Biology subjects

Samarakoon, H.

Publications and source records attributed to Samarakoon, H..

11 recordsLinked to original sources

bioRxiv2026

Open-source, Hardware-Independent GPU Acceleration for Scalable Nanopore Basecalling with Slorado and Openfish

Nanopore sequencing technologies are used widely in genomics research and their adoption continues to accelerate. 'Basecalling' is an essential step in the nanopore sequencing workflow, during which raw electrical signals are translated into nucleotide sequences. The current state-of-the-art basecaller, Oxford Nanopore Technologies (ONT) software 'Dorado,' relies on proprietary, platform-specific NVIDIA GPU optimisations bundled in the closed-source 'Koi' library. As a result, practical, high-speed basecalling is effectively restricted to a narrow class of supported hardware, limiting accessibility, portability, and innovation. We present (1) 'Openfish,' an open-source GPU-accelerated nanopore basecaller decoding library that provides a competitive alternative to ONT's proprietary Koi library; and (2) Slorado, a fully open-source basecalling framework that supports both DNA and RNA with equivalent accuracy to Dorado. Together, Openfish and Slorado remove the hardware lock-in that currently limits high-performance nanopore basecalling. Our framework scales efficiently across heterogeneous computing environments, from low-power embedded devices to GPU-equipped datacenters, without sacrificing speed or accuracy. Openfish and Slorado are available as free open-source packages for basecalling research, optimisation and deployment beyond the constraints of proprietary software and hardware ecosystems: Openfish: https://github.com/warp9seq/openfish, Slorado: https://github.com/BonsonW/slorado.

bioinformatics↗

bioRxiv2025

The enduring advantages of the SLOW5 file format for raw nanopore sequencing data

Nanopore sequencing is a widespread and important method in genomics science. The raw electrical current signal data from a typical nanopore sequencing experiment is large and complex. This can be stored in two alternative file formats that are presently supported: POD5 is a signal data file format used by default on instruments from Oxford Nanopore Technologies (ONT); SLOW5 is an open-source file format originally developed as an alternative to ONTs previous file format, which was known as FAST5. The choice of format may have important implications for the cost, speed and simplicity of nanopore signal data analysis, management and storage. To inform this choice, we present a comparative evaluation of POD5 vs SLOW5. We conducted benchmarking experiments assessing file size, analysis performance and usability on a variety of different computer architectures. SLOW5 showed superior performance during sequential and non-sequential (random access) file reading on most systems, manifesting in faster, cheaper basecalling and other analysis, and we could find no instance in which POD5 file reading was significantly faster than SLOW5. We demonstrate that SLOW5 file writing is highly parallelisable, thereby meeting the demands of data acquisition on ONT instruments. Our analysis also identified differences in the complexity and stability of the software libraries for SLOW5 (slow5lib) and POD5 (pod5), including a large discrepancy in the number of underlying software dependencies, which may complicate the pod5 compilation process. In summary, many of the advantages originally conceived for SLOW5 remain relevant today, despite the replacement of FAST5 with POD5 as ONTs core file format.

bioinformatics↗

bioRxiv2024

Leveraging Basecaller's Move Table to Generate a Lightweight k-mer Model

Nanopore sequencing by Oxford Nanopore Technologies (ONT) enables direct analysis of DNA and RNA by capturing raw electrical signals. Different nanopore chemistries have varied k-mer lengths, current levels, and standard deviations, which are stored in k-mer models. Particularly in cases where official models are lacking or unsuitable for specific sequencing conditions, tailored k-mer models are crucial to ensure precise signal-to-sequence alignment and interpretation. The process of transforming raw signals into nucleotide sequences, known as basecalling, is a fundamental step in nanopore sequencing. In this study, we leverage the basecallers move table to create a lightweight denovo k-mer model for RNA004 chemistry. We showcase the effectiveness of our custom k-mer model through high alignment rates (97.48%) compared to larger default models. Additionally, our 5-mer model exhibits similar performance as the default 9-mer models in m6A methylation detection.

bioinformatics↗

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↗

bioRxiv2023

Accelerated nanopore basecalling with SLOW5 data format

Nanopore sequencing is emerging as a key pillar in the genomic technology landscape but computational constraints limiting its scalability remain to be overcome. The translation of raw current signal data into DNA or RNA sequence reads, known as basecalling, is a major friction in any nanopore sequencing workflow. Here, we exploit the advantages of the recently developed signal data format SLOW5 to streamline and accelerate nanopore basecalling on high-performance computer (HPC) and cloud environments. SLOW5 permits highly efficient sequential data access, eliminating a significant analysis bottleneck. To take advantage of this, we introduce Buttery-eel, an open-source wrapper for Oxford Nanopores Guppy basecaller that enables SLOW5 data access, resulting in performance improvements that are essential for scalable, affordable basecalling.

