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Ion Torrent sequencing as a tool for mutation discovery in the flax (Linum usitatissimum L.) genome

Overview of attention for article published in Plant Methods, March 2015
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Title
Ion Torrent sequencing as a tool for mutation discovery in the flax (Linum usitatissimum L.) genome
Published in
Plant Methods, March 2015
DOI 10.1186/s13007-015-0062-x
Pubmed ID
Authors

Leonardo Galindo-González, David Pinzón-Latorre, Erik A Bergen, Dustin C Jensen, Michael K Deyholos

Abstract

Detection of induced mutations is valuable for inferring gene function and for developing novel germplasm for crop improvement. Many reverse genetics approaches have been developed to identify mutations in genes of interest within a mutagenized population, including some approaches that rely on next-generation sequencing (e.g. exome capture, whole genome resequencing). As an alternative to these genome or exome-scale methods, we sought to develop a scalable and efficient method for detection of induced mutations that could be applied to a small number of target genes, using Ion Torrent technology. We developed this method in flax (Linum usitatissimum), to demonstrate its utility in a crop species.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Spain 1 2%
Norway 1 2%
Unknown 55 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 19%
Student > Ph. D. Student 11 19%
Student > Master 9 16%
Student > Bachelor 5 9%
Student > Doctoral Student 3 5%
Other 10 17%
Unknown 9 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 48%
Biochemistry, Genetics and Molecular Biology 9 16%
Medicine and Dentistry 3 5%
Engineering 3 5%
Unspecified 2 3%
Other 3 5%
Unknown 10 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 June 2015.
All research outputs
#16,722,190
of 25,374,917 outputs
Outputs from Plant Methods
#894
of 1,262 outputs
Outputs of similar age
#159,684
of 277,672 outputs
Outputs of similar age from Plant Methods
#16
of 22 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,262 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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 277,672 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.