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aTRAM - automated target restricted assembly method: a fast method for assembling loci across divergent taxa from next-generation sequencing data

Overview of attention for article published in BMC Bioinformatics, March 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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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18 X users
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Citations

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117 Mendeley
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1 CiteULike
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Title
aTRAM - automated target restricted assembly method: a fast method for assembling loci across divergent taxa from next-generation sequencing data
Published in
BMC Bioinformatics, March 2015
DOI 10.1186/s12859-015-0515-2
Pubmed ID
Authors

Julie M Allen, Daisie I Huang, Quentin C Cronk, Kevin P Johnson

Abstract

Assembling genes from next-generation sequencing data is not only time consuming but computationally difficult, particularly for taxa without a closely related reference genome. Assembling even a draft genome using de novo approaches can take days, even on a powerful computer, and these assemblies typically require data from a variety of genomic libraries. Here we describe software that will alleviate these issues by rapidly assembling genes from distantly related taxa using a single library of paired-end reads: aTRAM, automated Target Restricted Assembly Method. The aTRAM pipeline uses a reference sequence, BLAST, and an iterative approach to target and locally assemble the genes of interest. Our results demonstrate that aTRAM rapidly assembles genes across distantly related taxa. In comparative tests with a closely related taxon, aTRAM assembled the same sequence as reference-based and de novo approaches taking on average < 1 min per gene. As a test case with divergent sequences, we assembled >1,000 genes from six taxa ranging from 25 - 110 million years divergent from the reference taxon. The gene recovery was between 97 - 99% from each taxon. aTRAM can quickly assemble genes across distantly-related taxa, obviating the need for draft genome assembly of all taxa of interest. Because aTRAM uses a targeted approach, loci can be assembled in minutes depending on the size of the target. Our results suggest that this software will be useful in rapidly assembling genes for phylogenomic projects covering a wide taxonomic range, as well as other applications. The software is freely available http://www.github.com/juliema/aTRAM .

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X Demographics

The data shown below were collected from the profiles of 18 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 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Spain 2 2%
Brazil 1 <1%
Norway 1 <1%
Canada 1 <1%
Unknown 108 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 28%
Researcher 25 21%
Student > Master 15 13%
Professor > Associate Professor 8 7%
Student > Doctoral Student 8 7%
Other 18 15%
Unknown 10 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 72 62%
Biochemistry, Genetics and Molecular Biology 14 12%
Computer Science 6 5%
Environmental Science 5 4%
Mathematics 1 <1%
Other 2 2%
Unknown 17 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 18 July 2017.
All research outputs
#3,085,177
of 24,417,958 outputs
Outputs from BMC Bioinformatics
#998
of 7,530 outputs
Outputs of similar age
#38,860
of 267,808 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 136 outputs
Altmetric has tracked 24,417,958 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,530 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 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 267,808 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 85% of its contemporaries.
We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.