Gaussian mixture model-based unsupervised nucleotide modification number detection using nanopore-sequencing readouts
Hongxu Ding, Andrew D. Bailey, Miten Jain, Hugh E. Olsen, Benedict Paten
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.