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Phylogeny reconstruction based on the length distribution of k-mismatch common substrings

Overview of attention for article published in Algorithms for Molecular Biology, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#27 of 212)
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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8 tweeters
wikipedia
1 Wikipedia page

Citations

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15 Dimensions

Readers on

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15 Mendeley

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Title
Phylogeny reconstruction based on the length distribution of k-mismatch common substrings
Published in
Algorithms for Molecular Biology, December 2017
DOI 10.1186/s13015-017-0118-8
Pubmed ID
29238399
Authors

Burkhard Morgenstern, Svenja Schöbel, Chris-André Leimeister

Abstract

Various approaches to alignment-free sequence comparison are based on the length of exact or inexact word matches between pairs of input sequences. Haubold et al. (J Comput Biol 16:1487-1500, 2009) showed how the average number of substitutions per position between two DNA sequences can be estimated based on the average length of exact common substrings. In this paper, we study the length distribution of k-mismatch common substrings between two sequences. We show that the number of substitutions per position can be accurately estimated from the position of a local maximum in the length distribution of their k-mismatch common substrings.

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Twitter Demographics

The data shown below were collected from the profiles of 8 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 40%
Professor > Associate Professor 2 13%
Researcher 2 13%
Student > Doctoral Student 2 13%
Unknown 3 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 33%
Computer Science 4 27%
Unspecified 1 7%
Agricultural and Biological Sciences 1 7%
Engineering 1 7%
Other 0 0%
Unknown 3 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 12 December 2018.
All research outputs
#2,531,072
of 14,021,395 outputs
Outputs from Algorithms for Molecular Biology
#27
of 212 outputs
Outputs of similar age
#93,994
of 399,373 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 12 outputs
Altmetric has tracked 14,021,395 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 212 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done well, scoring higher than 86% 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 399,373 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 76% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.

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