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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud
|
|---|---|
| Published in |
PLOS ONE, October 2015
|
| DOI | 10.1371/journal.pone.0140829 |
| Pubmed ID | |
| Authors |
Enis Afgan, Clare Sloggett, Nuwan Goonasekera, Igor Makunin, Derek Benson, Mark Crowe, Simon Gladman, Yousef Kowsar, Michael Pheasant, Ron Horst, Andrew Lonie |
| Abstract |
Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation. |
X Demographics
The data shown below were collected from the profiles of 50 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 8 | 16% |
| United States | 6 | 12% |
| France | 3 | 6% |
| United Kingdom | 3 | 6% |
| Canada | 2 | 4% |
| Bosnia and Herzegovina | 2 | 4% |
| Malawi | 1 | 2% |
| Germany | 1 | 2% |
| New Zealand | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 21 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 25 | 50% |
| Scientists | 24 | 48% |
| Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 163 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 2 | 1% |
| Netherlands | 1 | <1% |
| Sweden | 1 | <1% |
| United Kingdom | 1 | <1% |
| Denmark | 1 | <1% |
| Italy | 1 | <1% |
| Spain | 1 | <1% |
| United States | 1 | <1% |
| Luxembourg | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 152 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 35 | 21% |
| Student > Ph. D. Student | 26 | 16% |
| Student > Bachelor | 19 | 12% |
| Student > Master | 13 | 8% |
| Student > Doctoral Student | 11 | 7% |
| Other | 31 | 19% |
| Unknown | 28 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 40 | 25% |
| Biochemistry, Genetics and Molecular Biology | 36 | 22% |
| Computer Science | 24 | 15% |
| Immunology and Microbiology | 7 | 4% |
| Chemistry | 4 | 2% |
| Other | 23 | 14% |
| Unknown | 29 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 40. 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 19 January 2016.
All research outputs
#1,044,823
of 25,706,302 outputs
Outputs from PLOS ONE
#13,402
of 224,010 outputs
Outputs of similar age
#15,548
of 295,964 outputs
Outputs of similar age from PLOS ONE
#310
of 5,587 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 224,010 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 295,964 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 5,587 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.