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Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
Published on: August 20, 2021
Parallelized short read assembly of large genomes using de Bruijn graphs.
Yongchao Liu1, Bertil Schmidt, Douglas L Maskell
1School of Computer Engineering, Nanyang Technological University, Singapore. liuy0039@ntu.edu.sg
BMC Bioinformatics
|August 27, 2011
Summary
PASHA is a new parallelized short read assembler that efficiently assembles large genomes using hybrid computing. It delivers high-quality assemblies faster than existing tools, even for complex genomes like the human genome.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Next-generation sequencing has increased DNA data, driving the need for efficient de novo short read assemblers.
- Existing assemblers often require excessive time and computational resources for large genomes.
Purpose of the Study:
- To develop a parallelized short read assembler that addresses the limitations of current tools.
- To improve the efficiency and scalability of genome assembly.
Main Methods:
- Development of PASHA, a parallelized assembler utilizing de Bruijn graphs.
- Implementation of hybrid parallelism combining shared-memory multi-core CPUs and distributed-memory compute clusters.
- Evaluation on small-scale and large-scale datasets, including human genome assembly.
Main Results:
- PASHA produces more contiguous, high-quality assemblies in less time compared to Velvet, ABySS, and SOAPdenovo.
- Demonstrated scalability with human genome assembly, achieving competitive quality and faster execution than ABySS.
- Human genome assembly completed in approximately 21 hours using modest computational resources.
Conclusions:
- PASHA's hybrid parallelism enables high-quality, efficient assembly of large genomes.
- The assembler offers a viable solution for handling the growing volume of high-throughput sequencing data.
- PASHA provides a significant advancement in de novo genome assembly for large datasets.
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