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Detecting very low allele fraction variants using targeted DNA sequencing and a novel molecular barcode-aware variant caller

Overview of attention for article published in BMC Genomics, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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1 X user
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5 patents

Citations

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91 Dimensions

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153 Mendeley
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Title
Detecting very low allele fraction variants using targeted DNA sequencing and a novel molecular barcode-aware variant caller
Published in
BMC Genomics, January 2017
DOI 10.1186/s12864-016-3425-4
Pubmed ID
Authors

Chang Xu, Mohammad R. Nezami Ranjbar, Zhong Wu, John DiCarlo, Yexun Wang

Abstract

Detection of DNA mutations at very low allele fractions with high accuracy will significantly improve the effectiveness of precision medicine for cancer patients. To achieve this goal through next generation sequencing, researchers need a detection method that 1) captures rare mutation-containing DNA fragments efficiently in the mix of abundant wild-type DNA; 2) sequences the DNA library extensively to deep coverage; and 3) distinguishes low level true variants from amplification and sequencing errors with high accuracy. Targeted enrichment using PCR primers provides researchers with a convenient way to achieve deep sequencing for a small, yet most relevant region using benchtop sequencers. Molecular barcoding (or indexing) provides a unique solution for reducing sequencing artifacts analytically. Although different molecular barcoding schemes have been reported in recent literature, most variant calling has been done on limited targets, using simple custom scripts. The analytical performance of barcode-aware variant calling can be significantly improved by incorporating advanced statistical models. We present here a highly efficient, simple and scalable enrichment protocol that integrates molecular barcodes in multiplex PCR amplification. In addition, we developed smCounter, an open source, generic, barcode-aware variant caller based on a Bayesian probabilistic model. smCounter was optimized and benchmarked on two independent read sets with SNVs and indels at 5 and 1% allele fractions. Variants were called with very good sensitivity and specificity within coding regions. We demonstrated that we can accurately detect somatic mutations with allele fractions as low as 1% in coding regions using our enrichment protocol and variant caller.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 <1%
Unknown 152 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 21%
Student > Ph. D. Student 27 18%
Student > Master 20 13%
Student > Bachelor 14 9%
Other 11 7%
Other 17 11%
Unknown 32 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 44 29%
Agricultural and Biological Sciences 27 18%
Medicine and Dentistry 15 10%
Computer Science 14 9%
Neuroscience 4 3%
Other 15 10%
Unknown 34 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 23 May 2023.
All research outputs
#3,310,990
of 23,873,907 outputs
Outputs from BMC Genomics
#1,208
of 10,791 outputs
Outputs of similar age
#67,145
of 425,776 outputs
Outputs of similar age from BMC Genomics
#35
of 227 outputs
Altmetric has tracked 23,873,907 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,791 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 88% 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 425,776 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 227 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.