bam2tensor 1.5

Bam2Tensor

bam2tensor

bam2tensor is a Python package for converting bisulfite-sequencing .bam files to sparse tensor representations of DNA methylation data. It extracts read-level methylation states from CpG sites and outputs efficient sparse COO matrices as .npz files, ready for deep learning pipelines.

Table of Contents

• Features
• Requirements
• Installation
• Quick Start
• Usage
  • Basic Usage
  • Processing Multiple BAM Files
  • Using a Custom Genome
  • Command-Line Options
• Output Data Structure
  • Loading Output Files
  • Converting to Dense Arrays
  • Working with Genomic Coordinates
• Supported Aligners
• Performance Tips
• API Reference
• Contributing
• License
• Credits

Features

• BAM Parsing: Efficiently parses .bam files using pysam
• Complete CpG Extraction: Extracts methylation data from all CpG sites genome-wide
• Multi-Genome Support: Works with any reference genome (GRCh38/hg38, T2T-CHM13, mm10, etc.)
• Sparse Storage: Stores data in sparse COO matrix format for memory-efficient loading
• NumPy/SciPy Integration: Exports to .npz files compatible with NumPy and SciPy
• Efficient Algorithm: Linear-scan algorithm ensures minimal memory usage with no read duplication
• Batch Processing: Process multiple BAM files with directory recursion
• Caching: CpG site indexing is cached to accelerate repeated runs on the same genome
• Quality Filtering: Configurable mapping quality thresholds

Requirements

• Python 3.10 or higher
• A reference genome FASTA file (must match the genome used for alignment)
• Indexed BAM files (.bam with corresponding .bam.bai index files)

Dependencies

Core dependencies are automatically installed:

• pysam - BAM file handling
• biopython - FASTA parsing
• scipy - Sparse matrix operations
• numpy - Numerical operations
• click - Command-line interface
• tqdm - Progress bars

Installation

From PyPI (Recommended)

pip install bam2tensor

From Source

git clone https://github.com/mcwdsi/bam2tensor.git
cd bam2tensor
pip install .

Development Installation

git clone https://github.com/mcwdsi/bam2tensor.git
cd bam2tensor
pip install poetry
poetry install

Quick Start

# Basic usage with a single BAM file
bam2tensor \
    --input-path sample.bam \
    --reference-fasta GRCh38.fa \
    --genome-name hg38

# This creates: sample.methylation.npz

Usage

Basic Usage

Process a single bisulfite-sequencing BAM file:

bam2tensor \
    --input-path /path/to/aligned_reads.bam \
    --reference-fasta /path/to/reference.fa \
    --genome-name hg38

This will:

1. Parse the reference FASTA to identify all CpG sites (cached for future runs)
2. Extract methylation states from each read in the BAM file
3. Output a sparse matrix to aligned_reads.methylation.npz

Processing Multiple BAM Files

Process all BAM files in a directory recursively:

bam2tensor \
    --input-path /path/to/bam_directory/ \
    --reference-fasta /path/to/reference.fa \
    --genome-name hg38 \
    --verbose

Each BAM file will generate a corresponding .methylation.npz file in the same location.

Using a Custom Genome

For non-human genomes or custom chromosome sets:

# Mouse genome (mm10)
bam2tensor \
    --input-path mouse_sample.bam \
    --reference-fasta mm10.fa \
    --genome-name mm10 \
    --expected-chromosomes "chr1,chr2,chr3,chr4,chr5,chr6,chr7,chr8,chr9,chr10,chr11,chr12,chr13,chr14,chr15,chr16,chr17,chr18,chr19,chrX,chrY"

# T2T-CHM13 human genome
bam2tensor \
    --input-path sample.bam \
    --reference-fasta chm13v2.0.fa \
    --genome-name T2T-CHM13 \
    --expected-chromosomes "chr1,chr2,chr3,chr4,chr5,chr6,chr7,chr8,chr9,chr10,chr11,chr12,chr13,chr14,chr15,chr16,chr17,chr18,chr19,chr20,chr21,chr22,chrX,chrY"

Command-Line Options

Usage: bam2tensor [OPTIONS]

  Extract read-level methylation data from an aligned .bam file and export
  the data as a SciPy sparse matrix.

