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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
  • Among the highest-scoring outputs from this source (#43 of 5,719)
  • High Attention Score compared to outputs of the same age (96th percentile)

Mentioned by

blogs
2 blogs
twitter
50 tweeters

Citations

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15 Dimensions

Readers on

mendeley
86 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 .

Twitter Demographics

The data shown below were collected from the profiles of 50 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 6%
Germany 3 3%
Brazil 2 2%
United Kingdom 2 2%
France 1 1%
Sweden 1 1%
Belgium 1 1%
Spain 1 1%
Japan 1 1%
Other 1 1%
Unknown 68 79%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 34%
Researcher 27 31%
Student > Master 5 6%
Other 4 5%
Professor 4 5%
Other 10 12%
Unknown 7 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 60%
Biochemistry, Genetics and Molecular Biology 11 13%
Environmental Science 3 3%
Computer Science 2 2%
Mathematics 2 2%
Other 5 6%
Unknown 11 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 41. 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 July 2016.
All research outputs
#544,768
of 15,835,002 outputs
Outputs from BMC Bioinformatics
#43
of 5,719 outputs
Outputs of similar age
#5,477
of 157,949 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 4 outputs
Altmetric has tracked 15,835,002 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 5,719 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. 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 157,949 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 4 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