UNLABELLED: Prions are self-templating protein aggregates that stably perpetuate distinct biological states and are of keen interest to researchers in both evolutionary and biomedical science. The best understood prions are from yeast and have a prion-forming domain with strongly biased amino acid composition, most notably enriched for Q or N. PLAAC is a web application that scans protein sequences for domains with P: rion- L: ike A: mino A: cid C: omposition. Users can upload sequence files, or paste sequences directly into a textbox. PLAAC ranks the input sequences by several summary scores and allows scores along sequences to be visualized. Text output files can be downloaded for further analyses, and visualizations saved in PDF and PNG formats. AVAILABILITY AND IMPLEMENTATION: http://plaac.wi.mit.edu/. The Ruby-based web framework and the command-line software (implemented in Java, with visualization routines in R) are available at http://github.com/whitehead/plaac under the MIT license. All software can be run under OS X, Windows and Unix.

BACKGROUND: Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship. RESULTS: Using a supervised machine learning approach--probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP) of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue), a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database), and the remaining predictions may potentially represent unknown CCPs. CONCLUSIONS: We demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP) pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.

SUMMARY: FuncAssociate is a web-based tool to help researchers use Gene Ontology attributes to characterize large sets of genes derived from experiment. Distinguishing features of FuncAssociate include the ability to handle ranked input lists, and a Monte Carlo simulation approach that is more appropriate to determine significance than other methods, such as Bonferroni or idák p-value correction. FuncAssociate currently supports 10 organisms (Vibrio cholerae, Shewanella oneidensis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Caenorhaebditis elegans, Drosophila melanogaster, Mus musculus, Rattus norvegicus and Homo sapiens). AVAILABILITY: FuncAssociate is freely accessible at http://llama.med.harvard.edu/Software.html. Source code (in Perl and C) is freely available to academic users 'as is'.

Abstract Summary: GoFish is a Java application that allows users to search for gene products with particular gene ontology (GO) attributes, or combinations of attributes. GoFish ranks gene products by the degree to which they satisfy a Boolean query. Four organisms are currently supported: Saccaromyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and M.musculus. Availability: GoFish can be used freely through the www as a Java applet for Windows, Mac OS X, or Unix: http://llama.med.harvard.edu/Software.html. Contact: [email protected] * To whom correspondence should be addressed.

We determine the mass of extremal doubly-even self-dual binary codes of length 40. It follows that there are at least 12579 such codes.

In this paper, two-dimensional (2-D) motion estimation techniques based on the well-known optical-flow equation are treated. Cost functions are defined for various motion models that allow the optical flow equation (OFE) to be used in an optimization framework for the purpose of estimating the 2-D motion parameters. A method of extending these cost functions to include camera parameters is then proposed. The resulting sometimes non-linear optimization framework takes into account motion-model parameter estimation in the presence of geometric lens distortions. Linearization, when necessary, results in an OFE-like motion estimation method.