Motivation: A new technique, mammalian GFP reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To! characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy.

Results: We developed a fully-automatic method for detecting mGRASP-labeled synapse puncta. By modeling each punctum as a Gaussian distribution, our method enables accurate detection even when puncta of varying size and shape partially overlap. The method consists of three stages: blob detection by global thresholding; blob separation by watershed; and punctum modeling by a Variational Bayesian Gaussian Mixture Model. Extensive testing shows that the three-stage method improved detection accuracy markedly, and especially reduces under-segmentation. The method provides a goodness-of-fit score for each detected punctum, allowing efficient error detection. We applied this advantage to also develop an efficient interactive method for correcting errors.

Availability: The software is available on http! ://jinny.kist.re.kr

Motivation: The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut – normalized cut prime (FABS-NC′), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.

Results: We compare the performance of FABS-NC′ to other methods that could be used for drugs ranking. We devise two variants of the FABS algorithm: FABS-SVM that utilizes support vector machine (SVM) as black box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box. We compare the performance of FABS-NC′ also to three other methods that have been previously considered: center ranking (Center), PCA ranking (PCA), and graph transition energy method (GTEM). The conclusion is encouraging: FABS-NC′ consistently outperforms all these five alternatives. FABS-SVM has the second best performance among these six methods, but is far behind FABS-NC′: In some cases FABSNC ′ produces over half correctly predicted ranking experiment trials than FABS-SVM.

Availablility: The system and data for the evaluation reported here will be made available upon request to the authors after this manuscript is accepted for publication.

Motivation: Two types of transmembrane proteins exist, alpha-helical membrane proteins and transmembrane beta-barrels. The later type exists in the outer membrane of gram-negative bacteria and in chloroplast and mitochondria where they play a major role in the translocation machinery. Here, we aim to build three-dimensional models for transmembrane beta-barrels based on a large set of predicted topologies used to generate alternative three-dimensional models.Thereafter, the predicted Z-coordinate, i.e. the distance of a residue from the membrane center, is used to identify the best model.

Results: We present TMBMODEL; a method for generating three-dimensional models based on predicted topologies. TMBMODEL employs theoretic principles from known structures to construct a model for a barrel of a given transmembrane beta-barrel sequence. Firstly, different topologies are obtained from running the BOCTOPUS topology predictor and then three-dimensional models are constructed for different shear numbers. The best model is then selected based on a novel Z-coordinate predictor. Based on a leave-one-out cross-validation, the Z-coordinate predictor predicts 74% residues within 2 Å on a non-redundant dataset of 36 transmembrane beta-barrels. The average error and correctly identified membrane residues is 1.61 Å and 71%, respectively. TMBMODEL chose the correct topology for 75% proteins in the data set, which is a slight improvement over BOCTOPUS. More importantly TMBMODEL provides a C-alpha template for more detailed structural analysis. The average RMSD for this template is 7.24 Å.

Availability: TMBMODEL is freely available as a web-server at: http://tmbmodel.cbr.su.se/. The data sets used for training and evaluations are also available from this site.

Contact: arne@bioinfo.se

Abbreviations: TMB, transmembrane beta-barrel protein; HMM, Hidden
Markov Model; SVM, support vector machine.

We present DACTAL, a method for phylogeny estimation that produces trees from unaligned sequence datasets without ever needing to estimate an alignment on the entire dataset. DACTAL combines iteration with a novel divide-and-conquer approach, so that each iteration begins with a tree produced in the prior iteration, decomposes the taxon set into overlapping subsets, estimates trees on each subset, and then combines the smaller trees into a tree on the full taxon set using a new supertree method. We prove that DACTAL is guaranteed to produce the true tree under certain conditions. We compare DACTAL to SATe and maximum likelihood trees on estimated alignments using simulated and real datasets with 1000 to 27,643 taxa. Our studies show that DACTAL dramatically outperforms two-phase methods with respect to tree accuracy. The comparison to SAT\{e} shows that both have the same tree accuracy, but that DACTAL achieves this accuracy in a fraction of the time. Furthermore, DACTAL can analyze larger datasets than SAT\'{e}, including a dataset with almost 28,000 sequences.

DACTAL source code is available at www.cs.utexas.edu/users/phylo/software/dactal