↓ Skip to main content

High-specificity detection of rare alleles with Paired-End Low Error Sequencing (PELE-Seq)

Overview of attention for article published in BMC Genomics, June 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
50 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
High-specificity detection of rare alleles with Paired-End Low Error Sequencing (PELE-Seq)
Published in
BMC Genomics, June 2016
DOI 10.1186/s12864-016-2669-3
Pubmed ID
Authors

Jessica L. Preston, Ariel E. Royall, Melissa A. Randel, Kristin L. Sikkink, Patrick C. Phillips, Eric A. Johnson

Abstract

Polymorphic loci exist throughout the genomes of a population and provide the raw genetic material needed for a species to adapt to changes in the environment. The minor allele frequencies of rare Single Nucleotide Polymorphisms (SNPs) within a population have been difficult to track with Next-Generation Sequencing (NGS), due to the high error rate of standard methods such as Illumina sequencing. We have developed a wet-lab protocol and variant-calling method that identifies both sequencing and PCR errors, called Paired-End Low Error Sequencing (PELE-Seq). To test the specificity and sensitivity of the PELE-Seq method, we sequenced control E. coli DNA libraries containing known rare alleles present at frequencies ranging from 0.2-0.4 % of the total reads. PELE-Seq had higher specificity and sensitivity than standard libraries. We then used PELE-Seq to characterize rare alleles in a Caenorhabditis remanei nematode worm population before and after laboratory adaptation, and found that minor and rare alleles can undergo large changes in frequency during lab-adaptation. We have developed a method of rare allele detection that mitigates both sequencing and PCR errors, called PELE-Seq. PELE-Seq was evaluated using control E. coli populations and was then used to compare a wild C. remanei population to a lab-adapted population. The PELE-Seq method is ideal for investigating the dynamics of rare alleles in a broad range of reduced-representation sequencing methods, including targeted amplicon sequencing, RAD-Seq, ddRAD, and GBS. PELE-Seq is also well-suited for whole genome sequencing of mitochondria and viruses, and for high-throughput rare mutation screens.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Unknown 48 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 30%
Student > Ph. D. Student 9 18%
Student > Bachelor 6 12%
Student > Master 5 10%
Student > Doctoral Student 3 6%
Other 6 12%
Unknown 6 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 36%
Biochemistry, Genetics and Molecular Biology 17 34%
Medicine and Dentistry 2 4%
Computer Science 1 2%
Immunology and Microbiology 1 2%
Other 4 8%
Unknown 7 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 12 September 2017.
All research outputs
#5,326,905
of 20,375,306 outputs
Outputs from BMC Genomics
#2,304
of 10,016 outputs
Outputs of similar age
#77,387
of 279,047 outputs
Outputs of similar age from BMC Genomics
#7
of 13 outputs
Altmetric has tracked 20,375,306 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 10,016 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 76% 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 279,047 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 72% of its contemporaries.
We're also able to compare this research output to 13 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 53% of its contemporaries.