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Fast phylogenetic inference from typing data

Overview of attention for article published in Algorithms for Molecular Biology, February 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#6 of 194)
  • High Attention Score compared to outputs of the same age (85th percentile)

Mentioned by

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22 tweeters
facebook
1 Facebook page

Citations

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1 Dimensions

Readers on

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11 Mendeley
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Title
Fast phylogenetic inference from typing data
Published in
Algorithms for Molecular Biology, February 2018
DOI 10.1186/s13015-017-0119-7
Pubmed ID
Authors

João A. Carriço, Maxime Crochemore, Alexandre P. Francisco, Solon P. Pissis, Bruno Ribeiro-Gonçalves, Cátia Vaz

Abstract

Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequence-based typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases.

Twitter Demographics

The data shown below were collected from the profiles of 22 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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 36%
Researcher 3 27%
Student > Master 1 9%
Student > Bachelor 1 9%
Student > Doctoral Student 1 9%
Other 1 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 36%
Computer Science 3 27%
Agricultural and Biological Sciences 2 18%
Arts and Humanities 1 9%
Immunology and Microbiology 1 9%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 April 2018.
All research outputs
#1,021,646
of 12,813,846 outputs
Outputs from Algorithms for Molecular Biology
#6
of 194 outputs
Outputs of similar age
#39,028
of 270,442 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 1 outputs
Altmetric has tracked 12,813,846 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 194 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done particularly well, scoring higher than 96% 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 270,442 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them