jasmine identifies specific base modifications in PacBio HiFi reads by analyzing polymerase kinetic signatures.
The current models support detecting 5-Methylcytosine (5mC) at CpG sites and N6-Methyladenine (6mA) in Fiber-seq data.
For 5mC, the caller assumes that methylation is symmetric at the CpG site and reports methylation on the forward strand
of the read. In contrast, the 6mA caller provides strand-specific results. The 6mA calls are compatible with Fiber-seq
downstream analysis tools, such as fibertools
(Jha 2024).
Latest version can be installed via bioconda package pbjasmine.
Please refer to our official pbbioconda page for information on Installation, Support, License, Copyright, and Disclaimer.
Version 2.4.0: Full changelog here
Input for jasmine are PacBio HiFi reads with kinetics. For more info see ccs.how:
Running jasmine is as simple as:
jasmine movie.hifi_reads.bam movie.methylation.hifi_reads.bam
The output methylation prediction for each annotated HiFi read is encoded in the MM and ML tags,
defined in the SAM tag specification.
The MM tag specifies the modification and to which base it applies.
The ML tag specifies the probability of methylation at each base.
The output is also described in the PacBio BAM file format documentation as
| Tag | Type | Description |
|---|---|---|
MM |
Z |
Base modifications / methylation |
ML |
B,C |
Base modification probabilities |
Notes for ML: The continuous probability range of 0.0 to 1.0 is remapped to
the discrete integers 0 to 255 inclusively. The probability range corresponding
to an integer N is N/256 to (N + 1)/256.
The ML tag presents the probabilities in the order of modifications seen in the MM tag.
Read AGTCTAGACTCCGTAATTACTCGCCTAG...
C 1 2 34 5 6 78
CpG * *
MM:Z:C+m,3,1,... # CpG sites are at C #4 (1+3) and #6 (1+3+1+1)
ML:B:C,249,4,... # probability of methylation at the first CpG is in [249/256,250/256); second CpG is in [4/256,5/256).
The jasmine methylation models are trained using a supervised learning approach with curated positive and negative datasets.
| Model | Positive datasets | Negative datasets |
|---|---|---|
| 5mCpG | HG002 WGA + M.SssI | HG002 WGA |
| 6mA | HG002 Fiber-seq | HG002 WGS, HG002 Fiber-seq |
For the HG002 Fiber-seq training dataset, positive and negative labels were generated using fibertools predict-m6A (Jha 2024).
-
2.4.0
- Upcoming release
- Updated 5mC caller
- New 6mA caller
-
2.0.0
- Initial release that supports Sequel II and Revio
- Support for single-strand consensus reads