bioinformatics↗

bioRxiv2022

Flexible and efficient handling of nanopore sequencing signal data with slow5tools

BackgroundNanopore sequencing is an emerging technology that is being rapidly adopted in research and clinical genomics. We recently developed SLOW5, a new file format for storage and analysis of raw data from nanopore sequencing experiments. SLOW5 is a community-centric, open source format that offers considerable performance benefits over the existing nanopore data format, known as FAST5. Here we introduce slow5tools, a simple, intuitive toolkit for handling nanopore raw signal data in SLOW5 format. ResultsSlow5tools enables lossless FAST5-to-SLOW5 and SLOW5-to-FAST5 data conversion, and a range of tools for structuring, indexing, viewing and querying SLOW5 files. Slow5tools uses multi-threading, multi-processing and other engineering strategies to achieve fast data conversion and manipulation, including live FAST5-to-SLOW5 conversion during sequencing. We outline a series of examples and benchmarking experiments to illustrate slow5tools usage, and describe the engineering principles underpinning its high performance. ConclusionSlow5tools is an essential toolkit for handling nanopore signal data, which was developed to support adoption of SLOW5 by the nanopore community. Slow5tools is written in C/C++ with minimal dependencies and is freely available as an open-source program under an MIT licence: https://github.com/hasindu2008/slow5tools.

bioinformatics↗

bioRxiv2021

SLOW5: a new file format enables massive acceleration of nanopore sequencing data analysis

Nanopore sequencing is an emerging genomic technology with great potential. However, the storage and analysis of nanopore sequencing data have become major bottlenecks preventing more widespread adoption in research and clinical genomics. Here, we elucidate an inherent limitation in the file format used to store raw nanopore data - known as FAST5 - that prevents efficient analysis on high-performance computing (HPC) systems. To overcome this, we have developed SLOW5, an alternative file format that permits efficient parallelisation and, thereby, acceleration of nanopore data analysis. For example, we show that using SLOW5 format, instead of FAST5, reduces the time and cost of genome-wide DNA methylation profiling by an order of magnitude on common HPC systems, and delivers consistent improvements on a wide range of different architectures. With a simple, accessible file structure and a ~25% reduction in size compared to FAST5, SLOW5 format will deliver substantial benefits to all areas of the nanopore community.

genomics↗

bioRxiv2020

F5N : Nanopore Sequence Analysis Toolkit for Android Smartphones

F5N is the first ever Android application for nanopore sequence analysis on a mobile phone, comprised of popular tools for read alignment (Minimap2), sequence data manipulation (Samtools) and methylation calling (F5C/Nanopolish). On NA12878 nanopore data, F5N can perform a complete methylation calling pipeline on a mobile phone in [~]15 minutes for a batch of 4000 nanopore reads ([~]34 megabases). F5N is not only a toolkit but also a framework for integrating existing C/C++ based command line tools to run on Android. F5N will enable performing nanopore sequence analysis on-site when used with an ultra-portable nanopore sequencer (eg: MinION or the anticipated smidgION), consequently reducing the cost for special computers and high-speed Internet. Availability and implementationF5N Android application is available on Google Play store at https://play.google.com/store/apps/details?id=com.mobilegenomics.genopo&hl=en and the source code is available on Github at https://github.com/SanojPunchihewa/f5n. Contacthirunas@eng.pdn.ac.lk

bioinformatics↗

bioRxiv2019

GPU Accelerated Adaptive Banded Event Alignment for Rapid Comparative Nanopore Signal Analysis

Nanopore sequencing has the potential to revolutionise genomics by realising portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these applications requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. For instance, comparing raw nanopore signals to a biological reference sequence is a computationally complex task despite leveraging a dynamic programming algorithm for Adaptive Banded Event Alignment (ABEA)--a commonly used approach to polish sequencing data and identify non-standard nucleotides, such as measuring DNA methylation. Here, we parallelise and optimise an implementation of the ABEA algorithm (termed f5c) to efficiently run on heterogeneous CPU-GPU architectures. By optimising memory, compute and load balancing between CPU and GPU, we demonstrate how f5c can perform ~3-5x faster than the original implementation of ABEA in the Nanopolish software package. We also show that f5c enables DNA methylation detection on-the-fly using an embedded System on Chip (SoC) equipped with GPUs. Our work not only demonstrates that complex genomics analyses can be performed on lightweight computing systems, but also benefits High-Performance Computing (HPC). The associated source code for f5c along with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c.

bioinformatics↗