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Characterization of background noise in capture-based targeted sequencing data

Overview of attention for article published in Genome Biology (Online Edition), July 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

twitter
22 tweeters
patent
1 patent
facebook
1 Facebook page

Citations

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46 Dimensions

Readers on

mendeley
87 Mendeley
citeulike
2 CiteULike
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Title
Characterization of background noise in capture-based targeted sequencing data
Published in
Genome Biology (Online Edition), 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.

Twitter Demographics

The data shown below were collected from the profiles of 22 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 87 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 24%
Student > Ph. D. Student 19 22%
Student > Master 12 14%
Student > Bachelor 6 7%
Other 5 6%
Other 11 13%
Unknown 13 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 38%
Agricultural and Biological Sciences 25 29%
Medicine and Dentistry 7 8%
Computer Science 2 2%
Environmental Science 1 1%
Other 7 8%
Unknown 12 14%

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 30 January 2020.
All research outputs
#1,903,270
of 21,510,091 outputs
Outputs from Genome Biology (Online Edition)
#1,705
of 3,980 outputs
Outputs of similar age
#38,387
of 288,579 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 21,510,091 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,980 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 57% 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 288,579 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them