Management

Large Scale Data Handling in Biology

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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.

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Detail Information

Series Title

Large Scale Data Handling in Biology

Call Number

-

Publisher

: ., 2010

Collation

1-55

Language

English

ISBN/ISSN

978-87-7681-555-4

Classification

NONE

Content Type

-

Media Type

-

Carrier Type

-

Edition

1st Edtion

Subject(s)

Management

Specific Detail Info

-

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