↓ Skip to main content

DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation

Overview of attention for article published in Genome Biology (Online Edition), October 2021
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 (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

blogs
1 blog
twitter
44 tweeters

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
157 Mendeley
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
DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation
Published in
Genome Biology (Online Edition), October 2021
DOI 10.1186/s13059-021-02510-z
Pubmed ID
Authors

Yang Liu, Wojciech Rosikiewicz, Ziwei Pan, Nathaniel Jillette, Ping Wang, Aziz Taghbalout, Jonathan Foox, Christopher Mason, Martin Carroll, Albert Cheng, Sheng Li

Abstract

Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation-calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies. We compare seven analytic tools for detecting DNA methylation from nanopore long-read sequencing data generated from human natural DNA at a whole-genome scale. We evaluate the per-read and per-site performance of CpG methylation prediction across different genomic contexts, CpG site coverage, and computational resources consumed by each tool. The seven tools exhibit different performances across the evaluation criteria. We show that the methylation prediction at regions with discordant DNA methylation patterns, intergenic regions, low CG density regions, and repetitive regions show room for improvement across all tools. Furthermore, we demonstrate that 5hmC levels at least partly contribute to the discrepancy between bisulfite and nanopore sequencing. Lastly, we provide an online DNA methylation database ( https://nanome.jax.org ) to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts. Our study is the first systematic benchmark of computational methods for detection of mammalian whole-genome DNA modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization and an evaluation of analytical tools designed for genome-scale modified base detection using nanopore sequencing.

Twitter Demographics

The data shown below were collected from the profiles of 44 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 157 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 157 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 24%
Student > Ph. D. Student 27 17%
Student > Bachelor 10 6%
Student > Doctoral Student 7 4%
Professor 6 4%
Other 25 16%
Unknown 45 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 44 28%
Agricultural and Biological Sciences 31 20%
Computer Science 8 5%
Engineering 5 3%
Medicine and Dentistry 4 3%
Other 15 10%
Unknown 50 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 01 August 2022.
All research outputs
#1,104,481
of 22,113,391 outputs
Outputs from Genome Biology (Online Edition)
#957
of 4,037 outputs
Outputs of similar age
#32,479
of 425,401 outputs
Outputs of similar age from Genome Biology (Online Edition)
#109
of 342 outputs
Altmetric has tracked 22,113,391 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,037 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 well, scoring higher than 76% 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 425,401 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 92% of its contemporaries.
We're also able to compare this research output to 342 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 68% of its contemporaries.