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Scalable and Maintainable Distributed Sequence Alignment Using Spark
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DOI：10.1109/TCBBIO.2025.3565188.png)
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Abstract
En 中文
The exponential growth of genomic data presents a challenge to bioinformatics research. NCBI BLAST, a popular pairwise sequence alignment tool, does not scale with the hundreds of gigabytes (GB) of sequenced data. Therefore, mpiBLAST was widely adopted and scaled up to 65,536 processors. However, mpiBLAST is tightly coupled with an obsolete NCBI BLAST version, creating a challenge to upgrading mpiBLAST with the ever-changing NCBI BLAST code. Recent parallel BLAST implementations, like SparkBLAST, use parallelism wrappers separate from NCBI BLAST to overcome this issue. However, query partitioning, a parallel method that duplicates the genome database on each compute node, makes SparkBLAST scale poorly with databases larger than a single node’s memory. Thus, no parallel BLAST utility simultaneously addresses performance, scalability, and software maintainability. To fill this gap, we introduce SparkLeBLAST, a parallel BLAST tool that uses the Spark framework and efficient data partitioning to combine mpiBLAST’s performance and scalability with SparkBLAST’s simplicity and maintainability. SparkLeBLAST democratizes scalable genomic analysis for domain scientists without extensive distributed computing experience. SparkLeBLAST runs up to 6.68× faster than SparkBLAST. SparkLeBLAST also accelerates taxonomic assignment of COVID-19 genomic diversity analysis by 20.9× as it speeds up the BLAST search component by 88.6× using 128 compute nodes.
Keywords:
BLAST
Big Data
high-performance computing
Spark
COVID-19
genomic diversity analysis
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