A graphical, interactive and GPU-enabled workflow to process long-read sequencing data
Abstract Background Long-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 effic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-08-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-021-07927-1 |
| _version_ | 1818910823778615296 |
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| author | Shishir Reddy Ling-Hong Hung Olga Sala-Torra Jerald P. Radich Cecilia CS Yeung Ka Yee Yeung |
| author_facet | Shishir Reddy Ling-Hong Hung Olga Sala-Torra Jerald P. Radich Cecilia CS Yeung Ka Yee Yeung |
| author_sort | Shishir Reddy |
| collection | DOAJ |
| description | Abstract Background Long-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. Results We 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. Conclusions Our 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. |
| first_indexed | 2024-12-19T22:48:56Z |
| format | Article |
| id | doaj.art-ff2c02551adb4007b0a074feaace793b |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-19T22:48:56Z |
| publishDate | 2021-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-ff2c02551adb4007b0a074feaace793b2022-12-21T20:02:54ZengBMCBMC Genomics1471-21642021-08-012211810.1186/s12864-021-07927-1A graphical, interactive and GPU-enabled workflow to process long-read sequencing dataShishir Reddy0Ling-Hong Hung1Olga Sala-Torra2Jerald P. Radich3Cecilia CS Yeung4Ka Yee Yeung5University of CaliforniaSchool of Engineering and Technology, University of WashingtonClinical Research Division, Fred Hutchinson Cancer Research CenterClinical Research Division, Fred Hutchinson Cancer Research CenterClinical Research Division, Fred Hutchinson Cancer Research CenterSchool of Engineering and Technology, University of WashingtonAbstract Background Long-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. Results We 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. Conclusions Our 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.https://doi.org/10.1186/s12864-021-07927-1Cancer diagnosticsWorkflowsCloud computingNanoporeGPUFAIR |
| spellingShingle | Shishir Reddy Ling-Hong Hung Olga Sala-Torra Jerald P. Radich Cecilia CS Yeung Ka Yee Yeung A graphical, interactive and GPU-enabled workflow to process long-read sequencing data BMC Genomics Cancer diagnostics Workflows Cloud computing Nanopore GPU FAIR |
| title | A graphical, interactive and GPU-enabled workflow to process long-read sequencing data |
| title_full | A graphical, interactive and GPU-enabled workflow to process long-read sequencing data |
| title_fullStr | A graphical, interactive and GPU-enabled workflow to process long-read sequencing data |
| title_full_unstemmed | A graphical, interactive and GPU-enabled workflow to process long-read sequencing data |
| title_short | A graphical, interactive and GPU-enabled workflow to process long-read sequencing data |
| title_sort | graphical interactive and gpu enabled workflow to process long read sequencing data |
| topic | Cancer diagnostics Workflows Cloud computing Nanopore GPU FAIR |
| url | https://doi.org/10.1186/s12864-021-07927-1 |
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