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Characterization of background noise in capture-based targeted sequencing data

Overview of attention for article published in Genome Biology, July 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 (86th percentile)
  • Average Attention Score compared to outputs of the same age and source

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21 X users
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2 patents
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1 Facebook page

Citations

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51 Dimensions

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94 Mendeley
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2 CiteULike
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Title
Characterization of background noise in capture-based targeted sequencing data
Published in
Genome Biology, July 2017
DOI 10.1186/s13059-017-1275-2
Pubmed ID
Authors

Gahee Park, Joo Kyung Park, Seung-Ho Shin, Hyo-Jeong Jeon, Nayoung K. D. Kim, Yeon Jeong Kim, Hyun-Tae Shin, Eunjin Lee, Kwang Hyuck Lee, Dae-Soon Son, Woong-Yang Park, Donghyun Park

Abstract

Targeted deep sequencing is increasingly used to detect low-allelic fraction variants; it is therefore essential that errors that constitute baseline noise and impose a practical limit on detection are characterized. In the present study, we systematically evaluate the extent to which errors are incurred during specific steps of the capture-based targeted sequencing process. We removed most sequencing artifacts by filtering out low-quality bases and then analyze the remaining background noise. By recognizing that plasma DNA is naturally fragmented to be of a size comparable to that of mono-nucleosomal DNA, we were able to identify and characterize errors that are specifically associated with acoustic shearing. Two-thirds of C:G > A:T errors and one quarter of C:G > G:C errors were attributed to the oxidation of guanine during acoustic shearing, and this was further validated by comparative experiments conducted under different shearing conditions. The acoustic shearing step also causes A > G and A > T substitutions localized to the end bases of sheared DNA fragments, indicating a probable association of these errors with DNA breakage. Finally, the hybrid selection step contributes to one-third of the remaining C:G > A:T and one-fifth of the C > T errors. The results of this study provide a comprehensive summary of various errors incurred during targeted deep sequencing, and their underlying causes. This information will be invaluable to drive technical improvements in this sequencing method, and may increase the future usage of targeted deep sequencing methods for low-allelic fraction variant detection.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 94 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 23%
Student > Ph. D. Student 20 21%
Student > Master 13 14%
Student > Bachelor 6 6%
Other 5 5%
Other 12 13%
Unknown 16 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 37%
Agricultural and Biological Sciences 25 27%
Medicine and Dentistry 7 7%
Immunology and Microbiology 2 2%
Computer Science 2 2%
Other 7 7%
Unknown 16 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 February 2023.
All research outputs
#2,415,498
of 25,736,439 outputs
Outputs from Genome Biology
#1,948
of 4,509 outputs
Outputs of similar age
#43,673
of 325,594 outputs
Outputs of similar age from Genome Biology
#37
of 60 outputs
Altmetric has tracked 25,736,439 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,509 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 56% 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 325,594 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 86% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.