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HTS-IA: High Throughput Screening Information Architecture for Genomics

HTS-IA: High Throughput Screening Information Architecture for Genomics

Wienand A. Omta, David A. Egan, Judith Klumperman, Marco R. Spruit, Sjaak Brinkkemper
Copyright: © 2013 |Volume: 8 |Issue: 4 |Pages: 15
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781466635395|DOI: 10.4018/ijhisi.2013100102
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MLA

Omta, Wienand A., et al. "HTS-IA: High Throughput Screening Information Architecture for Genomics." IJHISI vol.8, no.4 2013: pp.17-31. http://doi.org/10.4018/ijhisi.2013100102

APA

Omta, W. A., Egan, D. A., Klumperman, J., Spruit, M. R., & Brinkkemper, S. (2013). HTS-IA: High Throughput Screening Information Architecture for Genomics. International Journal of Healthcare Information Systems and Informatics (IJHISI), 8(4), 17-31. http://doi.org/10.4018/ijhisi.2013100102

Chicago

Omta, Wienand A., et al. "HTS-IA: High Throughput Screening Information Architecture for Genomics," International Journal of Healthcare Information Systems and Informatics (IJHISI) 8, no.4: 17-31. http://doi.org/10.4018/ijhisi.2013100102

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Abstract

This paper describes a high throughput screening architecture for functional genomics screens that use high content methods. Case studies were performed using the Yin case study approach. Additionally a detailed process model is provided using a Method Engineering approach. This study shows that current information architecture lacks interchangeability and functionality. Data enrichment is carried out manually, and software is still deficient in term of interoperability so as to be able to successfully gather data from various external sources. This begs for the growing need of a real integrated laboratory information management system both in academia as well as small-to-medium-sized commercial organizations. Current solutions are designed primarily for clinical samples and lack functionality for larger libraries. A solution should give users the ability to create data pipelines that allow processes to be easily reflected in a relational database.

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