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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MITE Digger, an efficient and accurate algorithm for genome wide discovery of miniature inverted repeat transposable elements
|
|---|---|
| Published in |
BMC Bioinformatics, June 2013
|
| DOI | 10.1186/1471-2105-14-186 |
| Pubmed ID | |
| Authors |
Guojun Yang |
| Abstract |
Miniature inverted repeat transposable elements (MITEs) are abundant non-autonomous elements, playing important roles in shaping gene and genome evolution. Their characteristic structural features are suitable for automated identification by computational approaches, however, de novo MITE discovery at genomic levels is still resource expensive. Efficient and accurate computational tools are desirable. Existing algorithms process every member of a MITE family, therefore a major portion of the computing task is redundant. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 5% |
| Brazil | 2 | 4% |
| India | 1 | 2% |
| Australia | 1 | 2% |
| Russia | 1 | 2% |
| Taiwan | 1 | 2% |
| Unknown | 46 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 27% |
| Researcher | 13 | 24% |
| Student > Master | 7 | 13% |
| Student > Postgraduate | 6 | 11% |
| Student > Doctoral Student | 4 | 7% |
| Other | 7 | 13% |
| Unknown | 3 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 34 | 62% |
| Biochemistry, Genetics and Molecular Biology | 11 | 20% |
| Computer Science | 5 | 9% |
| Chemistry | 1 | 2% |
| Unknown | 4 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 2013.
All research outputs
#14,275,291
of 23,322,258 outputs
Outputs from BMC Bioinformatics
#4,576
of 7,385 outputs
Outputs of similar age
#110,289
of 198,899 outputs
Outputs of similar age from BMC Bioinformatics
#65
of 101 outputs
Altmetric has tracked 23,322,258 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,385 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 34th percentile – i.e., 34% 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 198,899 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.