GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads
Abstract Background In Overlap-Layout-Consensus (OLC) based de novo assembly, all reads must be compared with every other read to find overlaps. This makes the process rather slow and limits the practicality of using de novo assembly methods at a large scale in the field. Darwin is a fast and accura...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-020-03685-1 |
| _version_ | 1818323383719297024 |
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| author | Nauman Ahmed Tong Dong Qiu Koen Bertels Zaid Al-Ars |
| author_facet | Nauman Ahmed Tong Dong Qiu Koen Bertels Zaid Al-Ars |
| author_sort | Nauman Ahmed |
| collection | DOAJ |
| description | Abstract Background In Overlap-Layout-Consensus (OLC) based de novo assembly, all reads must be compared with every other read to find overlaps. This makes the process rather slow and limits the practicality of using de novo assembly methods at a large scale in the field. Darwin is a fast and accurate read overlapper that can be used for de novo assembly of state-of-the-art third generation long DNA reads. Darwin is designed to be hardware-friendly and can be accelerated on specialized computer system hardware to achieve higher performance. Results This work accelerates Darwin on GPUs. Using real Pacbio data, our GPU implementation on Tesla K40 has shown a speedup of 109x vs 8 CPU threads of an Intel Xeon machine and 24x vs 64 threads of IBM Power8 machine. The GPU implementation supports both linear and affine gap, scoring model. The results show that the GPU implementation can achieve the same high speedup for different scoring schemes. Conclusions The GPU implementation proposed in this work shows significant improvement in performance compared to the CPU version, thereby making it accessible for utilization as a practical read overlapper in a DNA assembly pipeline. Furthermore, our GPU acceleration can also be used for performing fast Smith-Waterman alignment between long DNA reads. GPU hardware has become commonly available in the field today, making the proposed acceleration accessible to a larger public. The implementation is available at https://github.com/Tongdongq/darwin-gpu . |
| first_indexed | 2024-12-13T11:11:49Z |
| format | Article |
| id | doaj.art-3a6acb84b405492291b56dd2b3868836 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-13T11:11:49Z |
| publishDate | 2020-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-3a6acb84b405492291b56dd2b38688362022-12-21T23:48:42ZengBMCBMC Bioinformatics1471-21052020-09-0121S1311710.1186/s12859-020-03685-1GPU acceleration of Darwin read overlapper for de novo assembly of long DNA readsNauman Ahmed0Tong Dong Qiu1Koen Bertels2Zaid Al-Ars3Delft University of TechnologyDelft University of TechnologyDelft University of TechnologyDelft University of TechnologyAbstract Background In Overlap-Layout-Consensus (OLC) based de novo assembly, all reads must be compared with every other read to find overlaps. This makes the process rather slow and limits the practicality of using de novo assembly methods at a large scale in the field. Darwin is a fast and accurate read overlapper that can be used for de novo assembly of state-of-the-art third generation long DNA reads. Darwin is designed to be hardware-friendly and can be accelerated on specialized computer system hardware to achieve higher performance. Results This work accelerates Darwin on GPUs. Using real Pacbio data, our GPU implementation on Tesla K40 has shown a speedup of 109x vs 8 CPU threads of an Intel Xeon machine and 24x vs 64 threads of IBM Power8 machine. The GPU implementation supports both linear and affine gap, scoring model. The results show that the GPU implementation can achieve the same high speedup for different scoring schemes. Conclusions The GPU implementation proposed in this work shows significant improvement in performance compared to the CPU version, thereby making it accessible for utilization as a practical read overlapper in a DNA assembly pipeline. Furthermore, our GPU acceleration can also be used for performing fast Smith-Waterman alignment between long DNA reads. GPU hardware has become commonly available in the field today, making the proposed acceleration accessible to a larger public. The implementation is available at https://github.com/Tongdongq/darwin-gpu .http://link.springer.com/article/10.1186/s12859-020-03685-1GenomicsRead overlapperDe novo assemblyLong DNA readsGPU acceleration |
| spellingShingle | Nauman Ahmed Tong Dong Qiu Koen Bertels Zaid Al-Ars GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads BMC Bioinformatics Genomics Read overlapper De novo assembly Long DNA reads GPU acceleration |
| title | GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads |
| title_full | GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads |
| title_fullStr | GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads |
| title_full_unstemmed | GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads |
| title_short | GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads |
| title_sort | gpu acceleration of darwin read overlapper for de novo assembly of long dna reads |
| topic | Genomics Read overlapper De novo assembly Long DNA reads GPU acceleration |
| url | http://link.springer.com/article/10.1186/s12859-020-03685-1 |
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