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Rapid and precise alignment of raw reads against redundant databases with KMA

Overview of attention for article published in BMC Bioinformatics, August 2018
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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 (96th percentile)

Mentioned by

twitter
44 tweeters

Citations

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225 Dimensions

Readers on

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332 Mendeley
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Title
Rapid and precise alignment of raw reads against redundant databases with KMA
Published in
BMC Bioinformatics, August 2018
DOI 10.1186/s12859-018-2336-6
Pubmed ID
Authors

Philip T. L. C. Clausen, Frank M. Aarestrup, Ole Lund

Abstract

As the cost of sequencing has declined, clinical diagnostics based on next generation sequencing (NGS) have become reality. Diagnostics based on sequencing will require rapid and precise mapping against redundant databases because some of the most important determinants, such as antimicrobial resistance and core genome multilocus sequence typing (MLST) alleles, are highly similar to one another. In order to facilitate this, a novel mapping method, KMA (k-mer alignment), was designed. KMA is able to map raw reads directly against redundant databases, it also scales well for large redundant databases. KMA uses k-mer seeding to speed up mapping and the Needleman-Wunsch algorithm to accurately align extensions from k-mer seeds. Multi-mapping reads are resolved using a novel sorting scheme (ConClave scheme), ensuring an accurate selection of templates. The functionality of KMA was compared with SRST2, MGmapper, BWA-MEM, Bowtie2, Minimap2 and Salmon, using both simulated data and a dataset of Escherichia coli mapped against resistance genes and core genome MLST alleles. KMA outperforms current methods with respect to both accuracy and speed, while using a comparable amount of memory. With KMA, it was possible map raw reads directly against redundant databases with high accuracy, speed and memory efficiency.

Twitter Demographics

The data shown below were collected from the profiles of 44 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 332 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 332 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 62 19%
Researcher 60 18%
Student > Master 47 14%
Student > Bachelor 42 13%
Other 16 5%
Other 43 13%
Unknown 62 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 79 24%
Agricultural and Biological Sciences 76 23%
Immunology and Microbiology 29 9%
Veterinary Science and Veterinary Medicine 11 3%
Computer Science 10 3%
Other 41 12%
Unknown 86 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 17 March 2022.
All research outputs
#1,272,435
of 21,687,907 outputs
Outputs from BMC Bioinformatics
#210
of 7,010 outputs
Outputs of similar age
#28,392
of 296,592 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 26 outputs
Altmetric has tracked 21,687,907 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,010 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 done particularly well, scoring higher than 97% 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 296,592 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 26 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 96% of its contemporaries.