Biology subjects

Gamaarachchi, H.

Publications and source records attributed to Gamaarachchi, H..

24 records · Page 2Linked to original sources

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↗

bioRxiv2021

InterARTIC: an interactive web application for whole-genome nanopore sequencing analysis of SARS-CoV-2 and other viruses

MotivationInterARTIC is an interactive web application for the analysis of viral whole-genome sequencing (WGS) data generated on Oxford Nanopore Technologies (ONT) devices. A graphical interface enables users with no bioinformatics expertise to analyse WGS experiments and reconstruct consensus genome sequences from individual isolates of viruses, such as SARS-CoV-2. InterARTIC is intended to facilitate widespread adoption and standardisation of ONT sequencing for viral surveillance and molecular epidemiology. Worked exampleWe demonstrate the use of InterARTIC for the analysis of ONT viral WGS data from SARS-CoV-2 and Ebola virus, using a laptop computer or the internal computer on an ONT GridION sequencing device. We showcase the intuitive graphical interface, workflow customisation capabilities and job-scheduling system that facilitate execution of small- and large-scale WGS projects on any common virus. ImplementationInterARTIC is a free, open-source web application implemented in Python. The application can be downloaded as a set of pre-compiled binaries that are compatible with all common Ubuntu distributions, or built from source. For further details please visit: https://github.com/Psy-Fer/interARTIC/.

bioinformatics↗

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↗

bioRxiv2018

Featherweight long read alignment using partitioned reference indexes

The advent of nanopore sequencing has realised portable genomic research and applications. However, state of the art long read aligners and large reference genomes are not compatible with most mobile computing devices due to their high memory requirements. We show how memory requirements can be reduced through parameter optimization and reference genome partitioning, but highlight the associated limitations and caveats of these approaches. We then demonstrate how these issues can be overcome through an appropriate merging technique. We extend the Minimap2 aligner and demonstrate that long read alignment to the human genome can be performed on a system with 2GB RAM with negligible impact on accuracy.

bioinformatics↗