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pycoMeth: a toolbox for differential methylation testing from Nanopore methylation calls

Overview of attention for article published in Genome Biology, April 2023
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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 (89th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

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37 X users

Citations

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4 Dimensions

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30 Mendeley
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Title
pycoMeth: a toolbox for differential methylation testing from Nanopore methylation calls
Published in
Genome Biology, April 2023
DOI 10.1186/s13059-023-02917-w
Pubmed ID
Authors

Rene Snajder, Adrien Leger, Oliver Stegle, Marc Jan Bonder

Abstract

We present pycoMeth, a toolbox to store, manage and analyze DNA methylation calls from long-read sequencing data obtained using the Oxford Nanopore Technologies sequencing platform. Building on a novel, rapid-access, read-level and reference-anchored methylation storage format MetH5, we propose efficient algorithms for haplotype aware, multi-sample consensus segmentation and differential methylation testing. We show that MetH5 is more efficient than existing solutions for storing Oxford Nanopore Technologies methylation calls, and carry out benchmarking for pycoMeth segmentation and differential methylation testing, demonstrating increased performance and sensitivity compared to existing solutions designed for short-read methylation data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 37 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 20%
Researcher 5 17%
Student > Master 3 10%
Student > Doctoral Student 2 7%
Professor 2 7%
Other 4 13%
Unknown 8 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 40%
Agricultural and Biological Sciences 3 10%
Computer Science 2 7%
Engineering 2 7%
Medicine and Dentistry 1 3%
Other 0 0%
Unknown 10 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 22 May 2023.
All research outputs
#2,282,252
of 25,587,485 outputs
Outputs from Genome Biology
#1,877
of 4,492 outputs
Outputs of similar age
#44,687
of 414,350 outputs
Outputs of similar age from Genome Biology
#41
of 89 outputs
Altmetric has tracked 25,587,485 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,492 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 gotten more attention than average, scoring higher than 58% 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 414,350 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 89 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 55% of its contemporaries.