bamurai 0.9.0

A package for splitting reads in BAM/FASTQ files into smaller fragments.

Bamurai

A Python toolkit for manipulating BAM and FASTQ files, designed to split reads into smaller fragments, extract statistics, validate files, and manage multi-sample data.

For BAM/SAM/CRAM files, Bamurai only processes primary alignments. Secondary and supplementary alignments are ignored in all commands. This approach ensures that each original read from sequencing is counted only once. Secondary and supplementary alignments represent alternative mappings or split alignments of the same read, not additional unique reads. Including them would artificially inflate read counts and statistics, leading to misleading results.

Description

Bamurai is a command-line tool for splitting reads in BAM/FASTQ files into smaller fragments. It is designed to be fast and efficient, and can be used to split reads into a target length or a target number of pieces per read.

These are the current features of Bamurai:

1. Splitting reads in a file to a target length
2. Splitting reads in a file to a target number of pieces per read
3. Getting statistics from a BAM or FASTQ(.gz) file
4. Basic validation of BAM and FASTQ(.gz) files

The split command splits reads into a target length, each read will be split into fragments as close to the target length as possible. Reads shorter than the target length will not be split.

The divide command splits reads into a target number of pieces, each read will be split into the number of pieces specified. A further minimum length can be specified to ensure that reads are not split if the resultant fragments are less than the minimum length.

The stats command will output the following information by default:

Statistics for input.bam:
  Total reads: 8160
  Average read length: 30638
  Throughput: 250006998
  N50: 82547

It can be used with the --tsv argument to output the statistics in a tab-separated format for computational analysis.

file_name       total_reads     avg_read_len    throughput      n50
input.bam      8160    30638   250006998       82547

The validate command will check the integrity of a BAM or FASTQ(.gz) file and output the following information if the file is valid.:

input.bam is a valid BAM file with 8160 records.

Installation

To install the released version of Bamurai from PyPI

pip install bamurai

To install the latest version of Bamurai from GitHub

pip install git+https://github.com/Shians/Bamurai.git

Usage

To get help on the command-line interface and list available commands

bamurai --help

To get help on a specific command

bamurai <command> --help

Splitting reads to target size

To split a file into 10,000 bp reads

bamurai split input.bam --target-length 10000 --output output.fastq

To create a gzipped output file

bamurai split input.bam --target-length 10000 | gzip > output.fastq.gz

Dividing reads into a target number of pieces

To divide reads into 2 pieces

bamurai divide input.bam --num_fragments 2 --output output.fastq

To divide reads into 2 pieces unless resultant fragments are less than 1000 bp

bamurai divide input.bam --num_fragments 2 --min_length 1000 --output output.fastq

Getting statistics from a BAM or FASTQ file

To get stats from a BAM file

bamurai stats input.bam

To get stats from a FASTQ file or Gzipped FASTQ file

bamurai stats input.fastq
bamurai stats input.fastq.gz

Validating BAM or FASTQ files

To validate a BAM file

bamurai validate input.bam

Working with multi-sample BAM files

Bamurai provides commands for processing BAM files with multiple samples based on barcode information.

Splitting BAM or FASTQ files by donor ID

To split a BAM or FASTQ file into multiple files, one for each donor ID:

bamurai split_samples --input input.bam --tsv barcode_to_donor.tsv --output-dir donor_bams
bamurai split_samples --input input.fastq.gz --tsv barcode_to_donor.tsv --output-dir donor_fastqs

The TSV file should contain at least two columns with headers 'barcode' and 'donor_id'. Each row maps a barcode to a donor ID.

You can process multiple BAM or FASTQ files at once:

bamurai split_samples --input input1.bam input2.bam --tsv barcode_to_donor.tsv --output-dir donor_bams
bamurai split_samples --input input1.fastq.gz input2.fastq.gz --tsv barcode_to_donor.tsv --output-dir donor_fastqs

Extracting reads for a specific donor

To extract all reads belonging to a specific donor from a BAM file:

bamurai extract_sample --bam input.bam --tsv barcode_to_donor.tsv --donor-id donor1 --output donor1.bam

You can also process multiple BAM files at once, combining all donor-specific reads into a single output file:

bamurai extract_sample --bam input1.bam input2.bam input3.bam --tsv barcode_to_donor.tsv --donor-id donor1 --output donor1.bam

This command will extract all reads with barcodes belonging to the specified donor ID and write them to a new BAM file.

Assigning samples to barcodes

The assign_samples command assigns donor IDs to barcodes based on a provided TSV mapping file. This is useful for annotating barcodes in single-cell data.

To assign donor IDs to barcodes in a TSV file:

bamurai assign_samples --barcodes barcodes.tsv --tsv barcode_to_donor.tsv --output assigned_barcodes.tsv

• barcodes.tsv should contain a list of barcodes (one per line or as a column in a table).
• barcode_to_donor.tsv should have at least two columns: 'barcode' and 'donor_id'.
• The output file assigned_barcodes.tsv will contain the barcodes with their assigned donor IDs.

You can also specify a custom column name for barcodes in the input file:

bamurai assign_samples --barcodes barcodes.tsv --tsv barcode_to_donor.tsv --barcode-column cell_barcode --output assigned_barcodes.tsv

This will use the column 'cell_barcode' in barcodes.tsv as the barcode column.