Stuck In Clustering MASH Database Step

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Introduction

The dRep tool is a powerful software for dereplicating and clustering genomic data. It has been widely used in various research studies to analyze large-scale genomic datasets. However, users have reported issues with the tool getting stuck at the "Clustering MASH database" step, especially when dealing with large datasets. In this article, we will delve into the possible reasons behind this issue and provide solutions to overcome it.

Background

The dRep tool uses the MASH (MinHash for Alignment of Short Sequences) algorithm to cluster genomic data. The MASH algorithm is a fast and efficient method for comparing short sequences, such as those found in genomic data. The tool has two main steps: primary clustering and secondary clustering. Primary clustering involves grouping genomes into clusters based on their MASH distances, while secondary clustering involves further refining these clusters based on additional criteria.

Case Study 1: 30,000 Genomes

In the first case study, we used the dRep tool to dereplicate 30,000 genomes. The command used was:

$ dRep dereplicate 30kMAGs_dRep99.99_1st --genomeInfo 1th_30k_cm2.csv -g 1th_30k.path  -p 30 -sa 0.9999 -comp 50 -con 10 --skip_plots &>drep1st.log

The key log output showed that the tool completed the primary clustering step in 7 minutes, with 7292 primary clusters made. The secondary clustering step took approximately 1511.4 minutes to complete.

Case Study 2: 58,375 Genomes

In the second case study, we used the dRep tool to dereplicate 58,375 genomes. The command used was:

$ ulimit -s 10000000 # to avoid the error of mash arguments too long 
$ dRep dereplicate 58375MAGs_dRep99.99_all --genomeInfo 58375MAGs_sort_cm2.csv -g 58375MAGs_sort.path   -p 30 -sa 0.9999 -comp 50 -con 10 --primary_chunksize 10000 --skip_plots &>drep_all.log

The key log output showed that the tool got stuck at the "Clustering MASH database" step, with this step lasting longer than 48 hours. The ll command showed that the MASH_files directory was populated with files, but the Clustering_files directory was empty.

Possible Reasons Behind the Issue

Based on the case studies, we can identify several possible reasons behind the issue:

  1. Insufficient Memory: The dRep tool requires a significant amount of memory to perform the MASH clustering step. If the system runs out of memory, the tool may get stuck.
  2. Large Dataset Size: The 58,375 genomes dataset is significantly larger than the 30,000 genomes dataset. This may cause the tool to take longer to complete the MASH clustering step.
  3. MASH Algorithm Parameters: The MASH algorithm parameters, such as the comp and con values, may need to be adjusted for larger datasets.
  4. System Configuration: The system configuration, such as the number of CPU cores and the memory allocation, may need to be adjusted for larger datasets.

Solutions to Overcome the Issue

To overcome the issue, we can try the following solutions:

  1. Increase Memory Allocation: Increase the memory allocation for the dRep tool by setting the ulimit value to a higher value.
  2. Use a Larger System: Use a larger system with more CPU cores and memory to perform the MASH clustering step.
  3. Adjust MASH Algorithm Parameters: Adjust the MASH algorithm parameters, such as the comp and con values, to optimize the clustering step for larger datasets.
  4. Use a Different Clustering Algorithm: Consider using a different clustering algorithm, such as the k-mer algorithm, which may be more efficient for larger datasets.

Conclusion

In conclusion, the dRep tool can get stuck at the "Clustering MASH database" step, especially when dealing with large datasets. By understanding the possible reasons behind this issue and trying the suggested solutions, users can overcome this issue and successfully complete the MASH clustering step.

Future Work

Future work can focus on optimizing the MASH algorithm parameters for larger datasets and exploring alternative clustering algorithms that may be more efficient for large-scale genomic data analysis.

References

Appendix

The following is the complete log output for the 30,000 genomes case study:

03-08 11:09 INFO     Running primary clustering
03-08 11:09 INFO     Running pair-wise MASH clustering
03-08 11:09 INFO       Will split genomes into 6 groups for primary clustering
03-08 12:16 DEBUG    Clustering MASH database
03-08 12:23 DEBUG    Saving primary_linkage pickle to 30kMAGs_dRep99.99_1st/data/Clustering_files/
03-08 12:23 INFO     7292 primary clusters made
03-08 12:23 INFO     Running secondary clustering
03-08 12:23 INFO     Running 831313 fastANI comparisons- should take ~ 1511.4 min
03-08 12:23 DEBUG    running cluster 4950
...

