Gaussian mixture model-based unsupervised nucleotide modification number detection using nanopore-sequencing readouts
Autor: | Benedict Paten, Hugh E. Olsen, Andrew D Bailey, Hongxu Ding, Miten Jain |
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Rok vydání: | 2020 |
Předmět: |
Statistics and Probability
Computer science Computational biology Biochemistry Signal 03 medical and health sciences chemistry.chemical_compound Nanopores 0302 clinical medicine Nucleotide Molecular Biology 030304 developmental biology chemistry.chemical_classification 0303 health sciences Nucleotides RNA High-Throughput Nucleotide Sequencing Sequence Analysis DNA Mixture model Original Papers Computer Science Applications Computational Mathematics Tree (data structure) Computational Theory and Mathematics chemistry Nanopore sequencing 030217 neurology & neurosurgery DNA Software |
Zdroj: | Bioinformatics |
ISSN: | 1367-4811 |
Popis: | 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. |
Databáze: | OpenAIRE |
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