PANET: A GPU-Based Tool for Fast Parallel Analysis of Robustness Dynamics and Feed-Forward/Feedback Loop Structures in Large-Scale Biological Networks

It has been a challenge in systems biology to unravel relationships between structural properties and dynamic behaviors of biological networks. A Cytoscape plugin named NetDS was recently proposed to analyze the robustness-related dynamics and feed-forward/feedback loop structures of biological networks. Despite such a useful function, limitations on the network size that can be analyzed exist due to high computational costs. In addition, the plugin cannot verify an intrinsic property which can be induced by an observed result because it has no function to simulate the observation on a large number of random networks. To overcome these limitations, we have developed a novel software tool, PANET. First, the time-consuming parts of NetDS were redesigned to be processed in parallel using the OpenCL library. This approach utilizes the full computing power of multi-core central processing units and graphics processing units. Eventually, this made it possible to investigate a large-scale network such as a human signaling network with 1,609 nodes and 5,063 links. We also developed a new function to perform a batch-mode simulation where it generates a lot of random networks and conducts robustness calculations and feed-forward/feedback loop examinations of them. This helps us to determine if the findings in real biological networks are valid in arbitrary random networks or not. We tested our plugin in two case studies based on two large-scale signaling networks and found interesting results regarding relationships between coherently coupled feed-forward/feedback loops and robustness. In addition, we verified whether or not those findings are consistently conserved in random networks through batch-mode simulations. Taken together, our plugin is expected to effectively investigate various relationships between dynamics and structural properties in large-scale networks. Our software tool, user manual and example datasets are freely available at http://panet-csc.sourceforge.net/.     

GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.     

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year's time to complete the same task.     

Quick Construction of Femoral Model Using Surface Feature Parameterization

To facilitate the modifying of femoral surface model, by dividing the femoral mesh into surface feature units bearing medical significance based on surface feature technology, a new approach of constructing femoral models using surface feature technology is proposed. Firstly, considering of femoral anatomy, the femoral triangle mesh model generated from the averaged point-clouds is divided into several specific regions, which are called feature regions; Secondly, feature parameters are defined and the constraints among them are set up, and feature surfaces are created by skinning the contours; Finally, the adjacent feature surfaces are connected by transition surfaces, and the parametric CAD surface model of femur is constructed. Experimental results show that, with the proposed method, the surface feature model can be intuitively constructed and edited with high-level parameters. Therefore, the proposed method provides a basic tool for the design of implants and the digital restoration of incomplete femurs.     

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Cytosine DNA methylation is an epigenetic mark implicated in several biological processes. Bisulfite treatment of DNA is acknowledged as the gold standard technique to study methylation. This technique introduces changes in the genomic DNA by converting cytosines to uracils while 5-methylcytosines remain nonreactive. During PCR amplification 5-methylcytosines are amplified as cytosine, whereas uracils and thymines as thymine. To detect the methylation levels, reads treated with the bisulfite must be aligned against a reference genome. Mapping these reads to a reference genome represents a significant computational challenge mainly due to the increased search space and the loss of information introduced by the treatment. To deal with this computational challenge we devised GPU-BSM, a tool based on modern Graphics Processing Units. Graphics Processing Units are hardware accelerators that are increasingly being used successfully to accelerate general-purpose scientific applications. GPU-BSM is a tool able to map bisulfite-treated reads from whole genome bisulfite sequencing and reduced representation bisulfite sequencing, and to estimate methylation levels, with the goal of detecting methylation. Due to the massive parallelization obtained by exploiting graphics cards, GPU-BSM aligns bisulfite-treated reads faster than other cutting-edge solutions, while outperforming most of them in terms of unique mapped reads.     

A Mutant Sumo Facilitates Quick Plasmid Construction for Expressing Proteins with Native N-termini After Tag Removal

Sumo is one of the fusion tags commonly used to enhance the expression and the solubility of recombinant proteins. One advantage of using sumo is that the removal of the sumo tag is highly specific because its recognition by a sumo protease is determined by its structural characteristics, instead of the sequence of a short peptide. Recently, it was reported that sumo could also be used as a protease recognition site to facilitate the removal of other fusion tags, such as MBP, when sumo itself is not suitable to enhance the solubility of a particular target protein. Using sumo as a recognition site is highly desirable when the target protein needs to have its native N terminus. However, constructing such a plasmid involves more than one cloning step because the N terminus of the target protein needs to be the next residue after the diglycine of sumo. Here, we report the construction of a new vector with a mutant sumo tag. The incorporation of a Pvu II site near the 3′ end of tag coding sequence enables quick construction of plasmids for producing proteins with native termini. Its usage includes producing recombinant food allergens for studying conformational IgE epitopes.     

Quick, Imputation-free meta-analysis with proxy-SNPs

ABSTRACT: BACKGROUND: Meta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increasethe power to detect strong or weak genotype effects or b) as a result verification method. As a consequence ofdiffering SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia toavoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software), however, enablescross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming. RESULTS: Here we present a fast method to avoid forfeiting SNPs present in only a subset of studies, without relying onimputation. This is accomplished by using reference linkage disequilibrium data from 1,000Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least onestudy. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Ouralgorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applicationscan be used as input files for MA, tremendously speeding up MA compared to the conventional imputationapproach. We show that our proxy algorithm is well-powered and yields valuable ad hoc results, possiblyproviding an incentive for follow-up studies. We propose our method as a quick screening step prior toimputation-based MA, as well as an additional main approach for studies without available reference datamatching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II DiabetesGWAS and found that the proxy algorithm clearly outperforms naive MA on the P-value level: for 17 out of23 we observe an improvement on the p-value level by a factor of more than two, and a maximumimprovement by a factor of 2127. CONCLUSIONS: YAMAS is an efficient and fast meta-analysis program which offers various methods, including conventionalMA as well as inserting proxy-SNPs for missing markers to avoid unnecessary power loss. MA with YAMAScan be readily conducted as YAMAS provides a generic parser for heterogeneous tabulated file formats withinthe GWAS field and avoids cumbersome setups. In this way, it supplements the meta-analysis process.     

A Fast Flexible Docking Method using an Incremental Construction Algorithm

We present an automatic method for docking organic ligands into protein binding sites. The method can be used in the design process of specific protein ligands. It combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand. If the ligand is flexible, it can adopt a large variety of different conformations. Each such minimum in conformational space presents a potential candidate for the conformation of the ligand in the complexed state. Our docking method samples the conformation space of the ligand on the basis of a discrete model and uses a tree-search technique for placing the ligand incrementally into the active site. For placing the first fragment of the ligand into the protein, we use hashing techniques adapted from computer vision. The incremental construction algorithm is based on a greedy strategy combined with efficient methods for overlap detection and for the search of new interactions. We present results on 19 complexes of which the binding geometry has been crystallographically determined. All considered ligands are docked in at most three minutes on a current workstation. The experimentally observed binding mode of the ligand is reproduced with 0.5 to 1.2 Å rms deviation. It is almost always found among the highest-ranking conformations computed.     

Relaxed Neighbor Joining: A Fast Distance-Based Phylogenetic Tree Construction Method

Our ability to construct very large phylogenetic trees is becoming more important as vast amounts of sequence data are becoming readily available. Neighbor joining (NJ) is a widely used distance-based phylogenetic tree construction method that has historically been considered fast, but it is prohibitively slow for building trees from increasingly large datasets. We developed a fast variant of NJ called relaxed neighbor joining (RNJ) and performed experiments to measure the speed improvement over NJ. Since repeated runs of the RNJ algorithm generate a superset of the trees that repeated NJ runs generate, we also assessed tree quality. RNJ is dramatically faster than NJ, and the quality of resulting trees is very similar for the two algorithms. The results indicate that RNJ is a reasonable alternative to NJ and that it is especially well suited for uses that involve large numbers of taxa or highly repetitive procedures such as bootstrapping. [Reviewing Editor: Dr. James Bull]     

Interaction of hirudin with the dysthrombins Quick I and II

The interaction of hirudin with the dysfunctional enzymes thrombin Quick I and II has been investigated. Natural and recombinant hirudin caused nonlinear competitive inhibition of thrombin Quick I. The results were consistent with thrombin Quick I existing in two forms that have different affinities for hirudin. The affinities of these forms for natural hirudin were respectively 10(4)- and 10(6)-fold lower than that of alpha-thrombin. In contrast, truncated hirudin molecules lacking the C-terminal tail of the molecule caused linear inhibition of thrombin Quick I. These results indicate that different modes of interaction of the two forms of thrombin Quick I with the C-terminal tail of hirudin were the cause of the nonlinear inhibition. Comparison of the dissociation constants of thrombin Quick I with the truncated and full-length forms of hirudin suggested that the interactions that normally occur between the C-terminal tail of hirudin and thrombin were completely disrupted with the low-affinity form of thrombin Quick I. Thrombin Quick II displayed an affinity for natural hirudin that was 10(3)-fold lower than that observed with alpha-thrombin. In contrast, it bound a mutant hirudin with altered N-terminal amino acids only 16-fold less tightly. These results are discussed in terms of structural alterations in the active-site cleft in thrombin Quick II.     

Quick Decline of Macadamia Trees: Association with Phytophthora capsici

Some macadamia trees in commercial orchards in Hawaii showing quick decline syndrome had bleeding from the trunk. Phytophthora capsici was isolated from c. 67% of such sites, and was shown to kill branches of artificially-inoculated healthy macadamia trees. The pathogen was detected in both diseased and apparently healthy bark and isolated from wood 80 mm away from the bark. Results suggest that trunk infection by P. capsici may lead to girdling and rapid decline, and attract insects which then cause some bleeding by making wounds at the sites of recent infection.