| Title |
ePGA: A Web-Based Information System for Translational Pharmacogenomics
|
|---|---|
| Published in |
PLOS ONE, September 2016
|
| DOI | 10.1371/journal.pone.0162801 |
| Pubmed ID | |
| Authors |
Kleanthi Lakiotaki, Evgenia Kartsaki, Alexandros Kanterakis, Theodora Katsila, George P. Patrinos, George Potamias |
| Abstract |
One of the challenges that arise from the advent of personal genomics services is to efficiently couple individual data with state of the art Pharmacogenomics (PGx) knowledge. Existing services are limited to either providing static views of PGx variants or applying a simplistic match between individual genotypes and existing PGx variants. Moreover, there is a considerable amount of haplotype variation associated with drug metabolism that is currently insufficiently addressed. Here, we present a web-based electronic Pharmacogenomics Assistant (ePGA; http://www.epga.gr/) that provides personalized genotype-to-phenotype translation, linked to state of the art clinical guidelines. ePGA's translation service matches individual genotype-profiles with PGx gene haplotypes and infers the corresponding diplotype and phenotype profiles, accompanied with summary statistics. Additional features include i) the ability to customize translation based on subsets of variants of clinical interest, and ii) to update the knowledge base with novel PGx findings. We demonstrate ePGA's functionality on genetic variation data from the 1000 Genomes Project. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 44 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 20% |
| Researcher | 8 | 18% |
| Student > Master | 7 | 16% |
| Student > Bachelor | 3 | 7% |
| Other | 2 | 5% |
| Other | 5 | 11% |
| Unknown | 10 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 20% |
| Biochemistry, Genetics and Molecular Biology | 7 | 16% |
| Agricultural and Biological Sciences | 5 | 11% |
| Medicine and Dentistry | 3 | 7% |
| Engineering | 3 | 7% |
| Other | 5 | 11% |
| Unknown | 12 | 27% |
Attention Score in Context
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#15,384,302
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