Researchers from the University of Eastern Finland and Aalto University have developed a computational framework called SegPore, which enhances the accuracy of RNA modification detection from direct RNA nanopore sequencing data.

RNA modifications are essential epigenetic regulators of gene expression and are involved in diverse biological processes, including cell differentiation, stress response and disease progression. Nanopore direct RNA sequencing offered by Oxford Nanopore Technologies provides a potential avenue for detecting RNA modifications at the single-molecule level, based on the direct measurement of electrical currents as RNA molecules translocate through a nanopore. However, accurately identifying these modifications remains a challenge due to the high noise and structural complexity of raw nanopore signals.

SegPore introduces a hierarchical, white-box segmentation model that more faithfully represents the molecular process underlying nanopore signal generation. The model leverages a molecular jiggling translocation hypothesis: instead of moving smoothly in one direction, the motor protein may cause the RNA molecule to slightly jiggle back and forth as it passes through the nanopore. This motor protein acts as a molecular engine that controls the speed and direction of RNA movement through the pore. Small irregularities in its motion can lead to subtle changes in the electrical signal, which SegPore captures and models precisely.  By explicitly modelling these dynamics, SegPore achieves superior segmentation accuracy and improves downstream RNA modification detection compared with existing methods.

SegPore workflow

(A) General workflow. The workflow consists of five steps: First, raw current signals are basecalled and mapped using Guppy and Minimap2. The raw current signal fragments are paired with the corresponding reference RNA sequence fragments using Nanopolish. Next, the raw current signal of each read is segmented using a hierarchical hidden Markov model (HHMM), which provides an estimated mean (𝜇_𝑖) for each segment. These segments are then aligned with the 5mer list of the reference sequence fragment using a full/partial alignment algorithm, based on a 5mer parameter table. For example, 𝐴_𝑗denotes the base “A” at the j-th position on the reference. All signals aligned to the same 5mer across different genomic locations are pooled together, and a two-component Gaussian Mixture Model (GMM) is used to predict the modification at the site-level or single-molecule level. One component of the GMM represents the unmodified state, while the other represents the modified state. GMM is used to re-estimate the 5mer parameter table. (B) Hierarchical hidden Markov model (HHMM). The outer HMM segments the current signal into alternating base and transition blocks. The inner HMM approximates the emission probability of a base block by considering neighboring 5mers. A linear model is used to approximate the emission probability of a transition block. (C) Full/partial alignment algorithms. Rows represent the estimated means of base blocks from the HHMM, and columns represent the 5mers of the reference sequence. Each 5mer can be aligned with multiple estimated means from the current signal. (D) Gaussian mixture model (GMM) for estimating modification states. The GMM consists of two components: the green component models the unmodified state of a 5mer, and the blue component models the modified state. Each component is described by three parameters: mean (μ), standard deviation (σ), and weight (ω).

“By delving deeply into the raw signals of nanopore sequencing, we discovered that the motor protein may move the RNA molecule both forward and backward, rather than strictly in one direction. By modelling this dynamic process, we developed a transparent white-box model that outperforms current state-of-the-art methods on in vitro transcribed datasets,” says Academy Research Fellow Dr Lu Cheng from the Institute of Biomedicine at theUniversity of Eastern Finland.

SegPore enables more reliable interpretation of single-molecule sequencing data, paving the way for improved studies of RNA modification landscapes and their functional roles in health and disease.

Source – University of Eastern Finland

Availability – The source code is hosted on GitHub (https://github.com/guangzhaocs/SegPore).