Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore

RNA modifications, such as N6-methyladenosine (m6A), modulate functions of cellular RNA species. However, quantifying differences in RNA modifications has been challenging. Researchers from A*STAR, Singapore have developed a computational method, xPore, to identify differential RNA modifications from nanopore direct RNA sequencing (RNA-seq) data. The researchers evaluated their method on transcriptome-wide m6A profiling data, demonstrating that xPore identifies positions of m6A sites at single-base resolution, estimates the fraction of modified RNA species in the cell and quantifies the differential modification rate across conditions. They apply xPore to direct RNA-seq data from six cell lines and multiple myeloma patient samples without a matched control sample and find that many m6A sites are preserved across cell types, whereas a subset exhibit significant differences in their modification rates. These results show that RNA modifications can be identified from direct RNA-seq data with high accuracy, enabling analysis of differential modifications and expression from a single high-throughput experiment.

Schematic workflow: quantification of RNA modifications
from direct RNA-seq data using xPore

Fig. 1

a, Example of raw signal data from a direct RNA-seq read. b, A close-up view of the raw signal with the corresponding transcript sequence obtained from basecalling, sequence alignment and signal segmentation. c, Signal of multiple reads aligned at a GGACT site from different samples (orange, green and blue). d, Shown is a histogram of the mean signal from all reads covering a position for three different samples (orange, green and blue). The gray line indicates the expected distribution for unmodified RNA; samples that contain modified RNA species will show a bimodal distribution. e, Graphical representation of the model used by xPore to quantify the modification rate at each position. The gray circle indicates observed variables (data); white circles indicate unobserved variables that are estimated by xPore. f, xPore estimates the parameters for two Gaussian distributions corresponding to modified (black) and unmodified RNA species (blue). g, xPore summarizes the modification rate for each sample. h, xPore models the modification rate jointly for all samples. xPore then identifies differentially modified positions by testing for significant deviation in the DMR across replicates from the conditions of interest.

Availability – The implementation in Python is available at https://github.com/GoekeLab/xpore. xPore’s documentation is available at https://xpore.readthedocs.io.

Pratanwanich PN, Yao F, Chen Y, Koh CWQ, Wan YK, Hendra C, Poon P, Goh YT, Yap PML, Chooi JY, Chng WJ, Ng SB, Thiery A, Goh WSS, Göke J. (2021) Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nat Biotechnol [Epub ahead of print]. [abstract]

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