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Empirical estimation of sequencing error rates using smoothing splines

Overview of attention for article published in BMC Bioinformatics, April 2016
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
1 blog
twitter
15 tweeters

Citations

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3 Dimensions

Readers on

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27 Mendeley
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Title
Empirical estimation of sequencing error rates using smoothing splines
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1052-3
Pubmed ID
Authors

Xuan Zhu, Jian Wang, Bo Peng, Sanjay Shete

Abstract

Next-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling. However, compared to conventional sequencing, the error rates for next-generation sequencing are often higher, which impacts the downstream genomic analysis. Recently, Wang et al. (BMC Bioinformatics 13:185, 2012) proposed a shadow regression approach to estimate the error rates for next-generation sequencing data based on the assumption of a linear relationship between the number of reads sequenced and the number of reads containing errors (denoted as shadows). However, this linear read-shadow relationship may not be appropriate for all types of sequence data. Therefore, it is necessary to estimate the error rates in a more reliable way without assuming linearity. We proposed an empirical error rate estimation approach that employs cubic and robust smoothing splines to model the relationship between the number of reads sequenced and the number of shadows. We performed simulation studies using a frequency-based approach to generate the read and shadow counts directly, which can mimic the real sequence counts data structure. Using simulation, we investigated the performance of the proposed approach and compared it to that of shadow linear regression. The proposed approach provided more accurate error rate estimations than the shadow linear regression approach for all the scenarios tested. We also applied the proposed approach to assess the error rates for the sequence data from the MicroArray Quality Control project, a mutation screening study, the Encyclopedia of DNA Elements project, and bacteriophage PhiX DNA samples. The proposed empirical error rate estimation approach does not assume a linear relationship between the error-free read and shadow counts and provides more accurate estimations of error rates for next-generation, short-read sequencing data.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 4%
United States 1 4%
Unknown 25 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 33%
Student > Ph. D. Student 4 15%
Student > Bachelor 3 11%
Student > Postgraduate 2 7%
Professor 1 4%
Other 5 19%
Unknown 3 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 33%
Agricultural and Biological Sciences 6 22%
Computer Science 4 15%
Unspecified 1 4%
Neuroscience 1 4%
Other 1 4%
Unknown 5 19%

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 05 May 2016.
All research outputs
#1,272,472
of 15,308,819 outputs
Outputs from BMC Bioinformatics
#392
of 5,598 outputs
Outputs of similar age
#30,218
of 265,042 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 8 outputs
Altmetric has tracked 15,308,819 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 5,598 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 92% 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 265,042 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 88% of its contemporaries.
We're also able to compare this research output to 8 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