A proof-of-concept Bayesian generative model aiming to de novo detect RNA modifications with ONT raw signals in single samples. It was only run on one of the RMAP challenge 2023 datasets. Right now it is mainly (and simply) a joint model of Gaussian mixture models and it probably learns something weird.
git clone https://github.com/chilampoon/bayespore.git
cd bayespore
pip install -e .$ bayespore run --help
Usage: bayespore run [OPTIONS]
Parse inputs and run the model
Options:
-b, --bam PATH BAM file [required]
-p, --pod5 PATH pod5 file [required]
--kmer_table PATH table of reference kmer levels [required]
--reverse_signal Reverse signal, specify if for direct RNA-
seq
--contig TEXT contig name of the reference [default:
template1]
--strand TEXT Reference strand [default: +]
--start INTEGER Start reference position (0-base) [default:
0]
--end INTEGER End reference position (0-base) [required]
--iter_region TEXT Reference region to iterate over, format:
start-end [default: 0-1000]
--win_size INTEGER Window size for modeling bases [default: 5]
--win_dist INTEGER Distance between windows [default: 0]
--middel_base_dist INTEGER Distance between window start and middle
base [default: 2]
--n_mod_status INTEGER Number of modification status [default: 4]
--use_ref_levels Use reference levels of means as prior
--learning_rate FLOAT Learning rate for SVI [default: 0.01]
--n_steps INTEGER Number of SVI steps [default: 2000]
--weight_prior TEXT Prior for modification rates, e.g 0.1,0.9
--weight_prior_concent FLOAT Dirichlet concentration for weight prior
[default: 0.01]
--mod_type TEXT Modification type [default: 5mC]
--min_read_posterior_prob FLOAT
Minimal posterior probability of a read to
be considered as modified [default: 0.9]
-o, --out_dir TEXT Output directory, default is current working
directory
--help Show this message and exit.