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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DotAligner: identification and clustering of RNA structure motifs
|
|---|---|
| Published in |
Genome Biology, December 2017
|
| DOI | 10.1186/s13059-017-1371-3 |
| Pubmed ID | |
| Authors |
Martin A. Smith, Stefan E. Seemann, Xiu Cheng Quek, John S. Mattick |
| Abstract |
The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins. |
X Demographics
The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 25% |
| United States | 2 | 17% |
| New Zealand | 1 | 8% |
| Canada | 1 | 8% |
| Unknown | 5 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 50% |
| Scientists | 6 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 79 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 24% |
| Researcher | 14 | 18% |
| Student > Master | 7 | 9% |
| Student > Bachelor | 5 | 6% |
| Professor | 4 | 5% |
| Other | 14 | 18% |
| Unknown | 16 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 35 | 44% |
| Agricultural and Biological Sciences | 13 | 16% |
| Computer Science | 11 | 14% |
| Business, Management and Accounting | 1 | 1% |
| Medicine and Dentistry | 1 | 1% |
| Other | 2 | 3% |
| Unknown | 16 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 03 June 2022.
All research outputs
#5,391,140
of 25,382,440 outputs
Outputs from Genome Biology
#2,899
of 4,468 outputs
Outputs of similar age
#108,009
of 448,935 outputs
Outputs of similar age from Genome Biology
#38
of 48 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 35th percentile – i.e., 35% 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 448,935 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 75% of its contemporaries.
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 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.