Hiruna Samarakoon

This paper introduces open-source GPU-accelerated basecalling tools for nanopore sequencing that are hardware-independent and competitive with proprietary solutions.

This paper compares POD5 and SLOW5 file formats for nanopore sequencing data, finding SLOW5 offers better performance, lower cost, and easier management.

This paper develops a lightweight k-mer model for RNA004 chemistry using basecaller move tables, achieving high alignment rates and comparable m6A detection performance to default models.

Squigulator simulates nanopore sequencing signals with tunable noise parameters to test and improve analysis methods.

Squigualiser provides an interactive visualization tool for nanopore signal data, enabling alignment to reference genomes and comparison of multiple datasets.

The paper introduces minimap2-fpga, a hardware-accelerated version of minimap2 that improves mapping speed for long-read sequencing data with minimal accuracy loss.

This paper introduces Buttery-eel, an open-source tool that accelerates nanopore basecalling by leveraging the SLOW5 data format for efficient data access on HPC and cloud environments.

slow5tools converts and manipulates nanopore sequencing data in the SLOW5 format, offering efficient and flexible data handling with performance improvements over FAST5.

The paper introduces SLOW5, a new file format for nanopore sequencing data that accelerates analysis on HPC systems by enabling efficient parallelization.

This paper introduces f5c, a GPU-optimized implementation of Adaptive Banded Event Alignment for faster nanopore signal analysis and DNA methylation detection.

F5N enables nanopore sequence analysis on Android smartphones using existing tools, completing methylation calling in 15 minutes for 4000 reads.