| Title |
CVE: an R package for interactive variant prioritisation in precision oncology
|
|---|---|
| Published in |
BMC Medical Genomics, May 2017
|
| DOI | 10.1186/s12920-017-0261-6 |
| Pubmed ID | |
| Authors |
Andreas Mock, Suzanne Murphy, James Morris, Francesco Marass, Nitzan Rosenfeld, Charlie Massie |
| Abstract |
An increasing number of precision oncology programmes are being launched world-wide. To support this development, we present the Cancer Variant Explorer (CVE), an R package with an interactive Shiny web browser interface. Leveraging Oncotator and the Drug Gene Interaction Database, CVE offers exploration of variants within single or multiple tumour exomes to identify drivers, resistance mechanisms and to assess druggability. We present example applications including the analysis of an individual patient and a cohort-wide study, and provide a first extension of CVE by adding a tumour-specific co-expression network. The CVE package allows interactive variant prioritisation to expedite the analysis of cancer sequencing studies. Our framework also includes the prioritisation of druggable targets, allows exploratory analysis of tissue specific networks and is extendable for specific applications by virtue of its modular design. We encourage the use of CVE within translational research studies and molecular tumour boards. The CVE package is available via Bioconductor ( http://bioconductor.org/packages/CVE/ ). |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Germany | 1 | 25% |
| Montenegro | 1 | 25% |
| Canada | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Taiwan | 1 | 2% |
| Unknown | 44 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 40% |
| Student > Ph. D. Student | 10 | 22% |
| Other | 5 | 11% |
| Student > Bachelor | 3 | 7% |
| Student > Master | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 5 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 13 | 29% |
| Agricultural and Biological Sciences | 7 | 16% |
| Medicine and Dentistry | 6 | 13% |
| Computer Science | 4 | 9% |
| Design | 2 | 4% |
| Other | 8 | 18% |
| Unknown | 5 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 05 March 2018.
All research outputs
#13,555,965
of 22,977,819 outputs
Outputs from BMC Medical Genomics
#506
of 1,229 outputs
Outputs of similar age
#160,214
of 313,676 outputs
Outputs of similar age from BMC Medical Genomics
#10
of 17 outputs
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