ArticleBMC genomics2021
A graphical, interactive and GPU-enabled workflow to process long-read sequencing data.
Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- NANOME: A Nextflow pipeline for haplotype-aware allele-specific consensus DNA methylation detection by nanopore long-read sequencing.bioRxiv : the preprint server for biology · 2025Article
- Whole genome comparisons reveal gut-to-lung translocation of Escherichia coli and Burkholderia cenocepacia in two cases of ventilator-associated pneumonia in ICU patients.Respiratory research · 2025Article
- Biodepot Launcher: an app to install, manage and launch bioinformatics workflows.GigaByte (Hong Kong, China) · 2025Article
- From DNA to Big Data: NGS Technologies and Their Applications.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Complete genome sequence ofMicrobiology resource announcements · 2024Article
- Oxford Nanopore Technology and its Application in Liquid Biopsies.Current genomics · 2023Review
- NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing.Briefings in bioinformatics · 2023Article
- Rapid detection of myeloid neoplasm fusions using single-molecule long-read sequencing.PLOS global public health · 2023Article
- Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundLong-read sequencing has great promise in enabling portable, rapid molecular-assisted cancer diagnoses. A key challenge in democratizing long-read sequencing technology in the biomedical and clinical community is the lack of graphical bioinformatics software tools which can efficiently process the raw nanopore reads, support graphical output and interactive visualizations for interpretations of results. Another obstacle is that high performance software tools for long-read sequencing data analyses often leverage graphics processing units (GPU), which is challenging and time-consuming to configure, especially on the cloud.
resultsWe present a graphical cloud-enabled workflow for fast, interactive analysis of nanopore sequencing data using GPUs. Users customize parameters, monitor execution and visualize results through an accessible graphical interface. The workflow and its components are completely containerized to ensure reproducibility and facilitate installation of the GPU-enabled software. We also provide an Amazon Machine Image (AMI) with all software and drivers pre-installed for GPU computing on the cloud. Most importantly, we demonstrate the potential of applying our software tools to reduce the turnaround time of cancer diagnostics by generating blood cancer (NB4, K562, ME1, 238 MV4;11) cell line Nanopore data using the Flongle adapter. We observe a 29x speedup and a 93x reduction in costs for the rate-limiting basecalling step in the analysis of blood cancer cell line data.
conclusionsOur interactive and efficient software tools will make analyses of Nanopore data using GPU and cloud computing accessible to biomedical and clinical scientists, thus facilitating the adoption of cost effective, fast, portable and real-time long-read sequencing.
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