Sonja Holl

The UNICORE Rich Client: Facilitating the Automated Execution of Scientific Workflows

Today, many scientific disciplines heavily rely on computer systems for in-silico experimentation or data management and analysis. The employed computer hard- and software is heterogeneous and complies to different standards, interfaces and protocols for interoperation. Grid middleware systems like UNICORE 6 try to hide some of the complexity of the underlying systems by offering high-level, uniform interfaces for executing computational jobs or storing, moving, and searching through data. Via UNICORE 6 computer resources can be accessed securely with different software clients, e.g. the UNICORE Command line Client (UCC) or the graphical UNICORE Rich Client (URC) which is based on Eclipse. In this paper, we describe the design and features of the URC, and highlight its role as a flexible and extensible Grid client framework using the QSAR field as an example.

A new optimization phase for scientific workflow management systems

Scientific workflows have emerged as an important tool for combining computational power with data analysis for all scientific domains in e-science. They help scientists to design and execute complex in silico experiments. However, with increasing complexity it becomes more and more infeasible to optimize scientific workflows by trial and error. To address this issue, this paper describes the design of a new optimization phase integrated in the established scientific workflow life cycle. We have also developed a flexible optimization application programming interface (API) and have integrated it into a scientific workflow management system. A sample plugin for parameter optimization based on genetic algorithms illustrates, how the API enables rapid implementation of concrete workflow optimization methods. Finally, a use case taken from the area of structural bioinformatics validates how the optimization approach facilitates setup, execution and monitoring of workflow parameter optimization in high performance computing e-science environments.

A UNICORE Plugin for HPC-Enabled Scientific Workflows in Taverna 2.2

As scientific workflows are becoming more complex and apply compute-intensive methods to increasingly large data volumes, access to HPC resources is becoming mandatory. We describe the development of a novel plug in for the Tavern a workflow system, which provides transparent and secure access to HPC/grid resources via the UNICORE grid middleware, while maintaining the ease of use that has been the main reason for the success of scientific workflow systems. A use case from the bioinformatics domain demonstrates the potential of the UNICORE plug in for Tavern a by creating a scientific workflow that executes the central parts in parallel on a cluster resource.

On specifying and sharing scientific workflow optimization results using research objects

Reusing and repurposing scientific workflows for novel scientific experiments is nowadays facilitated by workflow repositories. Such repositories allow scientists to find existing workflows and re-execute them. However, workflow input parameters often need to be adjusted to the research problem at hand. Adapting these parameters may become a daunting task due to the infinite combinations of their values in a wide range of applications. Thus, a scientist may preferably use an automated optimization mechanism to adjust the workflow set-up and improve the result. Currently, automated optimizations must be started from scratch as optimization meta-data are not stored together with workflow provenance data. This important meta-data is lost and can neither be reused nor assessed by other researchers. In this paper we present a novel approach to capture optimization meta-data by extending the Research Object model and reusing the W3C standards. We validate our proposal through a real-world use case taken from the biodivertsity domain, and discuss the exploitation of our solution in the context of existing e-Science infrastructures.

Life science application support in an interoperable e-science environment

In the last decade, life science applications have become more and more integrated into e-Science environments, hence they are typically very demanding, both in terms of computational capabilities and data capacities. Especially the access to life science applications, embedded in such environments via Grid clients still constitutes a major hurdle for scientists that do not have an IT background. Life science applications often comprise a whole set of small programs instead of a single executable. Many of the graphical Grid clients are not perfectly suited for these types of applications, as they often assume that Grid jobs will run a single executable instead of a set of chained executions (i.e. sequences). This means that in order to execute a sequence of multiple programs on a single Grid resource, piping data from one program to the next, the user would have to run a hand-written shell script. Otherwise each program is independently scheduled as a Grid job, which causes unnecessary file transfers between the jobs, even if they are scheduled on the same resource. We present a generic solution to this problem and provide a reference implementation, which seamlessly integrates with the Grid middleware UNICORE. Our approach focuses on a comfortable user interface for the creation of such program sequences, validated in UNICORE-driven HPC-based Grids. Thus, we applied our approach in order to provide support for the usage of the AMBER package (a widely-used collection of programs for molecular dynamics simulations) within Grid workflows. We finally provide a scientific use case of our approach leveraging the interoperability of two different scientific infrastructures that represents an instance of the infrastructure interoperability reference model.

Enhanced Resource Management Enabling Standard Parameter Sweep Jobs for Scientific Applications

Parameter sweeps are used by researchers with scientific domain-specific tools or workflows to submit a large collection of computational jobs whereby each single job of it only varies in certain parts. They require a more fine-grained distribution of jobs across resources, which also raise a significant challenge for efficient resource management in middleware environments that have been not specifically designed to perform parameter sweeps. This paper offers insights into parameter sweep solutions that support multi-disciplinary science environments via abstraction from resource management complexities using middleware. The solutions are based on use case requirements, enable efficient submission, enhanced usability, and standard compliance. We also apply a use case taken from the life science domain to demonstrate usefulness and efficiency of the solutions.

Scientific workflow optimization for improved peptide and protein identification

BackgroundPeptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal.ResultsWe have applied a new optimization framework for the Taverna scientific workflow management system (http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction.ConclusionUsing the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework.