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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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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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1 news outlet
blogs
1 blog
policy
1 policy source
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39 X users

Citations

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458 Dimensions

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476 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 39 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 476 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 476 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 88 18%
Researcher 81 17%
Student > Master 59 12%
Student > Bachelor 52 11%
Student > Doctoral Student 22 5%
Other 54 11%
Unknown 120 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 103 22%
Agricultural and Biological Sciences 95 20%
Immunology and Microbiology 41 9%
Veterinary Science and Veterinary Medicine 20 4%
Medicine and Dentistry 13 3%
Other 53 11%
Unknown 151 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 40. 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 August 2023.
All research outputs
#1,043,265
of 25,559,053 outputs
Outputs from BMC Bioinformatics
#78
of 7,718 outputs
Outputs of similar age
#22,024
of 345,042 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 87 outputs
Altmetric has tracked 25,559,053 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,718 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 99% 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 345,042 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 93% of its contemporaries.
We're also able to compare this research output to 87 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 99% of its contemporaries.