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A new method to compute K-mer frequencies and its application to annotate large repetitive plant genomes

Overview of attention for article published in BMC Genomics, October 2008
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
q&a
1 Q&A thread

Citations

dimensions_citation
214 Dimensions

Readers on

mendeley
351 Mendeley
citeulike
22 CiteULike
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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 351 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 10 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 111 32%
Student > Ph. D. Student 79 23%
Student > Master 36 10%
Student > Doctoral Student 19 5%
Student > Bachelor 19 5%
Other 70 20%
Unknown 17 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 189 54%
Computer Science 54 15%
Biochemistry, Genetics and Molecular Biology 50 14%
Chemistry 5 1%
Environmental Science 5 1%
Other 20 6%
Unknown 28 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.