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Mendeley readers
Attention Score in Context
| Title |
Improving the specificity of high-throughput ortholog prediction
|
|---|---|
| Published in |
BMC Bioinformatics, May 2006
|
| DOI | 10.1186/1471-2105-7-270 |
| Pubmed ID | |
| Authors |
Debra L Fulton, Yvonne Y Li, Matthew R Laird, Benjamin GS Horsman, Fiona M Roche, Fiona SL Brinkman |
| Abstract |
Orthologs (genes that have diverged after a speciation event) tend to have similar function, and so their prediction has become an important component of comparative genomics and genome annotation. The gold standard phylogenetic analysis approach of comparing available organismal phylogeny to gene phylogeny is not easily automated for genome-wide analysis; therefore, ortholog prediction for large genome-scale datasets is typically performed using a reciprocal-best-BLAST-hits (RBH) approach. One problem with RBH is that it will incorrectly predict a paralog as an ortholog when incomplete genome sequences or gene loss is involved. In addition, there is an increasing interest in identifying orthologs most likely to have retained similar function. |
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 % |
|---|---|---|
| United States | 6 | 4% |
| Brazil | 4 | 2% |
| France | 1 | <1% |
| Italy | 1 | <1% |
| Kenya | 1 | <1% |
| Australia | 1 | <1% |
| Netherlands | 1 | <1% |
| Sweden | 1 | <1% |
| Israel | 1 | <1% |
| Other | 6 | 4% |
| Unknown | 140 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 48 | 29% |
| Student > Ph. D. Student | 37 | 23% |
| Professor > Associate Professor | 12 | 7% |
| Student > Bachelor | 12 | 7% |
| Student > Master | 12 | 7% |
| Other | 30 | 18% |
| Unknown | 12 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 97 | 60% |
| Biochemistry, Genetics and Molecular Biology | 32 | 20% |
| Computer Science | 13 | 8% |
| Engineering | 2 | 1% |
| Chemistry | 2 | 1% |
| Other | 3 | 2% |
| Unknown | 14 | 9% |
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 05 October 2016.
All research outputs
#7,451,942
of 22,782,096 outputs
Outputs from BMC Bioinformatics
#3,021
of 7,277 outputs
Outputs of similar age
#22,585
of 64,653 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 42 outputs
Altmetric has tracked 22,782,096 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,277 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 gotten more attention than average, scoring higher than 50% 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 64,653 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.