Cloud computing is currently on everyone’s lips – it is of interest to researchers (to speed up computations), managers (to reduce costs), and also the general public (to access music everywhere). The information technology research and advisory firm Gartner Inc., for example, announced that cloud computing will be one out of ten top information technology topics in the next years. At least in 2015 cloud computing is predicted to be a mass product and will likely be the preferred solution for many application areas [1]. In view of these developments, the idea of cloud computing sounds promising for the communities of Medical Biometry and Medical Informatics as well. Possible benefits include cost reductions in hardware, maintenance and software licensing, access to latest software and hardware for everyone from any place in the world, standardized and centralized analyses platforms, and new ways of collaboration between researchers and practitioners. Biomedical research, as many other scientific disciplines, currently experiences the increased focus on high performance computing (HPC) – mainly driven by the three aspects of 1) larger data sets and the “big data” hype, 2) increased computational requirements stemming from more sophisticated statistical methodologies, and 3) new computing environments including multi-core processors and the use of graphics processing units to do general purpose computing. On this account, the German working group for Statistical Computing of the German Region of the International Biometric Society (IBS-DR) and the German Association of Medical Informatics, Biometry and Epidemiology (GMDS) organized two workshops in the year 2011 (March 23 in Munich, and September 27 in Mainz). In the course of these meetings, whose organizing committees consisted of the authors of this editorial, leading community experts discussed how HPC technologies can be used to improve and accelerate biomedical analysis. The present Focus Theme summarizes the results and the outcomes of the workshops. Specifically, the four papers highlight a selection of benefits and challenges for grid and cloud computing, and the general use of graphic processors in the communities of Medical Biometry and Medical Informatics. Additionally, they contain evaluation of technical solutions and business model aspects. Before going into details about the four papers, we briefly introduce common terms and relations of HPC technologies.

The application of Software Engineering (SE) techniques in High Performance Computing (HPC) is an often discussed and difficult issue. The 'as a service' model approach rising from latest cloud computing trends provides an promising way to offer appropriate software engineering support for high performance computing. This paper presents and discusses the idea and concept for a Software Engineering as a Service (SEaaS) platform for HPC. It is designed to enhance efficient problem solving and implementation, to deal with limited resources, and to guide interdisciplinary teams. The service supports agile SE techniques, can be adapted for the depending HPC environment, is user optimized based on the preferred programming language and will be implemented as an open-source and app-like framework. The implementation and evaluation of the SEaaS app is work in progress.

As an operational definition, we refer to regions in proteins that do not adopt regular three-dimensional structures in isolation, as disordered regions. An antipode to disorder would be 'well-structured' rather than 'ordered'. Here, we argue for the following three hypotheses. Firstly, it is more useful to picture disorder as a distinct phenomenon in structural biology than as an extreme example of protein flexibility. Secondly, there are many very different flavors of protein disorder, nevertheless, it seems advantageous to portray the universe of all possible proteins in terms of two main types: well-structured, disordered. There might be a third type 'other' but we have so far no positive evidence for this. Thirdly, nature uses protein disorder as a tool to adapt to different environments. Protein disorder is evolutionarily conserved and this maintenance of disorder is highly nontrivial. Increasingly integrating protein disorder into the toolbox of a living cell was a crucial step in the evolution from simple bacteria to complex eukaryotes. We need new advanced computational methods to study this new milestone in the advance of protein biology.

Due to the increasing availability of powerful hardware resources, parallel computing is becoming an important issue, as a noticeable speedup may be achieved. The statistical programming language R allows for parallel computing on computer clusters as well as multicore systems through several packages. This tutorial gives a short, practical overview of four, in view of the authors, important packages for parallel computing in R, namely multicore, snow, snowfall and nws. First, the general principle of parallelizing simple tasks is briefly illustrated based on a statistical cross-validation example. Afterwards, the usage of each of the introduced packages is being demonstrated on the example. Furthermore, we address some specific features of the packages and provide guidance for selecting an adequate package for the computing environment at hand.