Options:
  --version                       Show the version and exit.
  --input-path PATH               Input .bam file OR directory to recursively
                                  process.  [required]
  --genome-name TEXT              A custom string referring to your genome
                                  name, used to save a cache file (e.g. hg38,
                                  hg38-no-alt, etc.).  [required]
  --expected-chromosomes TEXT     A comma-separated list of chromosomes to
                                  expect in the .fa genome. Defaults to hg38
                                  chromosomes (chr1-chr22, chrX, chrY).
  --reference-fasta PATH          Reference genome FASTA file (critical to
                                  determine CpG sites).  [required]
  --quality-limit INTEGER         Quality filter for aligned reads (default =
                                  20).
  --verbose                       Verbose output.
  --skip-cache                    De-novo generate CpG sites (slow).
  --debug                         Debug mode (extensive validity checking +
                                  debug messages).
  --overwrite                     Overwrite output file if it exists.
  --help                          Show this message and exit.

Option Details

Option	Description
--input-path	Path to a single .bam file or a directory. If a directory is provided, all .bam files are processed recursively.
--genome-name	An identifier for your reference genome (e.g., hg38, mm10). Used to name the cache file for CpG site positions.
--expected-chromosomes	Comma-separated list of chromosome names to process. Chromosomes not in this list are skipped. Defaults to human autosomes + sex chromosomes.
--reference-fasta	Path to the reference genome FASTA file. Must match the genome used for alignment.
--quality-limit	Minimum mapping quality score (MAPQ) for reads to be included. Default is 20.
--verbose	Enable detailed progress output including per-chromosome progress bars.
--skip-cache	Force regeneration of CpG site cache. Useful if you've modified the reference or chromosome list.
--debug	Enable extensive validation and debug output. Slower but useful for troubleshooting.
--overwrite	Overwrite existing .methylation.npz files. Without this flag, existing outputs are skipped.

Output Data Structure

bam2tensor generates one .npz file per input BAM file. Each file contains a SciPy sparse COO matrix with the following structure:

Dimension	Represents
Rows	Unique reads (primary alignments that pass quality filters)
Columns	CpG sites (ordered by genomic position across all chromosomes)

Methylation State Values

Value	Meaning
1	Methylated (cytosine preserved as C)
0	Unmethylated (cytosine converted to T by bisulfite treatment)
-1	No data (indel, SNV, or site not covered by read)

Note: Sparse matrices only store non-zero values. Positions with value 0 (unmethylated) are stored, but positions not covered by a read are simply absent from the matrix.

Loading Output Files

import scipy.sparse
import numpy as np

# Load the sparse matrix
methylation_matrix = scipy.sparse.load_npz("sample.methylation.npz")

print(f"Matrix shape: {methylation_matrix.shape}")
print(f"Number of reads: {methylation_matrix.shape[0]}")
print(f"Number of CpG sites: {methylation_matrix.shape[1]}")
print(f"Non-zero entries: {methylation_matrix.nnz}")
print(f"Sparsity: {1 - methylation_matrix.nnz / np.prod(methylation_matrix.shape):.4%}")

Converting to Dense Arrays

For small regions or when dense operations are needed:

# Convert entire matrix to dense (warning: may use significant memory)
dense_matrix = methylation_matrix.toarray()

# Convert to CSR format for efficient row slicing
csr_matrix = methylation_matrix.tocsr()

# Get methylation data for reads 0-99
subset = csr_matrix[0:100, :].toarray()

# Convert to CSC format for efficient column slicing
csc_matrix = methylation_matrix.tocsc()

# Get data for CpG sites 1000-1099
cpg_subset = csc_matrix[:, 1000:1100].toarray()

Working with Genomic Coordinates

To map between matrix column indices and genomic coordinates, use the GenomeMethylationEmbedding class:

from bam2tensor.embedding import GenomeMethylationEmbedding

# Load or recreate the embedding used during extraction
embedding = GenomeMethylationEmbedding(
    genome_name="hg38",
    expected_chromosomes=["chr" + str(i) for i in range(1, 23)] + ["chrX", "chrY"],
    fasta_source="/path/to/GRCh38.fa",
)

# Convert matrix column index to genomic position
chrom, pos = embedding.embedding_to_genomic_position(12345)
print(f"Column 12345 corresponds to {chrom}:{pos}")

# Convert genomic position to matrix column index
col_idx = embedding.genomic_position_to_embedding("chr1", 10525)
print(f"chr1:10525 is at column {col_idx}")

# Get total number of CpG sites
print(f"Total CpG sites: {embedding.total_cpg_sites:,}")

Example: Analyzing Methylation Patterns

import scipy.sparse
import numpy as np

# Load the data
matrix = scipy.sparse.load_npz("sample.methylation.npz")
csr = matrix.tocsr()

# Calculate per-CpG methylation rates (excluding -1 values)
methylation_rates = []
for cpg_idx in range(matrix.shape[1]):
    col_data = csr.getcol(cpg_idx).toarray().flatten()
    # Filter out -1 (no data) and positions with no coverage
    valid_data = col_data[(col_data >= 0)]
    if len(valid_data) > 0:
        rate = np.mean(valid_data)
    else:
        rate = np.nan
    methylation_rates.append(rate)

methylation_rates = np.array(methylation_rates)
print(f"Mean methylation rate: {np.nanmean(methylation_rates):.2%}")
print(f"CpG sites with coverage: {np.sum(~np.isnan(methylation_rates)):,}")

