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 (split)
2. Splitting reads in a file to a target number of pieces per read (divide)
3. Getting statistics from a BAM or FASTQ(.gz) file (stats)
4. Basic validation of BAM and FASTQ(.gz) files (validate)
5. Splitting a file into size-based chunks (chunk)
6. Splitting or extracting reads by donor ID using a barcode mapping (split_samples, extract_sample, assign_samples)
7. Extracting hashtag oligo (HTO) information from 10x FASTQ pairs (get_hto)
8. Tagging BAM reads with arbitrary values from a TSV keyed by read ID (add_tags)

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 (default 2), 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 (default 100).

The stats command will output the following information by default:

Statistics for input.bam:
  Total reads: 8160
  Average read length: 30638
  Throughput (Gb): 0.25
  N50: 82547

It can be used with the --tsv argument to output the statistics in a tab-separated format for computational analysis. Note that throughput is reported in raw bases here, rather than the gigabases of the default output.

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.

Unlike the read-processing commands, validate inspects every record in the file, so the count it reports includes any secondary and supplementary alignments.

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 --len-target 10000 --output output.fastq

To create a gzipped output file

bamurai split input.bam --len-target 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

Splitting a file into chunks

To split a file into chunks of at least 1 GB each

bamurai chunk input.bam --size 1G

Sizes are given in human-readable notation (1G, 100M, 1000K). Each chunk is at least the requested size, and may be slightly larger because reads are never split across chunks.

Output files are written as chunk_1.fastq, chunk_2.fastq, and so on. Use --prefix to change the name

bamurai chunk input.bam --size 100M --prefix sample_a

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 a barcode column and a donor ID column, one row per barcode; see Barcode and donor columns below for how those columns are found.

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.

Tagging reads with their donor ID

The assign_samples command labels each read in a BAM with the donor it came from, using the RG (read group) tag. Unlike split_samples, which writes one file per donor, this keeps every read in a single BAM so that downstream tools can group by read group.

bamurai assign_samples --bam input.bam --tsv barcode_to_donor.tsv --output assigned.bam

This writes assigned.bam containing every read from the input, where:

• reads whose cell barcode maps to a donor carry an RG tag naming that donor
• reads with an unknown or absent barcode are written through untagged
• the header gains an @RG record per donor, with ID and SM both set to the donor ID

A summary of how many reads were assigned to each donor is printed on completion. Note that --bam takes a single BAM, unlike split_samples and extract_sample, which accept several.

Barcode and donor columns

split_samples, extract_sample and assign_samples all read the same barcode-to-donor TSV, and all detect its columns the same way. The barcode column is taken from a column named barcode or cell, and the donor column from one named donor_id. If both barcode and cell are present the file is ambiguous and the command fails, asking you to choose.

Use --barcode-column and --donor-id-column when your columns are named something else, or to resolve that ambiguity:

bamurai extract_sample --bam input.bam --tsv mapping.tsv --donor-id donor1 \
  --barcode-column cell_barcode --donor-id-column sample --output donor1.bam

Which barcode tag is read

For BAM input, a read's cell barcode is read from the first of these tags that is present:

BC and RX are deliberately not consulted: per the SAM specification BC identifies a sample and RX holds the UMI, so neither names the cell that a donor is assigned to. A read carrying only those tags is treated as having no barcode.

Extracting HTO information

The get_hto command pulls hashtag oligo (HTO) information out of a pair of 10x FASTQ files. It assumes R1 holds the cell barcode followed by the UMI, and R2 holds the hashtag sequence.

bamurai get_hto --r1 sample_R1.fastq.gz --r2 sample_R2.fastq.gz \
  --bc-len 16 --umi-len 12 --output htos.tsv

--bc-len and --umi-len give the barcode and UMI lengths in R1, and are required. The output is a tab-separated file with the columns read_name, cell_barcode, umi, hto, bc_qual, umi_qual and hto_qual, where the three _qual columns carry the FASTQ quality strings for the preceding sequences.

Where in R2 the hashtag is read from can be adjusted with --hashtag-len (how many bases to take, default 15) and --hashtag-left-buffer (how many bases to skip first, default 10).

Tagging reads from a TSV

To tag reads in a BAM file with values from a TSV, keyed by read_id:

bamurai add_tags --bam input.bam --tsv tags.tsv --output tagged.bam

The TSV must have a header row with a read_id column plus one column per tag, where each tag column name is the 2-letter BAM tag to apply (e.g., XX, RG) — every column besides read_id is applied as a tag. Tag values are auto-typed as int, float, or string. Reads not found in the TSV, or with a blank cell for a given tag column, are left untagged for that tag. Only primary alignments are tagged.