Large public repositories of microarray experiments offer an abundance of biological data. It is of interest to use and to combine the available material to create new biological information and to develop a broader view on biological phenomena.Meta-analyses recombine similar information over a series of experiments to sketch scientific aspects which were not accessible by each of the single experiments. Meta-analysis of high-throughput experiments has to handle methodological as well as technical challenges. Methodological aspects concern the identification of homogeneous material which can be combined by appropriate statistical procedures. Technical challenges come from the data management of large amounts of high-dimensional data, long computation time, as well as the handling of the stored phenotype data.This paper compares in a meta-analysis of a large series of microarray experiments the interaction structure within selected pathways between different tumour entities. The feasibility of such a study is explored and a technical as well as a statistical framework for its completion is presented. Multiple obstacles were met during completion of this project. They are mainly related to the quality of the available data and influence the biological interpretation of the results derived.The sobering experience of our study asks for combined efforts to improve the data quality in public repositories of high-throughput data. The exploration of the available data in large meta-analyses is limited by incomplete documentation of essential aspects of experiments and studies, by technical deficiencies in the data stored, and by careless duplications of data.

As microarray data quality can affect each step of the microarray analysis process, quality assessment and control is an integral part. It detects divergent measurements beyond the acceptable level of random fluctuations. This empirical study identifies association and correlation between the six quality assessment methods for microarray outlier detection used in the arrayQualityMetrics package version 2.2.2. For evaluation two different agreement tests—Cohen’s Kappa, after a homogeneity marginal criteria, and AC1 Statistic—, the Pearson Correlation Coefficient and realistic microarray data from the public ArrayExpress database have been used. It is possible to assess the quality of a data set using only four of the six currently proposed statistical methods to comprehensively quantify the quality information in large series of microarrays. This saves computation time and reduces decision complexity for the analyst. The new proposed rule is validated with data sets from biomedical studies.

R is an open-source programming language and software environment for statistical computing and graphics. During the last decade a great deal of research has been conducted on parallel computing techniques with the R language. Two packages (snow and Rmpi) stand out as particularly useful for general use on computer clusters and the multicore package for the use on multi-core machines.

A strategy for testing differential conditional independence structures (CIS) between two graphs is introduced. The graphs have the same set of nodes and are estimated from data sampled under two different conditions. The test uses the entire pathplot in a Lasso regression as the information on how a node connects with the remaining nodes in the graph.The interpretation of the paths as random processes allows defining stopping times which make the statistical properties of the test statistic accessible to analytic reasoning. A resampling approach is proposed to calculated p-values simultaneously for a hierarchical testing procedure. The hierarchical testing steps through a given hierarchy of clusters. First, collective effects are measured at the coarsest level possible (the global null hypothesis that no node in the graph shows a differential CIS). If the global nul I hypothesis can be rejected, finer resolution levels are tested for an effect until the level of individual nodes is reached.The strategy is applied to association patterns of categories from the ICF in patients under post-acute rehabilitation. The patients are characterized by two different conditions. A comprehensive understanding of differences in the conditional independence structures between the patient groups is pivotal for evidence-based intervention design on the policy, the service and the clinical level related to their treatment. Due to extensive computation, parallel computing offers an effective approach to implement our explorative tool and to locate nodes in a graph which show differential CIS between two conditions.

