LD Pruning Run Time

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Introduction

LD pruning is a crucial step in the PopGLen pipeline, used to remove redundant genetic variants from a dataset. However, users have reported experiencing long run times for this step, which can be frustrating and time-consuming. In this article, we will delve into the potential causes of LD pruning runtime issues and provide suggestions for optimizing the process.

Understanding the Issue

The user in question has reported that LD pruning is taking an unexpectedly long time to complete, with an estimated run time of over a month. This is significantly longer than the suggested run time of 1 day provided in the default profile. The user has already increased memory to 32G and threads to 8, but this has not had a significant impact on the run time.

Analyzing the Log Output

The provided log output from one chunk of the LD pruning process reveals a pattern of slow progress. The log shows that the process is pruning nodes at a rate of approximately 0.43 nodes per second, with a significant amount of time spent on each pruning step. This suggests that the issue may be related to the size of the input data or the efficiency of the pruning algorithm.

Potential Causes of the Issue

Based on the user's description and the log output, there are several potential causes of the LD pruning runtime issue:

1. Not Enough Resources

Increasing memory to 32G and threads to 8 may not be sufficient to handle the size of the input data. The user may need to consider increasing resources further or using a more efficient algorithm.

2. Reference Chunk Size Too Big

The user has chosen a reference chunk size of 200000000, which is larger than the largest contig. This may be causing the pruning process to take longer than expected. Reducing the chunk size may help to improve performance.

3. Window Size Too Large

The user has chosen a window size of 25k bp, which may be too large for the input data. Reducing the window size may help to improve performance.

Optimizing the LD Pruning Process

To optimize the LD pruning process, the user can consider the following steps:

1. Increase Resources

Consider increasing memory and threads further to see if this improves performance.

2. Reduce Reference Chunk Size

Reduce the reference chunk size to a smaller value, such as 100000000 or 50000000, to see if this improves performance.

3. Reduce Window Size

Reduce the window size to a smaller value, such as 10k bp or 5k bp, to see if this improves performance.

4. Use a More Efficient Algorithm

Consider using a more efficient algorithm for LD pruning, such as the ngsLD_prune_sites algorithm with the --fast option.

Conclusion

LD pruning runtime issues can be frustrating and time-consuming. By analyzing the log output and considering potential causes of the issue, users can take steps to optimize the process and improve performance. In this article, we have discussed potential causes of the issue and provided suggestions for optimizing the LD pruning process.

Recommendations

  • Increase resources to see if this improves performance.
  • Reduce reference chunk size to a smaller value.
  • Reduce window size to a smaller value.
  • Use a more efficient algorithm for LD pruning.

Future Work

Introduction

In our previous article, we discussed the potential causes of LD pruning runtime issues and provided suggestions for optimizing the process. In this article, we will answer some frequently asked questions (FAQs) related to LD pruning runtime issues.

Q: What is LD pruning, and why is it taking so long?

A: LD pruning is a step in the PopGLen pipeline used to remove redundant genetic variants from a dataset. It can take a long time due to the size of the input data and the complexity of the algorithm.

Q: I've increased memory and threads, but it's still taking a long time. What can I do?

A: Consider reducing the reference chunk size or window size to see if this improves performance. You can also try using a more efficient algorithm for LD pruning.

Q: How do I know if my reference chunk size is too big?

A: If your reference chunk size is larger than the largest contig, it may be causing the pruning process to take longer than expected. Try reducing the chunk size to a smaller value.

Q: What is the optimal window size for LD pruning?

A: The optimal window size for LD pruning depends on the size of the input data and the desired level of pruning. A smaller window size may be more efficient for smaller datasets, while a larger window size may be more efficient for larger datasets.

Q: Can I use a more efficient algorithm for LD pruning?

A: Yes, you can use a more efficient algorithm for LD pruning, such as the ngsLD_prune_sites algorithm with the --fast option.

Q: How do I know if my LD pruning process is complete?

A: You can check the log output to see if the pruning process has completed. If the process is still running after a long time, it may be necessary to increase resources or try a different algorithm.

Q: Can I run LD pruning in parallel?

A: Yes, you can run LD pruning in parallel by using multiple threads or processes. This can help to improve performance and reduce the run time.

Q: How do I optimize my LD pruning process for large datasets?

A: To optimize your LD pruning process for large datasets, consider the following:

  • Increase resources (memory and threads) to see if this improves performance.
  • Reduce the reference chunk size or window size to see if this improves performance.
  • Use a more efficient algorithm for LD pruning.
  • Run LD pruning in parallel using multiple threads or processes.

Conclusion

LD pruning runtime issues can be frustrating and time-consuming. By understanding the potential causes of the issue and following the suggestions provided in this article, you can optimize your LD pruning process and improve performance.

Recommendations

  • Increase resources to see if this improves performance.
  • Reduce reference chunk size or window size to see if this improves performance.
  • Use a more efficient algorithm for LD pruning.
  • Run LD pruning in parallel using multiple threads or processes.

Future Work

Further research is needed to understand the causes of LD pruning runtime issues and to develop more efficient algorithms for this process.