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Mendeley readers
Attention Score in Context
| Title |
Identifying differential exon splicing using linear models and correlation coefficients
|
|---|---|
| Published in |
BMC Bioinformatics, January 2009
|
| DOI | 10.1186/1471-2105-10-26 |
| Pubmed ID | |
| Authors |
Sonia H Shah, Jacqueline A Pallas |
| Abstract |
With the availability of the Affymetrix exon arrays a number of tools have been developed to enable the analysis. These however can be expensive or have several pre-installation requirements. This led us to develop an analysis workflow for analysing differential splicing using freely available software packages that are already being widely used for gene expression analysis. The workflow uses the packages in the standard installation of R and Bioconductor (BiocLite) to identify differential splicing. We use the splice index method with the LIMMA framework. The main drawback with this approach is that it relies on accurate estimates of gene expression from the probe-level data. Methods such as RMA and PLIER may misestimate when a large proportion of exons are spliced. We therefore present the novel concept of a gene correlation coefficient calculated using only the probeset expression pattern within a gene. We show that genes with lower correlation coefficients are likely to be differentially spliced. |
Mendeley readers
The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 6% |
| United Kingdom | 3 | 4% |
| Germany | 1 | 1% |
| Mexico | 1 | 1% |
| Ireland | 1 | 1% |
| Spain | 1 | 1% |
| Denmark | 1 | 1% |
| Unknown | 56 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 30 | 44% |
| Student > Ph. D. Student | 13 | 19% |
| Student > Bachelor | 6 | 9% |
| Professor > Associate Professor | 6 | 9% |
| Professor | 5 | 7% |
| Other | 4 | 6% |
| Unknown | 4 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 39 | 57% |
| Biochemistry, Genetics and Molecular Biology | 10 | 15% |
| Medicine and Dentistry | 4 | 6% |
| Engineering | 4 | 6% |
| Mathematics | 2 | 3% |
| Other | 4 | 6% |
| Unknown | 5 | 7% |
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 02 June 2010.
All research outputs
#12,846,160
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#3,776
of 7,234 outputs
Outputs of similar age
#136,992
of 169,909 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 63 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,234 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
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 169,909 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.