pb-CpG-tools

pb-CpG-tools provides the tool aligned_bam_to_cpg_scores, which can generate site methylation probabilities from mapped HiFi reads, including probabilities for each haplotype when reads are haplotagged. The site probabilities are reported as both bed and bigwig file outputs. The example below from HG002 shows how these bigwig tracks can be used to visualize both combined and haplotype-specific methylation probabilities in IGV

Getting started

To use aligned_bam_to_cpg_scores download the latest release tarball compiled for 64-bit linux platforms on the github release channel, then unpack the tar file. As an example, the v2.3.2 release can be obtained as follows:

wget https://github.com/PacificBiosciences/pb-CpG-tools/releases/download/v2.3.2/pb-CpG-tools-v2.3.2-x86_64-unknown-linux-gnu.tar.gz
tar -xzf pb-CpG-tools-v2.3.2-x86_64-unknown-linux-gnu.tar.gz

# Run help option to test binary and see latest usage details:
pb-CpG-tools-v2.3.2-x86_64-unknown-linux-gnu/bin/aligned_bam_to_cpg_scores --help

aligned_bam_to_cpg_scores includes a number of ways to summarize site propabilities for 5mC methylation, detailed below. The recommended default workflow will use the model pileup mode and denovo modsites mode. Continuing from the example above, the script below runs the tool on a mapped WGS bam for HG002:

pb-CpG-tools-v2.3.2-x86_64-unknown-linux-gnu/bin/aligned_bam_to_cpg_scores \
  --bam HG002.hg38.pbmm2.bam \
  --output-prefix HG002.hg38.pbmm2 \
  --model pb-CpG-tools-v2.3.2-x86_64-unknown-linux-gnu/models/pileup_calling_model.v1.tflite \
  --threads 8

See the cmdline usage help for the full list of other commandline options:

aligned_bam_to_cpg_scores --help

Input Alignment File

The input alignment file should contain mapped reads in BAM or CRAM format and have an associated index file (.bai, .csi or .crai). 5mC base modification values are read from the MM and ML auxiliary tags which encode base modifications and confidence values. These tags are further described in the SAM tag specification document. These are the default tags generated by jasmine for 5mC modifications on HiFi reads.

Methylated reads must be mapped using a method that does not hard-clip supplementary alignments, such as:

• pbmm2 (recommended)
• minimap2 with the -Y flag.

The reason for this restriction is that any process which shortens reads in the BAM file without also recalculating the MM and ML auxiliary tags will lead to an invalid decoding of the methylation values.

Note the following additional requirements on the input alignment file:

• CRAM format is supported but the corresponding fasta reference file must be specified in this case.
• Multiple base modification types may be present in the file's MM and ML tags, but only the C+m modification key will be processed, other modification keys such as A+a will be ignored. 5mC modifications encoded with a multiple-modification key, such as C+mh, cannot be used.

Output Files

There are bed (.bed) and bigwig (.bw) files generated for the complete read set and each separate haplotype (when haplotype tags are available in the bam). The bed files are a text format that is easier to review and contains additional site data, the bigwig files are an indexed binary format designed to be loaded into IGV for visualization.

The following 2 files are always generated:

[output-prefix].combined.bed
[output-prefix].combined.bw

If haplotype information is present in the input alignment file, an additional 4 output files are expected:

[output-prefix].hap1.bed 
[output-prefix].hap1.bw 
[output-prefix].hap2.bed
[output-prefix].hap2.bw

Bed file format

The bed file columns will differ between the model and count pileup methods, but both share the first six columns:

1. reference name
2. start coordinate
3. end coordinate
4. modification score
5. haplotype
6. coverage

Here the modification score is the methylation probability at the site expressed as a percentage. See the section on --pileup-mode below for a description of how this is calculated for each mode.

Additional columns in the bed output file depend on which pileup mode is selected. For the count pileup mode, four additional columns are present:

7. modified site count
8. unmodified site count
9. average mod score
10. average unmod score

For the model pileup mode, three additional columns are present:

7. estimated modified site count (extrapolated from model modification probability)
8. estimated unmodified site count (extrapolated from model modification probability)
9. discretized modification probability (calculated from estimated mod/unmod site counts)

Bigwig file format

The bigwig files are an indexed binary format which contain columns 1-4 listed above, and are preferred for loading 5mC tracks in IGV.

Output modes and option details

The --pileup-mode argument selects the modification probability calculation

• model: (default) This is the recommended pileup mode. It uses distributions of modification scores and a machine-learning model to calculate the modification probabilities across CpG sites. When using this option a path to a tflite model file must be provided with the --model argument. The current recommended model is pileup_calling_model.v1.tflite found in the models directory.
• count: For a given site, all bases with a 5mC modification probability greater than 0.5 are classified as modified, and all other bases are classified as unmodified. The methylation probability for a site is set to the proportion of bases classified as modified. In addition, the count and average modification probability (expressed as a percentage), are provided for both the modified and unmodified sets of bases in the bed file output.

The --modsites-mode argument determines which sites will be reported

• denovo: (default) This option will identify and output all CG sites found in the consensus sequence from the reads in the pileup. This allows reporting of CG sites with zero modification probability. This mode does not ensure that the reference also displays a CG site (e.g., there could be sequence mismatches between the reads and reference).
• reference: This option will identify and output all CG sites found in the reference sequences. This allows reporting of CG sites with zero modification probability. This mode does not ensure that aligned reads also display a CG site (e.g., there could be sequence mismatches between the reads and reference).

Using the --hap-tag flag allows an arbitrary SAM tag to be used to identify haplotypes, rather than the default HP tag. The haplotype values must be 0, 1, and 2, where 0 is not assigned/ambiguous.

Example Data

An aligned BAM file containing HiFi reads with 5mC tags (HG002.GRCh38.haplotagged.bam) is freely available for download: https://downloads.pacbcloud.com/public/dataset/HG002-CpG-methylation-202202/

The sample is HG002/NA24385 from the Human Pangenome Reference Consortium HG002 Data Freeze v1.0, and is aligned to GRCh38. There are also four unaligned bam files containing the HiFi reads.

Performance

Given a human ~30x WGS aligned BAM, such as the example data above, it should take approximately 40 minutes to complete default mode pileup track generation on 8 threads using 6 Gb of memory. Runtime should scale approximately linearly with thread count until limited by the filesystem's ability to read the bam file in parallel. Peak memory demand should be approximately constant for a human sample at 6 Gb regardless of thread count.

Comparison to Version 1

5mC site analysis was previously provided by the aligned_bam_to_cpg_scores.py python script in version 1.x releases.

The version 2 release of aligned_bam_to cpg_scores should have approximate feature parity with the previous script, although the command-line details have changed.

Given the same bam file and selected output modes, version 2 results should logically match the version 1 script, however the results will contain a number of minor expected differences:

1. Floating point thresholds and rounding differences lead to a small number of scoring differences.
2. In denovo modsites mode, tie-breaking details of the python implementation lead to a very small number of marginally denovo detected CpG not being included in this version.
3. The version 1 script output has some minor artifacts at its parallel 'chunksize' boundaries. These issues do not occur in version 2.
4. In count pileup mode, the bam ML tag values are translated into probabilities by taking bin mid-points, so all input probabilities are shifted by +(1/512) compared to version 1, which has a corresponding influence on the summarized site output.

Version 1 availability

The python version of the code is deprecated, but if needed the final version of this script before the version 2 update is available here.

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