Real time PCR - European Pharmaceutical Review

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HIGH CONTENT SCREENING ISSUE 2009 Figure 1: High Content Screening data flow analysis might provide much greater insight into their biological function and relevance (see Figure 1). Many free and commercial software packages are now available to analyse HCS data sets using classifiers, although it is still difficult to find a single off-the-shelf software package that answers. Statistical open source software packages such as BioConductor (www.bioconductor.org) provide large collections of methods suitable for HCS data analysis. However, their command-line usage can be too demanding for users without adequate computer knowledge. As an alternative, software packages where users can upload their data and receive their processed results are becoming increasingly common: Weka 16 , CellAnalyzer 2 , CellHTS 3 , TreeView 13 have all been published within the last year. Unfortunately, these services often allow only limited freedom in the choice and arrangement of processing steps. Other, more flexible tools, such as Eclipse 6 , KNIME 11 , JOpera 5 , operate either stand-alone or require considerable computer knowledge and extra software to run through the web. In order to make use of the vast variety of data analysis methods around, it is essential that such an environment is easy and intuitive to Figure 2: General concept of a pipeline node. The component properties are described by the input metadata, output metadata and user defined parameters or transformation rules. The input and output ports can have one or more incoming or outgoing metadata or images. 18 www.europeanpharmaceuticalreview.com
SUBSCRIBE ISSUE 2009 HIGH CONTENT SCREENING Figure 3: A schematic experimental flow of HCS data in a HC/DC workflow and corresponding nodes. use, allows for quick and interactive changes to the analysis process and enables the user to visually explore the results. To meet these challenges data pipelining environments have gathered incredible momentum over the past years. These environments allow the user to visually assemble and adapt the analysis flow from standardised building blocks, which are then connected through pipes carrying data or models. An additional advantage of these systems is the intuitive, graphical way to document what has been done. Open source data pipeline system Pipeline (workflow) systems could become crucial for enabling biologists doing large scale experiments to deal with this data explosion. The workflow is termed abstract in that it is not yet fully functional but the actual components are in place and in the requisite order. In general, workflow systems concentrate on creation of abstract process workflows to which data can be applied when the design process is complete. In contrast, workflow systems in the life sciences domain are often based on a dataflow 8 model, due to the data-centric and data-driven nature of many scientific analyses. A comprehensive understanding of biological phenomena can be achieved only through the integration of all available biological information and different data analysis tools and applications. Workflow environment allows screening researchers to perform the integration themselves without involving any programming. Workflow system allows the construction of complex in silico experiments in the form of workflows and data pipelines. Data pipelining is a relatively simple concept. Any computational component or node has data inputs and data outputs. Data pipelining views these nodes as being connected together by ‘pipes’ through which data flows (see Figure 2 on page 18). In a workflow controlled data pipeline, as the data flows, it is transformed and raw data is analysed to become information and the collected information gives rise to knowledge. The concept of workflow is not new and it has been used by many organisations, over the years, to improve productivity and increase efficiency. A workflow system is highly flexible and can accommodate any changes or updates whenever new or modified data and corresponding analytical tools become available. Currently, there are few workflow systems available in life sciences. Definitely InforSense KDE 4 and Pipeline Pilot 7 are state of the art workflow systems helping faster and efficient research in life sciences domain. However due to the high costs involved, these are still not accessible to many research institutions – a major contributor to scientific research. In this paper we describe open-source workflow package called HC/DC (High Content Data Chain) and some of the design aspects of the underlying architecture and briefly sketch how nodes for HCS data can be incorporated. GO TO CONTENTS PAGE 19
Page 1 and 2: TECHNOLOGY / KNOWLEDGE / INNOVATION
Page 3 and 4: EDITORIAL BOARD Dr Anthony Davies H
Page 5 and 6: ISSUE ONE 2009 Contents Index to Ad
Page 7 and 8: © 2009 Thermo Fisher Scientific In
Page 9 and 10: SUBSCRIBE ISSUE 2009 qPCR Figure 4:
Page 11 and 12: SUBSCRIBE ISSUE 2009 qPCR chromosom
Page 13 and 14: SUBSCRIBE ISSUE 2009 qPCR assays. T
Page 15 and 16: Single Cell Gene Expression from Fl
Page 17: © 2008 Thermo Fisher Scientific In
Page 21 and 22: SUBSCRIBE ISSUE 2009 HIGH CONTENT S
Page 23 and 24: More Imaging Speed Introducing the
Page 25 and 26: SUBSCRIBE ISSUE 2009 LAB AUTOMATION
Page 27 and 28: ISSUE 2009 LAB AUTOMATION washed su
Page 29 and 30: SUBSCRIBE ISSUE 2009 THERMAL ANALYS
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Page 33 and 34: SUBSCRIBE ISSUE 2009 PAT a bioproce
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Page 37 and 38: SUBSCRIBE ISSUE 2009 PAT extensive
Page 39: PharmaINFOcus A REGULAR ROUND-UP OF

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