Biology subjects

Liyanage, K.

Publications and source records attributed to Liyanage, K..

3 recordsLinked to original sources

bioRxiv2024

Interactive visualisation of raw nanopore signal data with Squigualiser

Nanopore sequencing measures ionic current during the translocation of DNA, RNA or protein molecules through a nanoscale protein pore. This raw current signal data can be basecalled into sequence information and has the potential to identify other diverse molecular features, such as base modifications, secondary structures, etc. Despite the unique properties and potential utility of nanopore signal data, there are currently limited options available for signal data visualisation. To address this, we have developed Squigualiser, a toolkit for intuitive, interactive visualisation of sequence-aligned signal data, which currently supports both DNA and RNA sequencing data from Oxford Nanopore Technologies (ONT) instruments. A series of methodological innovations enable efficient alignment of raw signal data to a reference genome/transcriptome with single-base resolution. Squigualiser generates an interactive signal browser view (HTML file), in which the user can navigate across a genome/transcriptome region and customise the display. Multiple independent reads are integrated into a signal pileup format and different datasets can be displayed as parallel tracks to facilitate their comparison. Squigualiser provides the most sophisticated framework for nanopore signal data visualisation to date and will catalyse new advances in signal analysis. We provide Squigualiser as an open-source tool for the nanopore community: https://github.com/hiruna72/squigualiser

bioinformatics↗

bioRxiv2023

minimap2-fpga: Integrating hardware-accelerated chaining for efficient end-to-end long-read sequence mapping

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. 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 [~] 30x ONT and [~] 50x 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 [~] 30x ONT and [~] 50x 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.

bioinformatics↗

bioRxiv2023

Squigulator: simulation of nanopore sequencing signal data with tunable noise parameters

In silico simulation of next-generation sequencing data is a technique used widely in the genomics field. However, there is currently a lack of optimal tools for creating simulated data from third-generation 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, 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 testing, debugging, validation and optimisation of nanopore analysis methods. The user may generate noise-free ideal data, realistic data with noise profiles emulating specific ONT protocols, or they may deterministically modify noise parameters and other variables to shape the data to their needs. To highlight its utility, we use Squigulator to model the degree to which different types of noise impact the accuracy of ONT basecalling and downstream variant detection, revealing new insights into the properties of ONT data. We provide Squigulator as an open-source tool for the nanopore community: https://github.com/hasindu2008/squigulator

bioinformatics↗