| Title |
GeneTIER: prioritization of candidate disease genes using tissue-specific gene expression profiles
|
|---|---|
| Published in |
Bioinformatics, April 2015
|
| DOI | 10.1093/bioinformatics/btv196 |
| Pubmed ID | |
| Authors |
Agne Antanaviciute, Catherine Daly, Laura A Crinnion, Alexander F Markham, Christopher M Watson, David T Bonthron, Ian M Carr |
| Abstract |
Motivation In attempts to determine the genetic causes of human disease, researchers are often faced with a large number of candidate genes. Linkage studies can point to a genomic region containing hundreds of genes, while the high-throughput sequencing approach will often identify a great number of non-synonymous genetic variants. Since systematic experimental verification of each such candidate gene is not feasible, a method is needed to decide which genes are worth investigating further. Computational gene prioritization presents itself as a solution to this problem, systematically analyzing and sorting each gene from the most to least likely to be the disease-causing gene, in a fraction of the time it would take a researcher to perform such queries manually. Results Here we present GeneTIER (Gene TIssue Expression Ranker), a new web-based application for candidate gene prioritization. GeneTIER replaces knowledge-based inference traditionally used in candidate disease gene prioritization applications with experimental data from tissue-specific gene expression datasets and thus largely overcomes the bias towards the better characterized genes/diseases that commonly afflict other methods. We show that our approach is capable of accurate candidate gene prioritization and illustrate its strengths and weaknesses using case study examples. Availability and Implementation Freely available on the web at http://dna.leeds.ac.uk/GeneTIER/ Contact: [email protected]. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 17% |
| Japan | 1 | 17% |
| Spain | 1 | 17% |
| Norway | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 3% |
| United States | 2 | 3% |
| Germany | 1 | 2% |
| Taiwan | 1 | 2% |
| Unknown | 60 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 30% |
| Researcher | 18 | 27% |
| Student > Master | 9 | 14% |
| Student > Doctoral Student | 3 | 5% |
| Student > Postgraduate | 3 | 5% |
| Other | 9 | 14% |
| Unknown | 4 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 32% |
| Biochemistry, Genetics and Molecular Biology | 18 | 27% |
| Computer Science | 9 | 14% |
| Medicine and Dentistry | 7 | 11% |
| Environmental Science | 1 | 2% |
| Other | 7 | 11% |
| Unknown | 3 | 5% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 August 2015.
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