/software-guides

How to fix slow or failing BLAST runs?

Optimize BLAST runs: Clean and split sequences, adjust parameters, choose small databases, allocate resources, update software, and review logs to boost performance.

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How to fix slow or failing BLAST runs?

 

Optimize the Query Sequence

 

  • Ensure the input sequence is free from ambiguous characters or errors. Use sequence cleaning tools to remove noise.
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  • Reduce the length of query sequences; break large sequences into smaller fragments to speed up computations.

 

Adjust BLAST Parameters

 

  • Set an appropriate Expectation value (E-value) to limit the number of statistically significant matches returned.
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  • Modify the word size parameter for nucleotide searches to increase speed by adjusting the sensitivity.
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  • Utilize the task-specific options such as 'blastn-short' for shorter queries which are optimized for such scenarios.

 

Optimize Database Selection

 

  • Choose the smallest database necessary to achieve your research goals, reducing the number of sequences to process.
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  • Consider downloading and filtering the database locally instead of using a larger public version, which may include many irrelevant entries.

 

Utilize Computational Resources Efficiently

 

  • If running BLAST locally, ensure that hardware resources like CPU and memory are sufficiently allocated. Consider using a more powerful machine or cloud-based resources.
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  • When using NCBI servers, understand the queue limitations and choose off-peak hours for quicker processing.
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  • Use parallel processing capabilities by splitting the task into smaller chunks that can be run simultaneously.

 

Check Software and Version

 

  • Ensure the version of BLAST you are using is up-to-date with the latest patches and improvements for performance and bug fixes.
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  • If issues persist, consider using an alternative tool or implementation, such as blat or diamond for specific tasks, to see if performance improves.

 

Analyze System Logs and Outputs

 

  • Review the BLAST logs to understand bottlenecks or errors that might be occurring during execution.
  •  

  • Look for patterns in failed runs and adjust parameters accordingly, keeping track of any changes that improve performance.

 

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