Literature DB >> 32597959

Gaussian mixture model-based unsupervised nucleotide modification number detection using nanopore-sequencing readouts.

Hongxu Ding1, Andrew D Bailey1, Miten Jain1, Hugh Olsen1, Benedict Paten1.   

Abstract

MOTIVATION: Nucleotide modification status can be decoded from the Oxford Nanopore Technologies nanopore-sequencing ionic current signals. Although various algorithms have been developed for nanopore-sequencing-based modification analysis, more detailed characterizations, such as modification numbers, corresponding signal levels and proportions are still lacking.
RESULTS: We present a framework for the unsupervised determination of the number of nucleotide modifications from nanopore-sequencing readouts. We demonstrate the approach can effectively recapitulate the number of modifications, the corresponding ionic current signal levels, as well as mixing proportions under both DNA and RNA contexts. We further show, by integrating information from multiple detected modification regions, that the modification status of DNA and RNA molecules can be inferred. This method forms a key step of de novo characterization of nucleotide modifications, shedding light on the interpretation of various biological questions.
AVAILABILITY AND IMPLEMENTATION: Modified nanopolish: https://github.com/adbailey4/nanopolish/tree/cigar_output. All other codes used to reproduce the results: https://github.com/hd2326/ModificationNumber. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Year:  2020        PMID: 32597959      PMCID: PMC7723331          DOI: 10.1093/bioinformatics/btaa601

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


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