Fishnet enables fast and accessible signal-to-sequence alignment of Nanopore sequencing data

Chemical modifications of nucleic acids expand the functional and structural diversity of both the epigenome and epitranscriptome, influencing essential biological processes such as gene regulation, differentiation, and disease development. While next-generation sequencing (NGS) approaches enable indirect detection of such modifications through chemical or antibody-based treatments, direct detection using Oxford Nanopore Technologies (ONT) has emerged as a powerful alternative. Nanopore sequencing preserves native DNA and RNA modifications and encodes their presence in characteristic signal deviations, which can be exploited by computational models to identify and quantify modification sites. A crucial step in this analysis is the alignment of raw nanopore current signals to their corresponding base sequences - known as signal-to-sequence alignment or resquiggling - which enables base-level resolution of signal features. Existing tools for this task are somewhat limited by outdated dependencies, deprecated format support, or complicated processing workflows. Here, we introduce Fishnet, a lightweight and high-performance signal-to-sequence alignment tool designed for efficient processing of nanopore data. Fishnet reimplements the Remora alignment algorithm, producing equivalent alignments with minimal deviation, while offering up to 25-fold faster execution through optimized multithreading. It supports current ONT chemistries and data formats, including Pod5, and provides additional utilities for downstream data reformatting and filtering to facilitate exploratory analysis and machine learning applications. Distributed as a single executable for Linux and Windows, Fishnet requires no installation or complex setup. We benchmark Fishnet against established tools, demonstrating comparable alignments and in part significantly improved performance. Finally, we showcase its application in analyzing an m¹A site within three synthetic RNA oligos, highlighting its potential for precise and accessible modification analysis.