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MOABS: model based analysis of bisulfite sequencing data

Overview of attention for article published in Genome Biology (Online Edition), January 2014
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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 (91st percentile)

Mentioned by

1 blog
14 tweeters
2 Google+ users


204 Dimensions

Readers on

240 Mendeley
1 CiteULike
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MOABS: model based analysis of bisulfite sequencing data
Published in
Genome Biology (Online Edition), January 2014
DOI 10.1186/gb-2014-15-2-r38
Pubmed ID

Deqiang Sun, Yuanxin Xi, Benjamin Rodriguez, Hyun Park, Pan Tong, Mira Meong, Margaret A Goodell, Wei Li


Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher's exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 240 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
Germany 3 1%
India 1 <1%
Norway 1 <1%
Canada 1 <1%
Spain 1 <1%
United Kingdom 1 <1%
Unknown 225 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 70 29%
Researcher 59 25%
Student > Master 21 9%
Professor > Associate Professor 15 6%
Student > Doctoral Student 13 5%
Other 38 16%
Unknown 24 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 115 48%
Biochemistry, Genetics and Molecular Biology 37 15%
Computer Science 19 8%
Mathematics 9 4%
Engineering 7 3%
Other 18 8%
Unknown 35 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 24 December 2014.
All research outputs
of 17,150,239 outputs
Outputs from Genome Biology (Online Edition)
of 3,561 outputs
Outputs of similar age
of 194,079 outputs
Outputs of similar age from Genome Biology (Online Edition)
of 2 outputs
Altmetric has tracked 17,150,239 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,561 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.4. This one has gotten more attention than average, scoring higher than 64% 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 194,079 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 91% of its contemporaries.
We're also able to compare this research output to 2 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