How to tweak BWA parameters for quality?

Introduction to BWA Parameters

• Begin by understanding the BWA (Burrows-Wheeler Aligner) tool, which efficiently aligns DNA sequences against a large reference genome.

 

• Familiarize yourself with the default settings and parameters, as they work well for most initial analyses.

Setting Up Your Environment

• Ensure your computational environment has adequate resources (CPU, memory) as BWA can be resource-intensive, especially with large datasets.

 

• Understand the type of sequencing data you have (e.g., whole genome, exome, targeted) to select appropriate parameters.

Tweaking BWA Parameters for Quality

• Read Alignment: Start by using the `bwa mem` algorithm for short reads, as it is optimized for these types of sequences. For long reads, consider using `bwa bwasw`.

 

• Adjusting Seed Length (`-k`): Modify the seed length parameter to control the sensitivity. A smaller seed length (default is 19) may increase sensitivity but at the cost of performance.

 

• Mismatch Penalty (`-A`, `-B`, `-O`): Adjust mismatch and gap penalties (match score `-A`, mismatch penalty `-B`, gap opening penalty `-O`) according to your experimental errors and quality requirements.

 

• Quality Threshold (`-q`): Increase the quality threshold to include only high-confidence base calls in the alignment. Default is 0, but setting this higher filters out low-quality reads.

 

• Minimize Suboptimal Alignments (`-T`): Use the `-T` parameter to increase the minimum score difference between primary and secondary alignments, reducing ambiguities.

Optimizing Performance

• Threading (`-t`): Utilize the `-t` parameter to enable multi-threading, speeding up the alignment process; adjust thread number based on your CPU capabilities.

 

• Memory Usage (`-M`): Adjust memory settings to prevent overloading your system while maintaining performance.

Testing and Validation

• Perform a trial run with a subset of your data to evaluate how your parameter adjustments affect alignment quality and computational performance.

 

• Validate your results by comparing them to known standards or using metrics such as alignment rate, error rate, and coverage.

Iterating on Parameters

• Based on testing results, iteratively refine your parameters to balance alignment quality and performance.

 

• Document each adjustment you make, noting the effects on alignment quality, so you can make informed decisions in future analyses.