The following is the complete log output for the 58,375 genomes case study:

03-09 21:43 DEBUG    Filtering genomes
03-09 21:43 INFO     98.87% of genomes passed checkM filtering
03-09 21:43 DEBUG    Storing resulting files
03-09 21:43 INFO    
    ..:: dRep dereplicate Step 2. Cluster ::..

03-09 21:43 INFO     Running primary clustering
03-09 21:43 INFO     Running pair-wise MASH clustering
03-09 21:43 INFO       Will split genomes into 6 groups for primary clustering
03-10 01:09 DEBUG    Clustering MASH database
This step state lasts longer than 48 h

The following is the ll command output for the 58,375 genomes case study:

(drep) [yut@node-fat 58375MAGs_dRep99.99]$ ll 58375MAGs_dRep99.99_all/data/MASH_files/MASH_files/
总用量 576G
-rw-r--r--  1 yut 510 452M 3月   9 09:49 ALL.msh
-rw-r--r--  1 yut 510 575G 3月   9 23:17 chunk_all_MASH_table.tsv
drwxr-xr-x 14 yut 510  240 3月   9 09:43 sketches
(drep) [yut@node-fat 58375MAGs_dRep99.99]$ ll 58375MAGs_dRep99.99_all/data/Clustering_files/
总用量 0
```<br/>
**Q&A: Stuck in Clustering MASH Database Step**
=============================================

**Q: What is the MASH algorithm and how does it work?**
------------------------------------------------

A: The MASH (MinHash for Alignment of Short Sequences) algorithm is a fast and efficient method for comparing short sequences, such as those found in genomic data. It works by creating a hash table of k-mers (short sequences of nucleotides) from the input data and then comparing the hash tables to determine the similarity between the sequences.

**Q: What are the possible reasons behind the issue of getting stuck at the "Clustering MASH database" step?**
-----------------------------------------------------------------------------------------

A: The possible reasons behind the issue of getting stuck at the "Clustering MASH database" step include:

1.  **Insufficient Memory**: The `dRep` tool requires a significant amount of memory to perform the MASH clustering step. If the system runs out of memory, the tool may get stuck.
2.  **Large Dataset Size**: The 58,375 genomes dataset is significantly larger than the 30,000 genomes dataset. This may cause the tool to take longer to complete the MASH clustering step.
3.  **MASH Algorithm Parameters**: The MASH algorithm parameters, such as the `comp` and `con` values, may need to be adjusted for larger datasets.
4.  **System Configuration**: The system configuration, such as the number of CPU cores and the memory allocation, may need to be adjusted for larger datasets.

**Q: How can I increase the memory allocation for the `dRep` tool?**
----------------------------------------------------------------

A: You can increase the memory allocation for the `dRep` tool by setting the `ulimit` value to a higher value. For example:

```bash
$ ulimit -s 10000000

Q: What are the MASH algorithm parameters and how can I adjust them?

A: The MASH algorithm parameters include:

  • comp: The number of k-mers to use for comparison.
  • con: The number of clusters to create.

You can adjust these parameters by modifying the dRep command. For example:

$ dRep dereplicate 58375MAGs_dRep99.99_all --genomeInfo 58375MAGs_sort_cm2.csv -g 58375MAGs_sort.path   -p 30 -sa 0.9999 -comp 100 -con 20 --primary_chunksize 10000 --skip_plots &>drep_all.log

Q: How can I adjust the system configuration for larger datasets?

A: You can adjust the system configuration by:

  • Increasing the number of CPU cores available for the dRep tool.
  • Increasing the memory allocation for the dRep tool.
  • Using a larger system with more memory and CPU cores.

Q: What are the alternative clustering algorithms that I can use?

A: Some alternative clustering algorithms that you can use include:

  • k-mer: A clustering algorithm that uses k-mers to compare sequences.
  • BLAST: A clustering algorithm that uses the BLAST (Basic Local Alignment Search Tool) algorithm to compare sequences.

Q: How can I optimize the MASH algorithm parameters for larger datasets?

A: You can optimize the MASH algorithm parameters by:

  • Increasing the number of k-mers used for comparison.
  • Increasing the number of clusters created.
  • Adjusting the comp and con values to optimize the clustering step.

Q: What are the best practices for using the dRep tool?

A: Some best practices for using the dRep tool include:

  • Increasing the memory allocation for the dRep tool.
  • Adjusting the MASH algorithm parameters for larger datasets.
  • Using a larger system with more memory and CPU cores.
  • Optimizing the MASH algorithm parameters for larger datasets.

Q: Where can I find more information about the dRep tool and the MASH algorithm?

A: You can find more information about the dRep tool and the MASH algorithm on the following websites: