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Characterizing and measuring bias in sequence data

Overview of attention for article published in Genome Biology (Online Edition), January 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
3 blogs
twitter
85 tweeters
patent
1 patent
googleplus
1 Google+ user
f1000
1 research highlight platform

Citations

dimensions_citation
615 Dimensions

Readers on

mendeley
1217 Mendeley
citeulike
14 CiteULike
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Title
Characterizing and measuring bias in sequence data
Published in
Genome Biology (Online Edition), January 2013
DOI 10.1186/gb-2013-14-5-r51
Pubmed ID
Authors

Michael G Ross, Carsten Russ, Maura Costello, Andrew Hollinger, Niall J Lennon, Ryan Hegarty, Chad Nusbaum, David B Jaffe

Abstract

DNA sequencing technologies deviate from the ideal uniform distribution of reads. These biases impair scientific and medical applications. Accordingly, we have developed computational methods for discovering, describing and measuring bias. We applied these methods to the Illumina, Ion Torrent, Pacific Biosciences and Complete Genomics sequencing platforms, using data from human and from a set of microbes with diverse base compositions. As in previous work, library construction conditions significantly influence sequencing bias. Pacific Biosciences coverage levels are the least biased, followed by Illumina, although all technologies exhibit error-rate biases in high- and low-GC regions and at long homopolymer runs. The GC-rich regions prone to low coverage include a number of human promoters, so we therefore catalog 1,000 that were exceptionally resistant to sequencing. Our results indicate that combining data from two technologies can reduce coverage bias if the biases in the component technologies are complementary and of similar magnitude. Analysis of Illumina data representing 120-fold coverage of a well-studied human sample reveals that 0.20% of the autosomal genome was covered at less than 10% of the genome-wide average. Excluding locations that were similar to known bias motifs or likely due to sample-reference variations left only 0.045% of the autosomal genome with unexplained poor coverage. The assays presented in this paper provide a comprehensive view of sequencing bias, which can be used to drive laboratory improvements and to monitor production processes. Development guided by these assays should result in improved genome assemblies and better coverage of biologically important loci.

Twitter Demographics

The data shown below were collected from the profiles of 85 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 1,217 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 28 2%
United Kingdom 13 1%
France 5 <1%
Germany 5 <1%
Spain 5 <1%
Brazil 4 <1%
Norway 4 <1%
Canada 4 <1%
Israel 3 <1%
Other 29 2%
Unknown 1117 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 293 24%
Researcher 283 23%
Student > Master 178 15%
Student > Bachelor 104 9%
Other 61 5%
Other 192 16%
Unknown 106 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 533 44%
Biochemistry, Genetics and Molecular Biology 285 23%
Computer Science 88 7%
Medicine and Dentistry 46 4%
Immunology and Microbiology 24 2%
Other 105 9%
Unknown 136 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 78. 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 13 April 2019.
All research outputs
#429,589
of 21,745,818 outputs
Outputs from Genome Biology (Online Edition)
#300
of 4,003 outputs
Outputs of similar age
#3,063
of 176,582 outputs
Outputs of similar age from Genome Biology (Online Edition)
#2
of 33 outputs
Altmetric has tracked 21,745,818 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,003 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.7. 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 176,582 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 98% of its contemporaries.
We're also able to compare this research output to 33 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 96% of its contemporaries.