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BugMat and FindNeighbour: command line and server applications for investigating bacterial relatedness

Overview of attention for article published in BMC Bioinformatics, November 2017
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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8 X users

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Title
BugMat and FindNeighbour: command line and server applications for investigating bacterial relatedness
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1907-2
Pubmed ID
Authors

Oriol Mazariegos-Canellas, Trien Do, Tim Peto, David W. Eyre, Anthony Underwood, Derrick Crook, David H. Wyllie

Abstract

Large scale bacterial sequencing has made the determination of genetic relationships within large sequence collections of bacterial genomes derived from the same microbial species an increasingly common task. Solutions to the problem have application to public health (for example, in the detection of possible disease transmission), and as part of divide-and-conquer strategies selecting groups of similar isolates for computationally intensive methods of phylogenetic inference using (for example) maximal likelihood methods. However, the generation and maintenance of distance matrices is computationally intensive, and rapid methods of doing so are needed to allow translation of microbial genomics into public health actions. We developed, tested and deployed three solutions. BugMat is a fast C++ application which generates one-off in-memory distance matrices. FindNeighbour and FindNeighbour2 are server-side applications which build, maintain, and persist either complete (for FindNeighbour) or sparse (for FindNeighbour2) distance matrices given a set of sequences. FindNeighbour and BugMat use a variation model to accelerate computation, while FindNeighbour2 uses reference-based compression. Performance metrics show scalability into tens of thousands of sequences, with options for scaling further. Three applications, each with distinct strengths and weaknesses, are available for distance-matrix based analysis of large bacterial collections. Deployed as part of the Public Health England solution for M. tuberculosis genomic processing, they will have wide applicability.

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The data shown below were collected from the profiles of 8 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 27%
Student > Master 12 25%
Student > Ph. D. Student 4 8%
Other 3 6%
Student > Bachelor 2 4%
Other 2 4%
Unknown 12 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 33%
Immunology and Microbiology 6 13%
Agricultural and Biological Sciences 6 13%
Medicine and Dentistry 2 4%
Computer Science 2 4%
Other 1 2%
Unknown 15 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 08 February 2018.
All research outputs
#7,455,068
of 24,525,936 outputs
Outputs from BMC Bioinformatics
#2,710
of 7,549 outputs
Outputs of similar age
#113,477
of 331,250 outputs
Outputs of similar age from BMC Bioinformatics
#52
of 163 outputs
Altmetric has tracked 24,525,936 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,549 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 gotten more attention than average, scoring higher than 63% 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 331,250 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 163 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.