Pipeface: Processing big genomic data in the rapidly evolving long-read sequencing space

One human genome contains roughly 3 billion bases or bytes of data and an estimated 220 million genomes are sequenced globally per year. This amounts to roughly 40 Exabytes of data, 40 times the amount of data produced on YouTube yearly. Managing and extracting meaning from this deluge of genomic information is one of the defining challenges of bioinformatics.The Deveson Lab generates large volumes of genomic data through both the long-read sequencing service and internal research projects, using two leading platforms, Oxford Nanopore Technologies (ONT) and PacBio. Many existing bioinformatics tools and workflows are licensed or optimised for a single platform, creating challenges for integrating and processing multi-platform datasets across both technologies at scale.To address this, we developed Pipeface, an open-source, scalable, reproducible, and portable Nextflow pipeline that transforms basecalled long-read sequencing data into analysis-ready outputs suitable for research and clinical interpretation. Pipeface is routinely deployed on Australia’s National Computational Infrastructure (NCI) and is portable to other high-performance computing (HPC) environments using containerised software (Singularity).The pipeline implements modular stages for alignment, variant calling (SNPs/indels and structural variants), phasing, haplotagging, variant annotation and summarisation of base modification frequencies for singletons, trios, or duos. Pipeface is designed for high throughput, automatically scaling across compute nodes using Nextflow’s process-level parallelisation and resource management. Since deployment, Pipeface has processed over 1,400 internal samples, with additional adoption by collaborators conducting large-scale genomics research.With global sequencing output now tipping into tens of exabytes each year, tools like Pipeface are vital for turning vast volumes of genomic data into meaningful biological and clinical insights.ABOUT THE AUTHORLeah Kemp is a computational Biologist in the Deveson Lab at the Garvan Institute of Medical Research in Sydney, although she is physically based in Taranaki, NZ. Her group develops, adopts and applies new genomics technologies, methods and algorithms to shed new light on the genome and improve the diagnosis of genomic disease. She holds a MSc in Ecology and Biodiversity, with her thesis researching the population genetics of Trevally in Australasia. From there she dove into bioinformatics when she joined the Human Genomics Group at PHF science (formally known as The Institute of Environmental Science and Research (ESR)). Today she focuses on all things data and data infrastructure. -----For more information about the eResearch NZ / eRangahau Aotearoa conference, visit: https://eresearchnz.co.nz/