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. |
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United States | 7 | 19% |
Switzerland | 2 | 5% |
Thailand | 2 | 5% |
United Kingdom | 2 | 5% |
Australia | 2 | 5% |
France | 1 | 3% |
India | 1 | 3% |
China | 1 | 3% |
Germany | 1 | 3% |
Other | 1 | 3% |
Unknown | 17 | 46% |
Demographic breakdown
Type | Count | As % |
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Scientists | 19 | 51% |
Members of the public | 16 | 43% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
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 % |
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Agricultural and Biological Sciences | 3 | 10% |
Computer Science | 2 | 7% |
Engineering | 2 | 7% |
Medicine and Dentistry | 1 | 3% |
Other | 0 | 0% |
Unknown | 10 | 33% |