Example: Integration with PyTorch

import torch
import scipy.sparse
import numpy as np

# Load sparse matrix
matrix = scipy.sparse.load_npz("sample.methylation.npz")

# Convert to PyTorch sparse tensor
coo = matrix.tocoo()
indices = torch.LongTensor(np.vstack((coo.row, coo.col)))
values = torch.FloatTensor(coo.data)
shape = torch.Size(coo.shape)

sparse_tensor = torch.sparse_coo_tensor(indices, values, shape)
print(f"PyTorch sparse tensor shape: {sparse_tensor.shape}")

# For models that need dense input (specific region)
region_start, region_end = 0, 1000
dense_region = matrix.tocsc()[:, region_start:region_end].toarray()
dense_tensor = torch.FloatTensor(dense_region)

Supported Aligners

bam2tensor supports BAM files from bisulfite-aware aligners that include strand information tags:

Aligner	Tag	Values
Biscuit	YD	f (forward/OT/CTOT), r (reverse/OB/CTOB)
gem3 / Blueprint	XB	C (forward), G (reverse)

These tags indicate which strand the original bisulfite-converted DNA came from, which is essential for correctly interpreting C/T as methylated/unmethylated.

Performance Tips

1. Use the cache: The first run on a new genome builds a CpG site index, which is cached. Subsequent runs are much faster.

2. Process in parallel: bam2tensor processes one BAM at a time, but you can run multiple instances in parallel on different BAM files:

   # Using GNU parallel
   find /data/bams -name "*.bam" | parallel -j 4 \
       bam2tensor --input-path {} --reference-fasta ref.fa --genome-name hg38
   

3. Ensure BAM files are indexed: Each BAM file requires a corresponding .bam.bai index file. Create with:

   samtools index sample.bam
   

4. Use SSDs: Both reading BAM files and writing output benefit from fast storage.

5. Memory considerations: Memory usage scales with the number of CpG sites (columns) rather than reads. For human genomes (~28M CpG sites), expect moderate memory usage.

API Reference

bam2tensor.embedding.GenomeMethylationEmbedding

Main class for managing CpG site positions and coordinate conversions.

GenomeMethylationEmbedding(
    genome_name: str,           # Identifier for caching
    expected_chromosomes: list, # List of chromosome names to process
    fasta_source: str,          # Path to reference FASTA
    skip_cache: bool = False,   # Force regeneration of cache
    verbose: bool = False       # Enable verbose output
)

Key Methods:

• embedding_to_genomic_position(embedding: int) -> tuple[str, int] - Convert column index to (chromosome, position)
• genomic_position_to_embedding(chrom: str, pos: int) -> int - Convert genomic position to column index

Key Attributes:

• total_cpg_sites: int - Total number of CpG sites across all chromosomes
• cpg_sites_dict: dict[str, list[int]] - Dictionary mapping chromosome names to lists of CpG positions

bam2tensor.functions.extract_methylation_data_from_bam

Core function for extracting methylation data from a BAM file.

extract_methylation_data_from_bam(
    input_bam: str,                                    # Path to BAM file
    genome_methylation_embedding: GenomeMethylationEmbedding,  # Embedding object
    quality_limit: int = 20,                           # Minimum MAPQ
    verbose: bool = False,                             # Enable verbose output
    debug: bool = False                                # Enable debug output
) -> scipy.sparse.coo_matrix

Returns: A SciPy COO sparse matrix with shape (n_reads, n_cpg_sites).

Contributing

Contributions are welcome! Please see the Contributor Guide for guidelines on:

• Setting up a development environment
• Running tests
• Code style requirements
• Submitting pull requests

Development Commands

# Install development dependencies
poetry install

# Run all checks (linting, type checking, tests)
nox

# Run specific checks
nox --session=tests       # Run pytest
nox --session=mypy        # Type checking
nox --session=pre-commit  # Linting

# Format code
poetry run black src tests
poetry run ruff check --fix src tests

License

Distributed under the terms of the MIT license, bam2tensor is free and open source software.

Issues

If you encounter any problems, please file an issue with:

• A description of the problem
• Steps to reproduce
• Your Python version and operating system
• Relevant error messages or logs

Credits

This project is developed and maintained by Nick Semenkovich (@semenko), as part of the Medical College of Wisconsin's Data Science Institute.

This project was generated from Statistics Norway's SSB PyPI Template.