networks for candidate genes, i.e. genes predicted to be involved in a biological process of interest. The user can narrow the search to genes with particular known functions or exclude genes belonging to particular functional classes. PiNGO provides support for a wide range of organisms and Gene Ontology classification schemes, and it can easily be customized for other organisms and functional classifications. PiNGO is implemented as a plugin for Cytoscape, a popular network visualization platform. AVAILABILITY: PiNGO is distributed as an open-source Java package under the GNU General Public License (http://www.gnu.org/), and can be downloaded via the Cytoscape plugin manager. A detailed user guide and tutorial are available on the PiNGO website (http://www.psb.ugent.be/esb/PiNGO.

UNLABELLED: Cytoscape is a popular bioinformatics package for biological network visualization and data integration. Version 2.8 introduces two powerful new features--Custom Node Graphics and Attribute Equations--which can be used jointly to greatly enhance Cytoscape's data integration and visualization capabilities. Custom Node Graphics allow an image to be projected onto a node, including images generated dynamically or at remote locations. Attribute Equations provide Cytoscape with spreadsheet-like functionality in which the value of an attribute is computed dynamically as a function of other attributes and network properties. AVAILABILITY AND IMPLEMENTATION: Cytoscape is a desktop Java application released under the Library Gnu Public License (LGPL). Binary install bundles and source code for Cytoscape 2.8 are available for download from http://cytoscape.org.

BACKGROUND: While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate. RESULTS: We compared near-optimal protein sequence alignments produced by the Zuker algorithm and a set of probabilistic alignments produced by the probA program with structural alignments produced by four different structure alignment algorithms. There is significant overlap between the solution spaces of structural alignments and both the near-optimal sequence alignments produced by commonly used scoring parameters for sequences that share significant sequence similarity (E-values < 10-5) and the ensemble of probA alignments. We constructed a logistic regression model incorporating three input variables derived from sets of near-optimal alignments: robustness, edge frequency, and maximum bits-per-position. A ROC analysis shows that this model more accurately classifies amino acid pairs (edges in the alignment path graph) according to the likelihood of appearance in structural alignments than the robustness score alone. We investigated various trimming protocols for removing incorrect edges from the optimal sequence alignment; the most effective protocol is to remove matches from the semi-global optimal alignment that are outside the boundaries of the local alignment, although trimming according to the model-generated probabilities achieves a similar level of improvement. The model can also be used to generate novel alignments by using the probabilities in lieu of a scoring matrix. These alignments are typically better than the optimal sequence alignment, and include novel correct structural edges. We find that the probA alignments sample a larger variety of alignments than the Zuker set, which more frequently results in alignments that are closer to the structural alignments, but that using the probA alignments as input to the regression model does not increase performance. CONCLUSIONS: The pool of suboptimal pairwise protein sequence alignments substantially overlaps structure-based alignments for pairs with statistically significant similarity, and a regression model based on information contained in this alignment pool improves the accuracy of pairwise alignments with respect to structure-based alignments.

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment--previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches--yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/.

UNLABELLED: The identification of protein complexes is a fundamental challenge in interpreting protein-protein interaction data. Cross-species analysis allows coping with the high levels of noise that are typical to these data. The NetworkBLAST web-server provides a platform for identifying protein complexes in protein-protein interaction networks. It can analyze a single network or two networks from different species. In the latter case, NetworkBLAST outputs a set of putative complexes that are evolutionarily conserved across the two networks. AVAILABILITY: NetworkBLAST is available as web-server at: www.cs.tau.ac.il/~roded/networkblast.htm.

MOTIVATION: Mathematically optimal alignments do not always properly align active site residues or well-recognized structural elements. Most near-optimal sequence alignment algorithms display alternative alignment paths, rather than the conventional residue-by-residue pairwise alignment. Typically, these methods do not provide mechanisms for finding effectively the most biologically meaningful alignment in the potentially large set of options. RESULTS: We have developed Web-based software that displays near optimal or alternative alignments of two protein or DNA sequences as a continuous moving picture. A WWW interface to a C++ program generates near optimal alignments, which are sent to a Java Applet, which displays them in a series of alignment frames. The Applet aligns residues so that consistently aligned regions remain at a fixed position on the display, while variable regions move. The display can be stopped to examine alignment details.