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TreeToReads - a pipeline for simulating raw reads from phylogenies

Overview of attention for article published in BMC Bioinformatics, March 2017
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About this Attention Score

  • 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)

Mentioned by

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69 tweeters
facebook
1 Facebook page

Citations

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17 Dimensions

Readers on

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61 Mendeley
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Title
TreeToReads - a pipeline for simulating raw reads from phylogenies
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1592-1
Pubmed ID
Authors

Emily Jane McTavish, James Pettengill, Steven Davis, Hugh Rand, Errol Strain, Marc Allard, Ruth E. Timme

Abstract

Using phylogenomic analysis tools for tracking pathogens has become standard practice in academia, public health agencies, and large industries. Using the same raw read genomic data as input, there are several different approaches being used to infer phylogenetic tree. These include many different SNP pipelines, wgMLST approaches, k-mer algorithms, whole genome alignment and others; each of these has advantages and disadvantages, some have been extensively validated, some are faster, some have higher resolution. A few of these analysis approaches are well-integrated into the regulatory process of US Federal agencies (e.g. the FDA's SNP pipeline for tracking foodborne pathogens). However, despite extensive validation on benchmark datasets and comparison with other pipelines, we lack methods for fully exploring the effects of multiple parameter values in each pipeline that can potentially have an effect on whether the correct phylogenetic tree is recovered. To resolve this problem, we offer a program, TreeToReads, which can generate raw read data from mutated genomes simulated under a known phylogeny. This simulation pipeline allows direct comparisons of simulated and observed data in a controlled environment. At each step of these simulations, researchers can vary parameters of interest (e.g., input tree topology, amount of sequence divergence, rate of indels, read coverage, distance of reference genome, etc) to assess the effects of various parameter values on correctly calling SNPs and reconstructing an accurate tree. Such critical assessments of the accuracy and robustness of analytical pipelines are essential to progress in both research and applied settings.

Twitter Demographics

The data shown below were collected from the profiles of 69 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 2%
Canada 1 2%
Egypt 1 2%
Spain 1 2%
United States 1 2%
Unknown 56 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 31%
Researcher 18 30%
Student > Master 10 16%
Student > Doctoral Student 3 5%
Professor > Associate Professor 3 5%
Other 4 7%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 43%
Biochemistry, Genetics and Molecular Biology 11 18%
Computer Science 6 10%
Medicine and Dentistry 4 7%
Immunology and Microbiology 3 5%
Other 6 10%
Unknown 5 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 December 2020.
All research outputs
#875,311
of 22,103,655 outputs
Outputs from BMC Bioinformatics
#76
of 7,099 outputs
Outputs of similar age
#19,543
of 285,322 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 23 outputs
Altmetric has tracked 22,103,655 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,099 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 particularly well, scoring higher than 98% 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 285,322 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 23 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.