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Combinatorial Pooling Enables Selective Sequencing of the Barley Gene Space

Overview of attention for article published in PLoS Computational Biology, April 2013
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

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1 blog
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1 Facebook page

Citations

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21 Dimensions

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51 Mendeley
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2 CiteULike
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Title
Combinatorial Pooling Enables Selective Sequencing of the Barley Gene Space
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1003010
Pubmed ID
Authors

Stefano Lonardi, Denisa Duma, Matthew Alpert, Francesca Cordero, Marco Beccuti, Prasanna R. Bhat, Yonghui Wu, Gianfranco Ciardo, Burair Alsaihati, Yaqin Ma, Steve Wanamaker, Josh Resnik, Serdar Bozdag, Ming-Cheng Luo, Timothy J. Close

Abstract

For the vast majority of species - including many economically or ecologically important organisms, progress in biological research is hampered due to the lack of a reference genome sequence. Despite recent advances in sequencing technologies, several factors still limit the availability of such a critical resource. At the same time, many research groups and international consortia have already produced BAC libraries and physical maps and now are in a position to proceed with the development of whole-genome sequences organized around a physical map anchored to a genetic map. We propose a BAC-by-BAC sequencing protocol that combines combinatorial pooling design and second-generation sequencing technology to efficiently approach denovo selective genome sequencing. We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when preparing sequencing libraries for hundreds or thousands of DNA samples, such as in this case gene-bearing minimum-tiling-path BAC clones. The novelty of the protocol hinges on the computational ability to efficiently compare hundred millions of short reads and assign them to the correct BAC clones (deconvolution) so that the assembly can be carried out clone-by-clone. Experimental results on simulated data for the rice genome show that the deconvolution is very accurate, and the resulting BAC assemblies have high quality. Results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate and the BAC assemblies have good quality. While our method cannot provide the level of completeness that one would achieve with a comprehensive whole-genome sequencing project, we show that it is quite successful in reconstructing the gene sequences within BACs. In the case of plants such as barley, this level of sequence knowledge is sufficient to support critical end-point objectives such as map-based cloning and marker-assisted breeding.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 6%
Spain 1 2%
Czechia 1 2%
Canada 1 2%
Unknown 45 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 37%
Student > Ph. D. Student 6 12%
Student > Master 4 8%
Professor 3 6%
Student > Doctoral Student 3 6%
Other 9 18%
Unknown 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 51%
Computer Science 8 16%
Biochemistry, Genetics and Molecular Biology 6 12%
Arts and Humanities 1 2%
Medicine and Dentistry 1 2%
Other 2 4%
Unknown 7 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 August 2013.
All research outputs
#2,784,505
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#2,463
of 9,043 outputs
Outputs of similar age
#22,547
of 213,621 outputs
Outputs of similar age from PLoS Computational Biology
#24
of 158 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 72% 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 213,621 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 89% of its contemporaries.
We're also able to compare this research output to 158 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.