In the 1990s a number of technological innovations appeared that revolutionized biology, and 'Bioinformatics' became a new scientific discipline. Microarrays can measure the abundance of tens of thousands of mRNA species, data on the complete genomic sequences of many different organisms are available, and other technologies make it possible to study various processes at the molecular level. In Bioinformatics and Biostatistics, current research and computations are limited by the available computer hardware. However, this problem can be solved using high-performance computing resources. There are several reasons for the increased focus on high-performance computing: larger data sets, increased computational requirements stemming from more sophisticated methodologies, and latest developments in computer chip production. The open-source programming language 'R' was developed to provide a powerful and extensible environment for statistical and graphical techniques. There are many good reasons for preferring R to other software or programming languages for scientific computations (in statistics and biology). However, the development of the R language was not aimed at providing a software for parallel or high-performance computing. Nonetheless, during the last decade, a great deal of research has been conducted on using parallel computing techniques with R. This PhD thesis demonstrates the usefulness of the R language and parallel computing for biological research. It introduces parallel computing with R, and reviews and evaluates existing techniques and R packages for parallel computing on Computer Clusters, on Multi-Core Systems, and in Grid Computing. From a computer-scientific point of view the packages were examined as to their reusability in biological applications, and some upgrades were proposed. Furthermore, parallel applications for next-generation sequence data and preprocessing of microarray data were developed. Microarray data are characterized by high levels of noise and bias. As these perturbations have to be removed, preprocessing of raw data has been a research topic of high priority over the past few years. A new Bioconductor package called affyPara for parallelized preprocessing of high-density oligonucleotide microarray data was developed and published. The partition of data can be performed on arrays using a block cyclic partition, and, as a result, parallelization of algorithms becomes directly possible. Existing statistical algorithms and data structures had to be adjusted and reformulated for the use in parallel computing. Using the new parallel infrastructure, normalization methods can be enhanced and new methods became available. The partition of data and distribution to several nodes or processors solves the main memory problem and accelerates the methods by up to the factor fifteen for 300 arrays or more. The final part of the thesis contains a huge cancer study analysing more than 7000 microarrays from a publicly available database, and estimating gene interaction networks. For this purpose, a new R package for microarray data management was developed, and various challenges regarding the analysis of this amount of data are discussed. The comparison of gene networks for different pathways and different cancer entities in the new amount of data partly confirms already established forms of gene interaction.

R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing.This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance.Two packages (snow, Rmpi) stand out as particularly suited to general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems five different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix.

Biochemical reaction networks in living cells usually involve reversible covalent modification of signaling molecules, such as protein phosphorylation. Under conditions of small molecule numbers, as is frequently the case in living cells, mass-action theory fails to describe the dynamics of such systems. Instead, the biochemical reactions must be treated as stochastic processes that intrinsically generate concentration fluctuations of the chemicals. We investigate the stochastic reaction kinetics of covalent modification cycles (CMCs) by analytical modeling and numerically exact Monte Carlo simulation of the temporally fluctuating concentration. Depending on the parameter regime, we find for the probability density of the concentration qualitatively distinct classes of distribution functions including power-law distributions with a fractional and tunable exponent. These findings challenge the traditional view of biochemical control networks as deterministic computational systems and suggest that CMCs in cells can function as versatile and tunable noise generators.

Microarray data repositories as well as large clinical applications of gene expression allow to analyse several hundreds of microarrays at one time. The preprocessing of large amounts of microarrays is still a challenge. The algorithms are limited by the available computer hardware. For example, building classification or prognostic rules from large microarray sets will be very time consuming. Here, preprocessing has to be a part of the cross-validation and resampling strategy which is necessary to estimate the rule's prediction quality honestly.This paper proposes the new Bioconductor package affyPara for parallelized preprocessing of Affymetrix microarray data. Partition of data can be applied on arrays and parallelization of algorithms is a straightforward consequence. The partition of data and distribution to several nodes solves the main memory problems and accelerates preprocessing by up to the factor 20 for 200 or more arrays.affyPara is a free and open source package, under GPL license, available form the Bioconductor project at www.bioconductor.org. A user guide and examples are provided with the package.

Studies of gene expression using high-density oligonucleotide microarrays have become standard in a variety of biological contexts. The data recorded using the microarray technique are characterized by high levels of noise and bias. These failures have to be removed, therefore preprocessing of raw data has been a research topic of high priority over the past few years. Actual research and computations are limited by the available computer hardware. Furthermore most of the existing preprocessing methods are very time consuming. To solve these problems, the potential of parallel computing should be used. For parallelization on multicomputers, the communication protocol MPI (message passing interface) and the R language will be used. This paper proposes the new R language package affyPara for parallelized preprocessing of high-density oligonucleotide microarray data. Partition of data could be done on arrays and therefore parallelization of algorithms gets intuitive possible. The partition of data and distribution to several nodes solves the main memory problems and accelerates the methods by up to the factor ten.

Chapter 1 starts with the historical development of the amount of biological data and introduces connections and dependencies between statistics, biology, and computer science.Chapter 2 covers a brief review of the essential concepts in cellular and molecular biology to motivate the biological problems.It starts with the basic unit of living organisms (cells), ConclusionActually, there is one other book which has a very similar content and structure: Baldi and Brunak (2001).Based on the clear structure, the basic chapters and the understandable