3.1 Scientific workflows  (Page 9/4)

Key concepts:

• Scientific workflows
• Data-intensive research

Introduction

The use of data processing workflows within the business sector has been commonplace for many years. Their use within the scientific community, however, has only just begun. With the uptake of workflows within scientific research, an unprecedented level of data analyses is now at the fingertips of individual researchers, leading to a change in the way research is carried out. This chapter describes the advantages of using workflows in modern biological research; demonstrating research from the field where the application of workflow technologies was vital for understanding the processes involved in resistance and susceptibility of infection by a parasite. Specific attention is drawn to the Taverna Workflow Workbench (Hull et al. 2006), a workflow management system that provides a suite of tools to support the design, execution, and management of complex analyses in the data intensive research, for example, in the Life Sciences.

Data-intensive research in the life sciences

In the last decade the field of informatics has moved from the fringes of biological and biomedical sciences to being an essential part of research. From the early days of gene and protein sequence analysis, to the high-throughput sequencing of whole genomes, informatics is integral in the analysis, interpretation, and understanding of biological data. The post-genomic era has been witness to an exponential rise in the generation of biological data; the majority of which is freely available in the public domain, and accessible over the Internet.

New techniques and technologies are continuously emerging to increase the speed of data production. As a result, the generation of novel biological hypotheses has shifted from the task of data generation to that of data analysis. The results of such high-throughput investigations, and the way it is published and shared, is initially for the benefit of the research groups generating the data; yet it is fundamental to many other investigations and research institutes. The public availability means that it can then be reused in the day to day work of many other scientists. This is true for most bioinformatics resources. The overall effect, however, is the accumulation of useful biological resources over time.

In the 2009 Databases special issue of Nucleic Acids Research , over 1000 different biological databases were available to the scientific community. Many of these data resources have associated analysis tools and search algorithms, increasing the number of possible tools and resources to several thousand. These resources have been developed over time by different institutions. Consequently, they are distributed and highly heterogeneous with few standards for data representation or data access. Therefore, despite the availability of these resources, integration and interoperability present significant challenges to researchers.