SUMMARY: DeepPheWAS is an R package for phenome-wide association studies that creates clinically curated composite phenotypes and integrates quantitative phenotypes from primary care data, longitudinal trajectories of quantitative measures, disease progression and drug response phenotypes. Tools are provided for efficient analysis of association with any genetic input, under any genetic model, with optional sex-stratified analysis, and for developing novel phenotypes. AVAILABILITY AND IMPLEMENTATION: The DeepPheWAS R package is freely available under GNU general public licence v3.0 from at https://github.com/Richard-Packer/DeepPheWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date. METHODS: We present two novel methods for identifying associated pathways. In the proposed hierarchical model, the SNP effects are analytically integrated out of the analysis, allowing computationally tractable model fitting to genome-wide data. The first method uses Bayes factors for calculating the effect of the pathways, whereas the second method uses a machine learning algorithm and adaptive lasso for finding a sparse solution of associated pathways. RESULTS: The performance of the proposed methods was explored on both simulated and real data. The results of the simulation study showed that the methods outperformed some well-established association methods: the commonly used Fisher's method for combining P-values and also the recently published BGSA. The methods were applied to two genome-wide association study datasets that aimed to find the genetic structure of platelet function and body mass index, respectively. The results of the analyses replicated the results of previously published pathway analysis of these phenotypes but also identified novel pathways that are potentially involved. AVAILABILITY: An R package is under preparation. In the meantime, the scripts of the methods are available on request from the authors.

BACKGROUND: This paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters. RESULTS: Protein interaction graphs were separated into subgraphs of interconnected proteins, using the JUNG implementation of Girvan and Newman's Edge-Betweenness algorithm. Functions were sought for these subgraphs by detecting significant correlations with the distribution of Gene Ontology terms which had been used to annotate the proteins within each cluster. The method was implemented using freely available software (JUNG and the R statistical package). Protein clusters with significant correlations to functional annotations could be identified and included groups of proteins know to cooperate in cell metabolism. The method appears to be resilient against the presence of false positive interactions. CONCLUSION: This method provides a useful tool for rapid screening of small to medium size protein interaction datasets.

SUMMARY: Linkage analysis software requires an input text file that describes the structure of the pedigrees to be analysed. Manual creation of these files is tedious and error-prone, and a graphical input tool is desirable. This is currently only available in commercial packages that include much greater functionality. We have therefore developed Pelican, a lightweight graphical pedigree editor for rapid construction of linkage pedigree files and diagrams. AVAILABILITY: The software runs on any Java-enabled machine (version 1.2 or higher). A Java Web Start launch, class files, a demonstration applet, source code and documentation are freely available at http://www.rfcgr.mrc.ac.uk/Software/PELICAN/

A method for block coding of images based on a least squares fractal approximation by a self-affine system (SAS) is presented. The computational cost of the approximation is linear in the number of pixels in the image. The approximation to a rectangularly tiled block involves evaluating various low-order moments over the block, and solving a system of four linear equations for each tile. The method is applied to a standard test image and the effects of various optimizations are shown. A quantitative comparison with the adaptive discrete cosine transform at 8:1 compression is made. The fidelity of the fractal method shows promise and its greater speed and simplicity compared to other fractal transforms suggest immediate applications such as interactive browsing of remote image archives or image representation in multimedia systems.>