ArticleBioinformatics advances2023
cvlr: finding heterogeneously methylated genomic regions using ONT reads.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- asms: finding allele-specific methylation in human genomes without phasing.NAR genomics and bioinformatics · 2026Article
- Disentangling oncogenic amplicons in esophageal adenocarcinoma.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: Nanopore reads encode information on the methylation status of cytosines in CpG dinucleotides. The length of the reads makes it comparatively easy to look at patterns consisting of multiple loci; here, we exploit this property to search for regions where one can define subpopulations of molecules based on methylation patterns. As an example, we run our clustering algorithm on known imprinted genes; we also scan chromosome 15 looking for windows corresponding to heterogeneous methylation. Our software can also compute the covariance of methylation across these regions while keeping into account the mixture of different types of reads. Availability and implementation: https://github.com/EmanueleRaineri/cvlr. Contact: simon.heath@cnag.crg.eu. Supplementary information: Supplementary data are available at
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.