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Analysis of high-depth sequence data for studying viral diversity: a comparison of next generation sequencing platforms using Segminator II

Overview of attention for article published in BMC Bioinformatics, March 2012
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
146 Mendeley
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6 CiteULike
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Title
Analysis of high-depth sequence data for studying viral diversity: a comparison of next generation sequencing platforms using Segminator II
Published in
BMC Bioinformatics, March 2012
DOI 10.1186/1471-2105-13-47
Pubmed ID
Authors

John Archer, Greg Baillie, Simon J Watson, Paul Kellam, Andrew Rambaut, David L Robertson

Abstract

Next generation sequencing provides detailed insight into the variation present within viral populations, introducing the possibility of treatment strategies that are both reactive and predictive. Current software tools, however, need to be scaled up to accommodate for high-depth viral data sets, which are often temporally or spatially linked. In addition, due to the development of novel sequencing platforms and chemistries, each with implicit strengths and weaknesses, it will be helpful for researchers to be able to routinely compare and combine data sets from different platforms/chemistries. In particular, error associated with a specific sequencing process must be quantified so that true biological variation may be identified. Segminator II was developed to allow for the efficient comparison of data sets derived from different sources. We demonstrate its usage by comparing large data sets from 12 influenza H1N1 samples sequenced on both the 454 Life Sciences and Illumina platforms, permitting quantification of platform error. For mismatches median error rates at 0.10 and 0.12%, respectively, suggested that both platforms performed similarly. For insertions and deletions median error rates within the 454 data (at 0.3 and 0.2%, respectively) were significantly higher than those within the Illumina data (0.004 and 0.006%, respectively). In agreement with previous observations these higher rates were strongly associated with homopolymeric stretches on the 454 platform. Outside of such regions both platforms had similar indel error profiles. Additionally, we apply our software to the identification of low frequency variants. We have demonstrated, using Segminator II, that it is possible to distinguish platform specific error from biological variation using data derived from two different platforms. We have used this approach to quantify the amount of error present within the 454 and Illumina platforms in relation to genomic location as well as location on the read. Given that next generation data is increasingly important in the analysis of drug-resistance and vaccine trials, this software will be useful to the pathogen research community. A zip file containing the source code and jar file is freely available for download from http://www.bioinf.manchester.ac.uk/segminator/.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
Brazil 2 1%
United Kingdom 2 1%
Sweden 2 1%
Switzerland 1 <1%
Netherlands 1 <1%
Colombia 1 <1%
Italy 1 <1%
Argentina 1 <1%
Other 3 2%
Unknown 127 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 44 30%
Student > Ph. D. Student 37 25%
Student > Master 17 12%
Professor > Associate Professor 11 8%
Student > Bachelor 9 6%
Other 24 16%
Unknown 4 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 90 62%
Biochemistry, Genetics and Molecular Biology 16 11%
Medicine and Dentistry 9 6%
Computer Science 7 5%
Immunology and Microbiology 4 3%
Other 9 6%
Unknown 11 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 09 July 2013.
All research outputs
#5,846,568
of 11,330,364 outputs
Outputs from BMC Bioinformatics
#2,075
of 4,197 outputs
Outputs of similar age
#43,473
of 103,803 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 75 outputs
Altmetric has tracked 11,330,364 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,197 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 103,803 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 75 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.