Data collection, high-throughput

Data systems, more broadly known as Laboratory Information Management Systems, are in almost universal use in laboratories, allowing the collection of mass data required by parallel and high-throughput analytical... 

The high-throughput concept for quantitative bioanalysis applies to steps such as assay development, sample collection and sorting, sample preparation, sample analysis, and data processing and reporting. Those processes are closely interlinked and improvement of process throughput is equally important. 

Handling such interwoven networks and complex feedback loops is beyond the capability of common laboratory methods, not to mention that just the complexity of scientific literature itself is already beyond measure. Help from computers and bioinformatics has become a must in today s biomedical research. In fact, bioinformatics methods have become indispensable for each step in biomedical research, from high-throughput data collection to clinical decision support. This chapter focuses on the application of bioinformatics methods in the study of pharmacogenomics, drug discovery, and systems biology. 

Calculation of ADME predictions is now routine and often high throughput. However, unlike many published studies in which calculated properties are validated with experimental data for a diverse collection of molecules, for virtual small-molecule libraries, there are usually no physical data to validate the predictions. Often, the molecules that are the subject of calculation are dissimilar to those molecules used to develop prediction tools. As a result, one is usually looking for trends in the prediction as a function of the selection of specific diversity reagents around a common core or scaffold. Thus, it is important to consider predicted molecular properties with care and to interpret the results with the proper level of expectation. 

High-throughput crystallographic data collection at synchrotrons... 

Rapid collection of diffraction data depends on access to such powerful X-ray sources. This chapter describes how high-quality, high-throughput data collection can be achieved. We use SGX-CAT, the SGX Collaborative Access Team beamline, located at the Advanced Photon Source of Argonne National Laboratory as an example to illustrate the concepts behind the design of, and the hardware used at, synchrotron beamlines. Many of these features are found, individually or in combination, at other beamlines. Data collection at synchrotron sources produces enormous quantities of data. We, therefore, also discuss the information technology infrastructure and software that is necessary for effective data management. 

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