mCaller.py - find methylation in nanopore reads
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usage: mCaller [-h] (-p POSITIONS | -m MOTIF)
-r REFERENCE -e TSV -f FASTQ
- [-t THREADS] [-b BASE] [-n NUM_VARIABLES] [--train] [--training_tsv
TRAINING_TSV] [-d MODELFILE] [-s SKIP_THRESH] [-q QUAL_THRESH] [-c
CLASSIFIER] [--plot_training] [-v]
Classify bases as methylated or unmethylated
- -h, --help
- show this help message and exit
- -p POSITIONS,
--positions POSITIONS
- file with a list of positions at which to classify bases (must be
formatted as space- or tab-separated file with chromosome, position,
strand, and label if training)
- -m MOTIF, --motif
MOTIF
- classify every base of type --base in the motif specified instead
(can be single one-mer)
- -r REFERENCE,
--reference REFERENCE
- fasta file with reference aligned to
- -e TSV, --tsv
TSV
- tsv file with nanopolish event alignment
- -f FASTQ, --fastq
FASTQ
- fastq file with nanopore reads
- -t THREADS, --threads
THREADS
- specify number of processes (default = 1)
- -b BASE, --base
BASE
- bases to classify as methylated or unmethylated (A or C, default A)
- -n NUM_VARIABLES,
--num_variables NUM_VARIABLES
- change the length of the context used to classify (default of 6 variables
corresponds to 11-mer context (6*2-1))
- --train
- train a new model (requires labels in positions file)
- --training_tsv
TRAINING_TSV
- mCaller output file for training
- -d MODELFILE,
--modelfile MODELFILE
- model file name
- -s SKIP_THRESH,
--skip_thresh SKIP_THRESH
- number of skips to allow within an observation (default 0)
- -q QUAL_THRESH,
--qual_thresh QUAL_THRESH
- quality threshold for reads (default none)
- -c CLASSIFIER,
--classifier CLASSIFIER
- use alternative classifier: options = NN (default), RF, LR, or NBC
(non-default may significantly increase runtime)
- --plot_training
- plot probabilities distributions for training positions (requires labels
in positions file and --train)
- -v, --version
- print version