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TILLING by sequencing to identify induced mutations in stress resistance genes of peanut (Arachis hypogaea)

Overview of attention for article published in BMC Genomics, March 2015
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Title
TILLING by sequencing to identify induced mutations in stress resistance genes of peanut (Arachis hypogaea)
Published in
BMC Genomics, March 2015
DOI 10.1186/s12864-015-1348-0
Pubmed ID
Authors

Yufang Guo, Brian Abernathy, Yajuan Zeng, Peggy Ozias-Akins

Abstract

Targeting Induced Local Lesions in Genomes (TILLING) is a powerful reverse genetics approach for functional genomics studies. We used high-throughput sequencing, combined with a two-dimensional pooling strategy, with either minimum read percentage with non-reference nucleotide or minimum variance multiplier as mutation prediction parameters, to detect genes related to abiotic and biotic stress resistances. In peanut, lipoxygenase genes were reported to be highly induced in mature seeds infected with Aspergillus spp., indicating their importance in plant-fungus interactions. Recent studies showed that phospholipase D (PLD) expression was elevated more quickly in drought sensitive lines than in drought tolerant lines of peanut. A newly discovered lipoxygenase (LOX) gene in peanut, along with two peanut PLD genes from previous publications were selected for TILLING. Additionally, two major allergen genes Ara h 1 and Ara h 2, and fatty acid desaturase AhFAD2, a gene which controls the ratio of oleic to linoleic acid in the seed, were also used in our study. The objectives of this research were to develop a suitable TILLING by sequencing method for this allotetraploid, and use this method to identify mutations induced in stress related genes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 1%
Unknown 67 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 28%
Researcher 14 21%
Other 7 10%
Student > Master 7 10%
Student > Bachelor 6 9%
Other 5 7%
Unknown 10 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 60%
Biochemistry, Genetics and Molecular Biology 9 13%
Medicine and Dentistry 3 4%
Unspecified 1 1%
Social Sciences 1 1%
Other 1 1%
Unknown 12 18%
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 18 April 2015.
All research outputs
#13,938,371
of 22,796,179 outputs
Outputs from BMC Genomics
#5,345
of 10,648 outputs
Outputs of similar age
#131,069
of 258,831 outputs
Outputs of similar age from BMC Genomics
#150
of 308 outputs
Altmetric has tracked 22,796,179 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,648 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 46th percentile – i.e., 46% 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 258,831 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 308 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.