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Pre-capture multiplexing improves efficiency and cost-effectiveness of targeted genomic enrichment

Overview of attention for article published in BMC Genomics, November 2012
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Title
Pre-capture multiplexing improves efficiency and cost-effectiveness of targeted genomic enrichment
Published in
BMC Genomics, November 2012
DOI 10.1186/1471-2164-13-618
Pubmed ID
Authors

A Eliot Shearer, Michael S Hildebrand, Harini Ravi, Swati Joshi, Angelica C Guiffre, Barbara Novak, Scott Happe, Emily M LeProust, Richard JH Smith

Abstract

Targeted genomic enrichment (TGE) is a widely used method for isolating and enriching specific genomic regions prior to massively parallel sequencing. To make effective use of sequencer output, barcoding and sample pooling (multiplexing) after TGE and prior to sequencing (post-capture multiplexing) has become routine. While previous reports have indicated that multiplexing prior to capture (pre-capture multiplexing) is feasible, no thorough examination of the effect of this method has been completed on a large number of samples. Here we compare standard post-capture TGE to two levels of pre-capture multiplexing: 12 or 16 samples per pool. We evaluated these methods using standard TGE metrics and determined the ability to identify several classes of genetic mutations in three sets of 96 samples, including 48 controls. Our overall goal was to maximize cost reduction and minimize experimental time while maintaining a high percentage of reads on target and a high depth of coverage at thresholds required for variant detection.

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The data shown below were collected from the profiles of 2 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
United Kingdom 1 2%
Sweden 1 2%
Australia 1 2%
Unknown 57 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 40%
Student > Master 8 13%
Student > Ph. D. Student 7 11%
Other 5 8%
Professor 3 5%
Other 7 11%
Unknown 7 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 56%
Biochemistry, Genetics and Molecular Biology 13 21%
Medicine and Dentistry 4 6%
Psychology 2 3%
Engineering 2 3%
Other 0 0%
Unknown 6 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 14 November 2012.
All research outputs
#15,256,044
of 22,685,926 outputs
Outputs from BMC Genomics
#6,660
of 10,616 outputs
Outputs of similar age
#112,615
of 179,003 outputs
Outputs of similar age from BMC Genomics
#95
of 146 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,616 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.