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A consensus approach to vertebrate de novo transcriptome assembly from RNA-seq data: assembly of the duck (Anas platyrhynchos) transcriptome

Overview of attention for article published in Frontiers in Genetics, June 2014
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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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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1 blog
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38 Dimensions

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78 Mendeley
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Title
A consensus approach to vertebrate de novo transcriptome assembly from RNA-seq data: assembly of the duck (Anas platyrhynchos) transcriptome
Published in
Frontiers in Genetics, June 2014
DOI 10.3389/fgene.2014.00190
Pubmed ID
Authors

Joanna Moreton, Stephen P. Dunham, Richard D. Emes

Abstract

For vertebrate organisms where a reference genome is not available, de novo transcriptome assembly enables a cost effective insight into the identification of tissue specific or differentially expressed genes and variation of the coding part of the genome. However, since there are a number of different tools and parameters that can be used to reconstruct transcripts, it is difficult to determine an optimal method. Here we suggest a pipeline based on (1) assessing the performance of three different assembly tools (2) using both single and multiple k-mer (MK) approaches (3) examining the influence of the number of reads used in the assembly (4) merging assemblies from different tools. We use an example dataset from the vertebrate Anas platyrhynchos domestica (Pekin duck). We find that taking a subset of data enables a robust assembly to be produced by multiple methods without the need for very high memory capacity. The use of reads mapped back to transcripts (RMBT) and CEGMA (Core Eukaryotic Genes Mapping Approach) provides useful metrics to determine the completeness of assembly obtained. For this dataset the use of MK in the assembly generated a more complete assembly as measured by greater number of RMBT and CEGMA score. Merged single k-mer assemblies are generally smaller but consist of longer transcripts, suggesting an assembly consisting of fewer fragmented transcripts. We suggest that the use of a subset of reads during assembly allows the relatively rapid investigation of assembly characteristics and can guide the user to the most appropriate transcriptome for particular downstream use. Transcriptomes generated by the compared assembly methods and the final merged assembly are freely available for download at http://dx.doi.org/10.6084/m9.figshare.1032613.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Netherlands 1 1%
United Kingdom 1 1%
Portugal 1 1%
Argentina 1 1%
Taiwan 1 1%
Japan 1 1%
Spain 1 1%
Unknown 69 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 27%
Researcher 16 21%
Student > Master 10 13%
Professor > Associate Professor 4 5%
Professor 4 5%
Other 13 17%
Unknown 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 59%
Biochemistry, Genetics and Molecular Biology 11 14%
Environmental Science 4 5%
Computer Science 2 3%
Immunology and Microbiology 2 3%
Other 2 3%
Unknown 11 14%
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 15 January 2015.
All research outputs
#2,920,663
of 22,757,090 outputs
Outputs from Frontiers in Genetics
#843
of 11,758 outputs
Outputs of similar age
#30,477
of 227,909 outputs
Outputs of similar age from Frontiers in Genetics
#14
of 130 outputs
Altmetric has tracked 22,757,090 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 11,758 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 92% 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 227,909 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 86% of its contemporaries.
We're also able to compare this research output to 130 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.