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Mendeley readers
Attention Score in Context
| Title |
Psiscan: a computational approach to identify H/ACA-like and AGA-like non-coding RNA in trypanosomatid genomes
|
|---|---|
| Published in |
BMC Bioinformatics, November 2008
|
| DOI | 10.1186/1471-2105-9-471 |
| Pubmed ID | |
| Authors |
Inna Myslyuk, Tirza Doniger, Yair Horesh, Avraham Hury, Ran Hoffer, Yaara Ziporen, Shulamit Michaeli, Ron Unger |
| Abstract |
Detection of non coding RNA (ncRNA) molecules is a major bioinformatics challenge. This challenge is particularly difficult when attempting to detect H/ACA molecules which are involved in converting uridine to pseudouridine on rRNA in trypanosomes, because these organisms have unique H/ACA molecules (termed H/ACA-like) that lack several of the features that characterize H/ACA molecules in most other organisms. |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 32 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 18% |
| Researcher | 6 | 18% |
| Professor > Associate Professor | 4 | 12% |
| Student > Master | 4 | 12% |
| Other | 2 | 6% |
| Other | 6 | 18% |
| Unknown | 5 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 30% |
| Biochemistry, Genetics and Molecular Biology | 7 | 21% |
| Medicine and Dentistry | 3 | 9% |
| Computer Science | 3 | 9% |
| Immunology and Microbiology | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 6 | 18% |
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 23 August 2011.
All research outputs
#7,454,427
of 22,789,566 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,280 outputs
Outputs of similar age
#32,602
of 92,934 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 48 outputs
Altmetric has tracked 22,789,566 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 7,280 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 92,934 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.