RNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. In recent years, a growing number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing has been shown to be sensitive to RNA modifications.
A team led by researchers at the University of Cambridge developed and validated Nanocompore, a robust analytical framework that identifies modifications from these data. Their strategy compares an RNA sample of interest against a non-modified control sample, not requiring a training set and allowing the use of replicates. The researchers show that Nanocompore can detect different RNA modifications with position accuracy in vitro, and they apply it to profile m6A in vivo in yeast and human RNAs, as well as in targeted non-coding RNAs. They confirm their results with orthogonal methods and provide novel insights on the co-occurrence of multiple modified residues on individual RNA molecules.
Overview of data preparation and Nanocompore steps
A Raw fast5 reads from 2 conditions are basecalled with Guppy, filtered with Samtools and the signal is then resquiggled with Nanopolish eventalign. The output of Nanopolish is then collapsed and indexed at the kmer level by NanopolishComp Eventalign_collapse. B Nanocompore aggregates median intensity and dwell time at transcript position level. The data is compared in a pairwise fashion position per position using univariate tests (KS, MW, t-tests) and/or a bivariate GMM classification method. The p-values are corrected for multiple tests and these data are saved in a database for further analyses. The signal graph is as an illustration not representative of all possible kmers.
Availability – The computational methods and custom scripts used for this paper are available in the following Github repository: https://github.com/tleonardi/nanocompore_paper_analyses.