↓ Skip to main content

Characterizing and measuring bias in sequence data

Overview of attention for article published in Genome Biology, May 2013
Altmetric Badge

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 (91st percentile)

Mentioned by

blogs
3 blogs
twitter
83 X users
patent
3 patents
googleplus
1 Google+ user
f1000
1 research highlight platform

Citations

dimensions_citation
719 Dimensions

Readers on

mendeley
1286 Mendeley
citeulike
14 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
Characterizing and measuring bias in sequence data
Published in
Genome Biology, May 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 83 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 1,286 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 1186 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 297 23%
Researcher 285 22%
Student > Master 175 14%
Student > Bachelor 112 9%
Other 62 5%
Other 207 16%
Unknown 148 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 531 41%
Biochemistry, Genetics and Molecular Biology 296 23%
Computer Science 88 7%
Medicine and Dentistry 51 4%
Immunology and Microbiology 30 2%
Other 108 8%
Unknown 182 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 80. 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 10 October 2023.
All research outputs
#534,884
of 25,374,647 outputs
Outputs from Genome Biology
#308
of 4,467 outputs
Outputs of similar age
#3,771
of 207,675 outputs
Outputs of similar age from Genome Biology
#6
of 67 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 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 done particularly well, scoring higher than 93% 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 207,675 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 67 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.