Title |
Implementation of Cloud based Next Generation Sequencing data analysis in a clinical laboratory
|
---|---|
Published in |
BMC Research Notes, May 2014
|
DOI | 10.1186/1756-0500-7-314 |
Pubmed ID | |
Authors |
Getiria Onsongo, Jesse Erdmann, Michael D Spears, John Chilton, Kenneth B Beckman, Adam Hauge, Sophia Yohe, Matthew Schomaker, Matthew Bower, Kevin A T Silverstein, Bharat Thyagarajan |
Abstract |
The introduction of next generation sequencing (NGS) has revolutionized molecular diagnostics, though several challenges remain limiting the widespread adoption of NGS testing into clinical practice. One such difficulty includes the development of a robust bioinformatics pipeline that can handle the volume of data generated by high-throughput sequencing in a cost-effective manner. Analysis of sequencing data typically requires a substantial level of computing power that is often cost-prohibitive to most clinical diagnostics laboratories. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 29% |
Unknown | 5 | 71% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 6 | 86% |
Scientists | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 4% |
Brazil | 2 | 2% |
China | 2 | 2% |
United Kingdom | 2 | 2% |
Sweden | 1 | 1% |
Germany | 1 | 1% |
Finland | 1 | 1% |
Canada | 1 | 1% |
Unknown | 68 | 84% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 24 | 30% |
Other | 14 | 17% |
Student > Ph. D. Student | 11 | 14% |
Professor > Associate Professor | 8 | 10% |
Student > Master | 7 | 9% |
Other | 11 | 14% |
Unknown | 6 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 27 | 33% |
Computer Science | 17 | 21% |
Biochemistry, Genetics and Molecular Biology | 9 | 11% |
Medicine and Dentistry | 8 | 10% |
Nursing and Health Professions | 3 | 4% |
Other | 10 | 12% |
Unknown | 7 | 9% |