↓ Skip to main content

Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data

Overview of attention for article published in PLoS Computational Biology, April 2013
Altmetric Badge

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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
49 X users
peer_reviews
1 peer review site
facebook
3 Facebook pages

Citations

dimensions_citation
251 Dimensions

Readers on

mendeley
522 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1003031
Pubmed ID
Authors

Lauren M. Bragg, Glenn Stone, Margaret K. Butler, Philip Hugenholtz, Gene W. Tyson

Abstract

The Ion Torrent Personal Genome Machine (PGM) is a new sequencing platform that substantially differs from other sequencing technologies by measuring pH rather than light to detect polymerisation events. Using re-sequencing datasets, we comprehensively characterise the biases and errors introduced by the PGM at both the base and flow level, across a combination of factors, including chip density, sequencing kit, template species and machine. We found two distinct insertion/deletion (indel) error types that accounted for the majority of errors introduced by the PGM. The main error source was inaccurate flow-calls, which introduced indels at a raw rate of 2.84% (1.38% after quality clipping) using the OneTouch 200 bp kit. Inaccurate flow-calls typically resulted in over-called short-homopolymers and under-called long-homopolymers. Flow-call accuracy decreased with consecutive flow cycles, but we also found significant periodic fluctuations in the flow error-rate, corresponding to specific positions within the flow-cycle pattern. Another less common PGM error, high frequency indel (HFI) errors, are indels that occur at very high frequency in the reads relative to a given base position in the reference genome, but in the majority of instances were not replicated consistently across separate runs. HFI errors occur approximately once every thousand bases in the reference, and correspond to 0.06% of bases in reads. Currently, the PGM does not achieve the accuracy of competing light-based technologies. However, flow-call inaccuracy is systematic and the statistical models of flow-values developed here will enable PGM-specific bioinformatics approaches to be developed, which will account for these errors. HFI errors may prove more challenging to address, especially for polymorphism and amplicon applications, but may be overcome by sequencing the same DNA template across multiple chips.

X Demographics

X Demographics

The data shown below were collected from the profiles of 49 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 522 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 2%
Germany 3 <1%
France 3 <1%
Italy 3 <1%
Sweden 3 <1%
Netherlands 2 <1%
Brazil 2 <1%
Australia 2 <1%
Russia 2 <1%
Other 17 3%
Unknown 472 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 129 25%
Student > Ph. D. Student 109 21%
Student > Master 66 13%
Other 45 9%
Student > Bachelor 31 6%
Other 86 16%
Unknown 56 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 260 50%
Biochemistry, Genetics and Molecular Biology 95 18%
Medicine and Dentistry 29 6%
Computer Science 21 4%
Environmental Science 13 2%
Other 37 7%
Unknown 67 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 08 August 2019.
All research outputs
#1,364,499
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#1,142
of 8,964 outputs
Outputs of similar age
#10,581
of 212,443 outputs
Outputs of similar age from PLoS Computational Biology
#13
of 152 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 87% 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 212,443 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 152 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.