(Ebook) Large Scale Data Handling in Biology by Karol Kozak ISBN 9788776815554, 8776815552

(Ebook) Large Scale Data Handling in Biology by Karol Kozak ISBN 9788776815554, 8776815552

Data Handling in Biology - the application of computational and analytical methods to biological problems - is a rapidly evolving scientific discipline. Written in a clear, engaging style, Large Scale Data Handling in Biology is for scientists and students who are learning computational approaches to biology. The book covers the data storage system, computational approaches to biological problems, an introduction to workflow systems, data mining, data visualization, and tips for tailoring existing data analysis software to individual research needs.Chapters:1. What to do with all the data?2. Data Storage, Backup and Archiving Architecture3. Workflow Systems4. Database Development: Laboratory Information Management Systems and Public DatabasesAbstract"High-throughput" in High Content Screening is relative: although instruments that acquire in the range of 100 000 images per day are already marketed, this is still not comparable to the throughput of classical High Throughput Screening. Assays get more and more complex, consequently assay development times become prolonged. Further, standardization of cell culture conditions is a major challenge. Informatics technologies are required to transform HCS data and images into useful information and then into knowledge to drive decision making in an efficient and cost effective manner. Major investments have to be made to gather a critical mass of instrumentation, image analysis tools and IT infrastructure. The data load per run of a screen may easily go beyond the one Terabyte border, and the processing of the hundreds of thousands of images applying complex image analysis software and algorithms requires an extraordinarily powerful IT infrastructure. This chapter will give an overview of the considerations that should be kept in mind while setting-up the informatics infrastructure to implement and successfully run large-scale high-content experiments. In this chapter we describe some of the challenges of harnessing the huge and growing volumes of HCS data, and provide insight to help toward implementing or selecting, utilizing a high content informatics solution to meet organization's needs and give an overview of informatics tools and technologies for HCS.