| Title |
SPRINT: A new parallel framework for R
|
|---|---|
| Published in |
BMC Bioinformatics, December 2008
|
| DOI | 10.1186/1471-2105-9-558 |
| Pubmed ID | |
| Authors |
Jon Hill, Matthew Hambley, Thorsten Forster, Muriel Mewissen, Terence M Sloan, Florian Scharinger, Arthur Trew, Peter Ghazal |
| Abstract |
Microarray analysis allows the simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples. The analysis of the resulting data tests the limits of existing bioinformatics computing infrastructure. A solution to this issue is to use High Performance Computing (HPC) systems, which contain many processors and more memory than desktop computer systems. Many biostatisticians use R to process the data gleaned from microarray analysis and there is even a dedicated group of packages, Bioconductor, for this purpose. However, to exploit HPC systems, R must be able to utilise the multiple processors available on these systems. There are existing modules that enable R to use multiple processors, but these are either difficult to use for the HPC novice or cannot be used to solve certain classes of problems. A method of exploiting HPC systems, using R, but without recourse to mastering parallel programming paradigms is therefore necessary to analyse genomic data to its fullest. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 6 | 6% |
| Germany | 3 | 3% |
| Brazil | 3 | 3% |
| United States | 3 | 3% |
| Belgium | 2 | 2% |
| Norway | 1 | 1% |
| Canada | 1 | 1% |
| Sweden | 1 | 1% |
| France | 1 | 1% |
| Other | 4 | 4% |
| Unknown | 68 | 73% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 35 | 38% |
| Student > Ph. D. Student | 15 | 16% |
| Professor > Associate Professor | 11 | 12% |
| Student > Master | 6 | 6% |
| Other | 5 | 5% |
| Other | 14 | 15% |
| Unknown | 7 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 52 | 56% |
| Computer Science | 9 | 10% |
| Biochemistry, Genetics and Molecular Biology | 7 | 8% |
| Engineering | 4 | 4% |
| Sports and Recreations | 3 | 3% |
| Other | 11 | 12% |
| Unknown | 7 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 10 January 2014.
All research outputs
#3,569,201
of 22,655,397 outputs
Outputs from BMC Bioinformatics
#1,305
of 7,236 outputs
Outputs of similar age
#20,193
of 168,773 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 69 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 81% 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 168,773 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.