Simulation of nanopore sequencing signal data with tunable parameters.

In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, a transcriptome, or read sequences, and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation, and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols or noise-free "ideal" data, or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.

Interactive visualization of nanopore sequencing signal data with Squigualiser.

MOTIVATION: Nanopore sequencing current signal data can be 'basecalled' into sequence information or analysed directly, with the capacity to identify diverse molecular features, such as DNA/RNA base modifications and secondary structures. However, raw signal data is large and complex, and there is a need for improved visualization strategies to facilitate signal analysis, exploration and tool development. RESULTS: Squigualiser (Squiggle visualiser) is a toolkit for intuitive, interactive visualization of sequence-aligned signal data, which currently supports both DNA and RNA sequencing data from Oxford Nanopore Technologies instruments. Squigualiser is compatible with a wide range of alternative signal-alignment software packages and enables visualization of both signal-to-read and signal-to-reference aligned data at single-base resolution. Squigualiser generates an interactive signal browser view (HTML file), in which the user can navigate across a genome/transcriptome region and customize the display. Multiple independent reads are integrated into a 'signal pileup' format and different datasets can be displayed as parallel tracks. Although other methods exist, Squigualiser provides the community with a software package purpose-built for raw signal data visualization, incorporating a range of new and existing features into a unified platform. AVAILABILITY AND IMPLEMENTATION: Squigualiser is an open-source package under an MIT licence: https://github.com/hiruna72/squigualiser. The software was developed using Python 3.8 and can be installed with pip or bioconda or executed directly using prebuilt binaries provided with each release.

Efficient end-to-end long-read sequence mapping using minimap2-fpga integrated with hardware accelerated chaining.

minimap2 is the gold-standard software for reference-based sequence mapping in third-generation long-read sequencing. While minimap2 is relatively fast, further speedup is desirable, especially when processing a multitude of large datasets. In this work, we present minimap2-fpga, a hardware-accelerated version of minimap2 that speeds up the mapping process by integrating an FPGA kernel optimised for chaining. Integrating the FPGA kernel into minimap2 posed significant challenges that we solved by accurately predicting the processing time on hardware while considering data transfer overheads, mitigating hardware scheduling overheads in a multi-threaded environment, and optimizing memory management for processing large realistic datasets. We demonstrate speed-ups in end-to-end run-time for data from both Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio). minimap2-fpga is up to 79% and 53% faster than minimap2 for [Formula: see text] ONT and [Formula: see text] PacBio datasets respectively, when mapping without base-level alignment. When mapping with base-level alignment, minimap2-fpga is up to 62% and 10% faster than minimap2 for [Formula: see text] ONT and [Formula: see text] PacBio datasets respectively. The accuracy is near-identical to that of original minimap2 for both ONT and PacBio data, when mapping both with and without base-level alignment. minimap2-fpga is supported on Intel FPGA-based systems (evaluations performed on an on-premise system) and Xilinx FPGA-based systems (evaluations performed on a cloud system). We also provide a well-documented library for the FPGA-accelerated chaining kernel to be used by future researchers developing sequence alignment software with limited hardware background.