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Content archived on 2024-06-16

Development of a complete integrated SNP analysis system

Objective

The requirement to screen for known SNPs (single nucleotide polymorphisms) has become one of the key challenges to be addressed to enable the exploitation of the human genome sequence, where approximately 3 million SNPs are responsible for all of the variation within the human population. Several methods and technologies are currently available for detection of SNPs, but no single platform exists which can deliver 100% accuracy, in a low cost, versatile, and easy-to-use integrated system.

The proposed research will focus on the development of integrated SNP detection platforms to include modules for DNA extraction and purification from biological samples, DNA amplification, DNA characterisation (including SNP detection), signal transduction, interpretation and data analysis. Integration of the modules on a single platform will include a transport mechanism based on electrowetting actuation (EWOD).

Two alternatives SNP detection platforms will be developed based on optical and magnetic sensing respectively. The whole system will be packaged as a single automated functional system with a simplified GUI interface, based on specifications recommended by potential end-users within the consortium. This systems development will focus on delivering a product customised for low to medium throughput, low cost, point-of-care applications, with emphasis on providing very rapid and accurate results.

Within the project, the systems will be benchmarked for screening of SNPs in the CFTR gene that are associated with cystic fibrosis. CF patient samples will be used to verify the accuracy and reproducibility of the system in a clinical diagnostics laboratory.

Fields of science (EuroSciVoc)

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Keywords

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Topic(s)

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Call for proposal

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FP6-2004-IST-NMP-2
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Funding Scheme

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STREP - Specific Targeted Research Project

Coordinator

UNIVERSITY COLLEGE CORK, NATIONAL UNIVERSITY OF IRELAND, CORK
EU contribution
No data
Total cost

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Participants (6)

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