💻 Usage

Inference usage

Prepare Data

Accelerated preparation (recommended, default)

• This method uses precompiled C++ binary for accelerating the preprocessing step.

  dorado basecaller --reference <ref_fasta> --min-qscore 0 --emit-moves rna004_130bps_sup@v5.0.0 <pod5_dir> \
  | tee >(samtools sort -@ <threads> -O BAM -o <bam_path> - && samtools index -@ <threads> <bam_path>) \
  | deeprm call prep -p <pod5_dir> -b - -o <prep_dir>
  

• If Dorado fails due to “illegal memory access”, try adding --chunksize <chunk_size> option (e.g., chunk_size=12000).

• If the precompiled binary does not work on your system, please refer to the advanced-installation page for detailed build instructions.

• Adjust the -g (--filter-flag) parameter according to your needs. If using a genomic reference, you may want to use -g 260.

Sequential preparation

• This method is slower than the accelerated preparation method, but is supported for cases such as:

  • The POD5 files are already basecalled to BAM files with move tags.

  • You want to run basecalling and preprocessing in separate machines.

• Basecall the POD5 files to BAM files with move tags (skip if already done):

  • If Dorado fails due to “illegal memory access”, try adding --chunksize <chunk_size> option (e.g., chunk_size=12000).

dorado basecaller --reference <reference_path> --min-qscore 0 --emit-moves rna004_130bps_sup@v5.0.0 <pod5_dir> > <raw_bam_path>"

• Filter, sort, and index the BAM files:

  • Adjust the -F parameter according to your needs. If using a genomic reference, you may want to use -F 260.

samtools view -@ <threads> -bh -F 276 -o <bam_path> <raw_bam_path>
samtools sort -@ <threads> -o <bam_path> <bam_path>
samtools index -@ <threads> <bam_path>

• To preprocess the inference data (transcriptome), run the following command:

deeprm call prep -p <input_POD5_dir> -b <bam_path> -o <prep_dir>

• This will create the npz files for inference.

Run Inference

• The trained DeepRM model file is attached in the repository: weight/deeprm_weights.pt.

• For inference, run the following command:

  • Modify the ‘-s’ (batch size) parameter according to your GPU memory capacity (default: 1000).

deeprm call run --model <model_file> --data <data_dir> --output <prediction_dir> --gpu_pool <gpu_pool>

• This will create a directory with the result files.

• Optionally, if you used a transcriptomic reference for alignment, you can convert the result to genomic coordinates by supplying a RefFlat/GenePred/RefGene file (--annot <annotation_file>).

Site-level BED file format

• The output BED file follows the standard bedMethyl format. Please see https://genome.ucsc.edu/goldenpath/help/bedMethyl.html for description.

• Please note that columns 14 to 18 are zero-filled for compatibility. These columns will be used for a planned future update.

Molecule-level BAM file format

• The output BAM file contains modification information in MM and ML tags. Please see https://samtools.github.io/hts-specs/SAMtags.pdf for description.

Molecule-level NPZ file format (advanced usage)

• The output NPZ file contains the following arrays:

    1. read_id
    2. label_id
    3. pred: modification score (between 0 and 1)

• Read ID specification:

  • The UUID4 format read ID (128 bits) is converted to two 64-bit integers for NumPy compatibility.

  • You can convert the two 64-bit integers back to UUID4 using the following Python code:

    import numpy as np
    import uuid
    def int_to_uuid(high, low):
        return uuid.UUID(bytes=b"".join([high.tobytes(),low.tobytes()]))
    

• Label ID specification:

  • Label ID contains the reference, position, and strand information.

  • You can decode the label ID using the following Python code:

  import numpy as np
  def decode_label_id(label_id, label_div = 10**9):
      strand = np.sign(label_id)
      label_id_abs = np.abs(label_id) - 1
      ref_id = label_id_abs // label_div
      pos = label_id_abs % label_div
      return ref_id, pos, strand
  

  • Reference ID is extracted from the input BAM file header.

Training usage

Prepare Data

• You can skip this step if your POD5 files are already basecalled to BAM files with move tags.

dorado basecaller --min-qscore 0 --emit-moves rna004_130bps_sup@v5.0.0 <pod5_dir> > <bam_path>
samtools index -@ <threads> <bam_path>

• To preprocess the training data (synthetic oligonucleotide), run the following command:

deeprm train prep --input <input_POD5_dir> --output <output_file>

• This will create:

  • Training dataset: /block

• To compile the training dataset, run the following command:

deeprm train compile --input <input_POD5_dir> --output <output_file>

• This will create:

  • Training dataset: /block

Run Training

• To train the model, run the following command:

deeprm train run --model deeprm_model --data <data_dir> --output <output_dir> --gpu_pool <gpu_pool>

• This will create a directory with the trained model file.