Run the pipeline on prokaryotic dataset

[pailloufat-stack]

Hi, I have 5 prokaryotic datasets. I know the HK model was trained on human and mouse DNA, but could I trust the results of pb-CpG-tools on my datasets ? In the 5 *.bed results files, there are about 75,000 5mC sites detected. Best

[ctsa]

pb-CpG-tools provides a utility to summarize site methylation probabilities, this tool is separate from the process used to call 5mC modifications on individual reads.

The site methylation probabilities can be summarized using either a machine-learning model or a simpler pileup count model (see https://github.com/PacificBiosciences/pb-CpG-tools#output-modes-and-option-details). If you'd like a control for the machine-learning model you might consider running this tool in count pileup mode to see if that better fits your intuition for these samples.

[pailloufat-stack]

Thanks for your reply. I'll try. I have one extra question : is the model pileup_calling_model.v1.tflite should be only use for human datasets? I mean, this model would infer only 5mC human patterns because it is trained on the M.SssI-treated and amplified DNA human datasets?

[ctsa]

The generalization of the machine-learning model across species hasn't been systematically evaluated. Given the training scheme, I might expect it would be applicable to something like mouse, but it is not clear that it would transfer to prokaryotes. I would start with the count pileup mode, plus any truth data, and compare these against the model pileup output for this case to access whether the model can reasonably be applied here.

[pailloufat-stack]

--pileup-mode count and --model pb-CpG-tools-v2.3.1-x86_64-unknown-linux-gnu/models/pileup_calling_model.v1.tflite argument give exactly the same results on my 5 datasets

[ctsa]

Hi Sorry for the delay in getting to this. I did some quick tests to try to reproduce what you describe but haven't had any luck. Can you share a more specific example of the problem?

[pailloufat-stack]

Hi, no worries.

I have 5 prokaryotic datasets, and I am looking for methylation motifs, without bias. I already ran the ipdSummary pipeline to detect the 4mC and 6mA motifs. It went pretty well. For the 5mC motifs, what I did for the moment :

1 - Run primrose on my CCS reads 2 - Alignment with pbmm2 3 - Run pb-CpG-tools-v2.3.1-x86_64-unknown-linux-gnu/bin/aligned_bam_to_cpg_scores with both arguments modeand count

I got (I only show the first rows for 1 dataset) :

• mode :

  TRV62_NO042_chromosome_flye     31      32      7.3     Total   677     49      628     7.2
  TRV62_NO042_chromosome_flye     59      60      5.0     Total   681     34      647     5.0
  TRV62_NO042_chromosome_flye     225     226     4.1     Total   683     28      655     4.1
  TRV62_NO042_chromosome_flye     415     416     4.1     Total   684     28      656     4.1

• count

  TRV62_NO042_chromosome_flye     31      32      11.5    Total   677     78      599     0.733   0.083
  TRV62_NO042_chromosome_flye     59      60      16.0    Total   681     109     572     0.752   0.109
  TRV62_NO042_chromosome_flye     225     226     16.7    Total   683     114     569     0.734   0.143
  TRV62_NO042_chromosome_flye     415     416     34.5    Total   684     236     448     0.747   0.169

I have one extra question (I am very new to machine learning) : is the CNN model trained on amplified DNA (unmethylated) and M.SssI-treated DNA (methylated) could be compared to the in silico control of the ipdSummary pipeline ? I mean, both pipelines "compare" data to an existing model, trained and tested on a previous dataset?

[ctsa]

Thanks @pailloufat-stack, I'm not sure we can comment on ipdSummary in depth here - the core modeling concepts are still relevant but have been updated with a more data-driven approach given the availability of a large positive training set from M.SssI-treatment. I'm not up to date if the original item on this ticket is still an issue so please open a new ticket if this is the case.