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Rapid identification and recovery of ENU-induced mutations with next-generation sequencing and Paired-End Low-Error analysis

Overview of attention for article published in BMC Genomics, February 2015
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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1 policy source
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2 X users

Citations

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31 Dimensions

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58 Mendeley
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Title
Rapid identification and recovery of ENU-induced mutations with next-generation sequencing and Paired-End Low-Error analysis
Published in
BMC Genomics, February 2015
DOI 10.1186/s12864-015-1263-4
Pubmed ID
Authors

Luyuan Pan, Arish N Shah, Ian G Phelps, Dan Doherty, Eric A Johnson, Cecilia B Moens

Abstract

Targeting Induced Local Lesions IN Genomes (TILLING) is a reverse genetics approach to directly identify point mutations in specific genes of interest in genomic DNA from a large chemically mutagenized population. Classical TILLING processes, based on enzymatic detection of mutations in heteroduplex PCR amplicons, are slow and labor intensive. Here we describe a new TILLING strategy in zebrafish using direct next generation sequencing (NGS) of 250bp amplicons followed by Paired-End Low-Error (PELE) sequence analysis. By pooling a genomic DNA library made from over 9,000 N-ethyl-N-nitrosourea (ENU) mutagenized F1 fish into 32 equal pools of 288 fish, each with a unique Illumina barcode, we reduce the complexity of the template to a level at which we can detect mutations that occur in a single heterozygous fish in the entire library. MiSeq sequencing generates 250 base-pair overlapping paired-end reads, and PELE analysis aligns the overlapping sequences to each other and filters out any imperfect matches, thereby eliminating variants introduced during the sequencing process. We find that this filtering step reduces the number of false positive calls 50-fold without loss of true variant calls. After PELE we were able to validate 61.5% of the mutant calls that occurred at a frequency between 1 mutant call:100 wildtype calls and 1 mutant call:1000 wildtype calls in a pool of 288 fish. We then use high-resolution melt analysis to identify the single heterozygous mutation carrier in the 288-fish pool in which the mutation was identified. Using this NGS-TILLING protocol we validated 28 nonsense or splice site mutations in 20 genes, at a two-fold higher efficiency than using traditional Cel1 screening. We conclude that this approach significantly increases screening efficiency and accuracy at reduced cost and can be applied in a wide range of organisms.

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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 %
Austria 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 26%
Student > Ph. D. Student 10 17%
Other 7 12%
Student > Doctoral Student 5 9%
Professor 2 3%
Other 7 12%
Unknown 12 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 40%
Biochemistry, Genetics and Molecular Biology 14 24%
Unspecified 1 2%
Nursing and Health Professions 1 2%
Environmental Science 1 2%
Other 2 3%
Unknown 16 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 01 January 2021.
All research outputs
#6,415,079
of 22,792,160 outputs
Outputs from BMC Genomics
#2,880
of 10,648 outputs
Outputs of similar age
#91,664
of 359,553 outputs
Outputs of similar age from BMC Genomics
#77
of 253 outputs
Altmetric has tracked 22,792,160 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 10,648 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 71% 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 359,553 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 253 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.