Nanopore sequencing has revolutionized the way scientists analyze DNA and RNA, allowing them to detect genetic sequences and modifications without extra processing steps. However, accurately identifying certain nucleotide modifications—especially in RNA—remains a challenge.

A research team from Johns Hopkins University has developed a new computational toolkit called Uncalled4, which enhances the accuracy of detecting DNA and RNA modifications from nanopore sequencing data. Their findings highlight how this new tool improves the alignment of nanopore signals, leading to better identification of epigenetic modifications such as 6-methyladenine (m6A) in RNA.

Pore model and alignment methods overview

a, Schematics of ONT sequencing chemistries, their pore k-mer model current distributions, and nucleotide compositions of k-mers within current ranges indicated by dashed lines. b, A signal-to-reference dotplot of an Escherichia coli 16S rRNA read sequenced using ONT r9.4 direct RNA sequencing. Top panel shows the raw samples (black) plotted over the reference base it was aligned to, with the expected pore model current in white. Main panel shows the Uncalled4 read alignment (purple line) over the projected basecaller metadata alignment (orange dots). Side panels show per-reference coordinate summary statistics for the alignment. c, Schematic of Uncalled4 inputs, outputs and subcommands. d, A trackplot displaying heatmaps of many native (bottom) and IVT (top) E. coli 16S rRNA reads aligned by Uncalled4, colored by the difference between the observed and expected normalized current level. Top bar is colored by reference base, and an O6-methylguanine site is known to occur at position 526. e, A refplot summarizing the distributions of differences between observed and expected normalized current levels for native (purple) and IVT (green) reads. e, A comparative signal-to-reference dotplot alongside distance (dist.) metrics between Uncalled4 and Nanopolish alignments of the same read, where line breaks in the distance plots correspond to regions masked by Nanopolish. norm., normalized.

The study applied Uncalled4 to human cell lines and identified 26% more m6A modifications than existing tools like Nanopolish, particularly in cancer-related genes. These results suggest that Uncalled4 could be a powerful resource for transcriptomic and epigenetic research, helping scientists uncover new insights into RNA modifications and their roles in disease.

By improving nanopore sequencing accuracy, Uncalled4 brings researchers closer to a more complete understanding of gene regulation, with potential applications in cancer research, developmental biology, and personalized medicine.

Availability – Uncalled4 is available open source at github.com/skovaka/uncalled4.