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A composite genome approach to identify phylogenetically informative data from next-generation sequencing

Overview of attention for article published in BMC Bioinformatics, June 2015
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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 (96th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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2 blogs
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54 X users

Citations

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28 Dimensions

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97 Mendeley
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Title
A composite genome approach to identify phylogenetically informative data from next-generation sequencing
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/s12859-015-0632-y
Pubmed ID
Authors

Rachel S. Schwartz, Kelly M. Harkins, Anne C. Stone, Reed A. Cartwright

Abstract

Improvements in sequencing technology now allow easy acquisition of large datasets; however, analyzing these data for phylogenetics can be challenging. We have developed a novel method to rapidly obtain homologous genomic data for phylogenetics directly from next-generation sequencing reads without the use of a reference genome. This software, called SISRS, avoids the time consuming steps of de novo whole genome assembly, multiple genome alignment, and annotation. For simulations SISRS is able to identify large numbers of loci containing variable sites with phylogenetic signal. For genomic data from apes, SISRS identified thousands of variable sites, from which we produced an accurate phylogeny. Finally, we used SISRS to identify phylogenetic markers that we used to estimate the phylogeny of placental mammals. We recovered eight phylogenies that resolved the basal relationships among mammals using datasets with different levels of missing data. The three alternate resolutions of the basal relationships are consistent with the major hypotheses for the relationships among mammals, all of which have been supported previously by different molecular datasets. SISRS has the potential to transform phylogenetic research. This method eliminates the need for expensive marker development in many studies by using whole genome shotgun sequence data directly. SISRS is open source and freely available at https://github.com/rachelss/SISRS/releases .

X Demographics

X Demographics

The data shown below were collected from the profiles of 54 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 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 5%
Germany 2 2%
Brazil 2 2%
United Kingdom 2 2%
Sweden 1 1%
France 1 1%
Belgium 1 1%
Taiwan 1 1%
Japan 1 1%
Other 1 1%
Unknown 80 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 31%
Researcher 29 30%
Student > Master 6 6%
Professor 5 5%
Student > Bachelor 4 4%
Other 13 13%
Unknown 10 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 58%
Biochemistry, Genetics and Molecular Biology 11 11%
Computer Science 4 4%
Environmental Science 3 3%
Mathematics 2 2%
Other 6 6%
Unknown 15 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 45. 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 13 June 2023.
All research outputs
#941,573
of 25,646,963 outputs
Outputs from BMC Bioinformatics
#64
of 7,735 outputs
Outputs of similar age
#11,100
of 281,331 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 114 outputs
Altmetric has tracked 25,646,963 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 7,735 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 done particularly well, scoring higher than 99% 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 281,331 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 96% of its contemporaries.
We're also able to compare this research output to 114 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.