How to speed up BWA-MEM on large inputs?

Optimize Hardware Resources

• Ensure your system has enough CPU cores and RAM. BWA-MEM scales well with multi-core processors, so take full advantage of available cores by increasing the thread count.

 

• Use solid-state drives (SSDs) over hard disk drives (HDDs) for faster I/O operations during the alignment process.

Adjust BWA-MEM Parameters

• Utilize the -t option to specify the number of threads. More threads can significantly speed up the process. Example: bwa mem -t 8 for an 8-core machine.

 

• Consider using the -K option for setting a larger batch size, reducing the frequency of synchronization between threads.

Efficient Input Data Management

• Preprocess the data to reduce input size. For instance, trim adapters and low-quality bases using tools like Trimmomatic or cutadapt.

 

• Use compressed input files to decrease disk I/O time. BWA-MEM can read compressed FASTQ files directly.

Parallelize the Workflow

• Split large input files into smaller chunks, align them separately using BWA-MEM in parallel, and merge results. This can be done with GNU parallel or custom scripts.

 

• Consider using a workflow manager or job scheduler to handle parallel BWA-MEM jobs efficiently, maximizing resource utilization.

Utilize Cloud or HPC Resources

• If local computational resources are inadequate, leverage cloud computing platforms like AWS or Google Cloud that provide scalable compute instances.

 

• Opt for High-Performance Computing (HPC) cluster environments for distributing workloads and capitalizing on large-scale parallel computing resources.

Keep BWA Updated

• Regularly update to the latest version of BWA to benefit from performance improvements and bug fixes that could enhance speed.