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MANTIS: an R package that simulates multilocus models of pathogen evolution

Overview of attention for article published in BMC Bioinformatics, May 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

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Title
MANTIS: an R package that simulates multilocus models of pathogen evolution
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0598-9
Pubmed ID
Authors

José Lourenço, Paul S Wikramaratna, Sunetra Gupta

Abstract

In host-pathogen systems the development of immunity by the host places pressure on pathogens, by setting up competition between genetic variants due to the establishment of cross-protective responses. These pressures can lead to pathogen-specific, ubiquitous dynamic behaviours. Understanding the evolutionary forces that shape these patterns is one of the key goals of computationally simulated epidemiological models. Despite the contribution of such research methods in recent years to our current understanding of pathogen evolution, the availability of free software tools for the general public remains scarce. We developed the Multilocus ANTIgenic Simulator (MANTIS) software package for the R statistical environment. MANTIS can simulate and analyse epidemiological time-series generated under the biological assumptions of the strain theory of host-pathogen systems by Gupta et al. MANTIS wraps a C/C++ ordinary-differential equations system and Runge-Kutta solver into a set of user-friendly R functions. These include routines to numerically simulate the system and others to analyse, visualize and export results. For this, the package offers its own set of time-series plotting and exportation functions. MANTIS's main goal is to serve as a free, ready-to-use academic software tool. Its open source nature further provides an opportunity for users with advanced programming skills to expand its capabilities. Here, we describe the background theory, implementation, basic functionality and usage of this package. MANTIS is freely available from http://www.eeid.ox.ac.uk/mantis under the GPL license.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 36%
Student > Master 4 11%
Student > Ph. D. Student 4 11%
Student > Doctoral Student 3 8%
Student > Bachelor 2 6%
Other 4 11%
Unknown 6 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 25%
Medicine and Dentistry 5 14%
Computer Science 4 11%
Biochemistry, Genetics and Molecular Biology 2 6%
Engineering 2 6%
Other 5 14%
Unknown 9 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 May 2019.
All research outputs
#3,704,987
of 22,807,037 outputs
Outputs from BMC Bioinformatics
#1,393
of 7,284 outputs
Outputs of similar age
#48,115
of 266,679 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 129 outputs
Altmetric has tracked 22,807,037 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,284 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 well, scoring higher than 80% 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 266,679 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 81% of its contemporaries.
We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.