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Mendeley readers
Attention Score in Context
| Title |
A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes
|
|---|---|
| Published in |
BMC Genomics, October 2008
|
| DOI | 10.1186/1471-2164-9-517 |
| Pubmed ID | |
| Authors |
Stefan Kurtz, Apurva Narechania, Joshua C Stein, Doreen Ware |
| Abstract |
The challenges of accurate gene prediction and enumeration are further aggravated in large genomes that contain highly repetitive transposable elements (TEs). Yet TEs play a substantial role in genome evolution and are themselves an important subject of study. Repeat annotation, based on counting occurrences of k-mers, has been previously used to distinguish TEs from low-copy genic regions; but currently available software solutions are impractical due to high memory requirements or specialization for specific user-tasks. |
Mendeley readers
The data shown below were compiled from readership statistics for 350 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 9 | 3% |
| United States | 8 | 2% |
| Brazil | 6 | 2% |
| India | 4 | 1% |
| France | 2 | <1% |
| Canada | 2 | <1% |
| Netherlands | 2 | <1% |
| Chile | 2 | <1% |
| Italy | 2 | <1% |
| Other | 11 | 3% |
| Unknown | 302 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 112 | 32% |
| Student > Ph. D. Student | 79 | 23% |
| Student > Master | 35 | 10% |
| Student > Doctoral Student | 19 | 5% |
| Student > Bachelor | 19 | 5% |
| Other | 68 | 19% |
| Unknown | 18 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 189 | 54% |
| Computer Science | 54 | 15% |
| Biochemistry, Genetics and Molecular Biology | 50 | 14% |
| Environmental Science | 5 | 1% |
| Chemistry | 4 | 1% |
| Other | 19 | 5% |
| Unknown | 29 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 11 September 2013.
All research outputs
#2,583,894
of 22,656,971 outputs
Outputs from BMC Genomics
#844
of 10,607 outputs
Outputs of similar age
#8,255
of 92,038 outputs
Outputs of similar age from BMC Genomics
#2
of 45 outputs
Altmetric has tracked 22,656,971 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,607 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 91% 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 92,038 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.