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FREQ-Seq: A Rapid, Cost-Effective, Sequencing-Based Method to Determine Allele Frequencies Directly from Mixed Populations

Overview of attention for article published in PLOS ONE, October 2012
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Title
FREQ-Seq: A Rapid, Cost-Effective, Sequencing-Based Method to Determine Allele Frequencies Directly from Mixed Populations
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0047959
Pubmed ID
Authors

Lon M. Chubiz, Ming-Chun Lee, Nigel F. Delaney, Christopher J. Marx

Abstract

Understanding evolutionary dynamics within microbial populations requires the ability to accurately follow allele frequencies through time. Here we present a rapid, cost-effective method (FREQ-Seq) that leverages Illumina next-generation sequencing for localized, quantitative allele frequency detection. Analogous to RNA-Seq, FREQ-Seq relies upon counts from the >10(5) reads generated per locus per time-point to determine allele frequencies. Loci of interest are directly amplified from a mixed population via two rounds of PCR using inexpensive, user-designed oligonucleotides and a bar-coded bridging primer system that can be regenerated in-house. The resulting bar-coded PCR products contain the adapters needed for Illumina sequencing, eliminating further library preparation. We demonstrate the utility of FREQ-Seq by determining the order and dynamics of beneficial alleles that arose as a microbial population, founded with an engineered strain of Methylobacterium, evolved to grow on methanol. Quantifying allele frequencies with minimal bias down to 1% abundance allowed effective analysis of SNPs, small in-dels and insertions of transposable elements. Our data reveal large-scale clonal interference during the early stages of adaptation and illustrate the utility of FREQ-Seq as a cost-effective tool for tracking allele frequencies in populations.

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Geographical breakdown

Country Count As %
United States 10 5%
Austria 2 1%
France 2 1%
Belgium 2 1%
Ireland 1 <1%
Italy 1 <1%
Canada 1 <1%
Germany 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 164 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 55 30%
Student > Ph. D. Student 46 25%
Student > Master 23 12%
Professor 10 5%
Student > Doctoral Student 9 5%
Other 31 17%
Unknown 12 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 108 58%
Biochemistry, Genetics and Molecular Biology 25 13%
Immunology and Microbiology 14 8%
Environmental Science 6 3%
Engineering 6 3%
Other 12 6%
Unknown 15 8%