bcpd / bam-filter

A simple tool to calculate metrics from a BAM file and filter references with uneven coverages

filterBAM

A simple tool to calculate metrics from a BAM file and filter references with uneven coverages.

Installation

We recommend having conda installed to manage the virtual environments

Using pip

First, we create a conda virtual environment with:

wget https://raw.githubusercontent.com/genomewalker/bam-filter/master/environment.yml
conda env create -f environment.yml

Then we proceed to install using pip:

pip install bam-filter

Using conda

conda install -c conda-forge -c bioconda -c genomewalker bam-filter

Install from source to use the development version

Using pip

pip install git+ssh://[e-mail]/genomewalker/bam-filter.git

By cloning in a dedicated conda environment

git clone [e-mail]:genomewalker/bam-filter.git
cd bam-filter
conda env create -f environment.yml
conda activate bam-filter
pip install -e .

Usage

filterBAM only needs a BAM file. For a complete list of options:

$ filterBAM --help
usage: filterBAM [-h] --bam BAM [-t INT] [--reference-trim-length INT] [--trim-min INT]
                 [--trim-max INT] [-p STR] [-A FLOAT] [-l INT] [-n INT] [-b FLOAT] [-e FLOAT]
                 [-g FLOAT] [-B FLOAT] [-a FLOAT] [-c FLOAT] [-V FLOAT] [-C FLOAT]
                 [--include-low-detection] [-m STR] [-N] [--disable-sort] [--scale STR] [-r FILE]
                 --stats [FILE] [--stats-filtered [FILE]] [--bam-filtered [FILE]]
                 [--read-length-freqs [FILE]] [--read-hits-count [FILE]] [--knee-plot [FILE]]
                 [--coverage-plots [FILE]] [--chunk-size INT] [--tmp-dir DIR] [--low-memory] [--debug]
                 [--version]

A simple tool to calculate metrics from a BAM file and filter with uneven coverage.

optional arguments:
  -h, --help            show this help message and exit
  --bam BAM             BAM file containing aligned reads (default: None)
  -t INT, --threads INT
                        Number of threads to use (default: 1)
  -p STR, --prefix STR  Prefix used for the output files (default: None)
  -m STR, --sort-memory STR
                        Set maximum memory per thread for sorting; suffix K/M/G recognized (default:
                        1G)
  -N, --sort-by-name    Sort by read names (default: False)
  --disable-sort        Disable sorting of the filtered BAM file (default: False)
  --scale STR           Scale taxonomic abundance by this factor; suffix K/M recognized (default:
                        1000000.0)
  -r FILE, --reference-lengths FILE
                        File with references lengths (default: None)
  --chunk-size INT      Chunk size for parallel processing (default: None)
  --tmp-dir DIR         Temporary directory (default: None)
  --low-memory          Activate the low memory mode (default: False)
  --debug               Print debug messages (default: False)
  --version             Print program version

filtering arguments:
  -A FLOAT, --min-read-ani FLOAT
                        Minimum read ANI to keep a read (default: 90.0)
  -l INT, --min-read-length INT
                        Minimum read length (default: 30)
  -n INT, --min-read-count INT
                        Minimum read count (default: 3)
  -b FLOAT, --min-expected-breadth-ratio FLOAT
                        Minimum expected breadth ratio (default: 0)
  -e FLOAT, --min-normalized-entropy FLOAT
                        Minimum normalized entropy (default: 0)
  -g FLOAT, --min-normalized-gini FLOAT
                        Minimum normalized Gini coefficient (default: 1.0)
  -B FLOAT, --min-breadth FLOAT
                        Minimum breadth (default: 0)
  -a FLOAT, --min-avg-read-ani FLOAT
                        Minimum average read ANI (default: 90.0)
  -c FLOAT, --min-coverage-evenness FLOAT
                        Minimum coverage evenness (default: 0)
  -V FLOAT, --min-coeff-var FLOAT
                        Minimum coverage evenness calculated as SD/MEAN (default: inf)
  -C FLOAT, --min-coverage-mean FLOAT
                        Minimum coverage mean (default: 0)
  --include-low-detection
                        Include those references that fullfi all filtering criteria but the coverage
                        evenness is 0 (default: False)

miscellaneous arguments:
  --reference-trim-length INT
                        Exclude n bases at the ends of the reference sequences (default: 0)
  --trim-min INT        Remove coverage that are below this percentile. Used for the Truncated Average
                        Depth (TAD) calculation (default: 10)
  --trim-max INT        Remove coverage that are above this percentile. Used for the Truncated Average
                        Depth (TAD) calculation (default: 90)

output arguments:
  --stats [FILE]        Save a TSV file with the statistics for each reference (default: None)
  --stats-filtered [FILE]
                        Save a TSV file with the statistics for each reference after filtering
                        (default: None)
  --bam-filtered [FILE]
                        Save a BAM file with the references that passed the filtering criteria
                        (default: None)
  --read-length-freqs [FILE]
                        Save a JSON file with the read length frequencies mapped to each reference
                        (default: None)
  --read-hits-count [FILE]
                        Save a TSV file with the read hits frequencies mapped to each reference
                        (default: None)
  --knee-plot [FILE]    Plot knee plot (default: None)
  --coverage-plots [FILE]
                        Folder where to save genome coverage plots (default: None)

One would run filterBAM as:

filterBAM --stats --min-read-count 100 --min-expected-breadth-ratio 0.75 --min-read-ani 98 --sort-by-name --sort-memory 1G --reference-lengths gtdb-r202.len.map --threads 16 --bam c55d4e2df1.dedup.bam 

--stats: Save a TSV file with the statistics for each reference

--min-read-count: Minimum number of reads mapped to a reference in the BAM file

--min-expected-breadth-ratio: Minimum expected breadth ratio needed to keep a reference.

--min-read-ani: Minimum average read ANI that a reference has

--sort-by-name: Sort filtered BAM file by read name so it can be used in metaDMG

--sort-memory: Memory used for each thread when sorting the filtered BAM file

--reference-lengths: File with the lengths of the references in the BAM file. This is used to calculate the coverage estimates of each reference when multiple contigs have been concatenad with Ns.

--threads: Number of threads

The program will produce two main outputs:

• A BAM file where the references that are below the defined threshold have been filtered out
• A TSV file with statistics for each reference, with the following columns:
  • reference: Reference name
  • n_reads: Number of reads mapped to the reference
  • n_alns: Number of alignments in the reference
  • read_length_mean: Mean read length mapped to the reference
  • read_length_std: Standard deviation of read lengths mapped to the reference
  • read_length_min: Minimum read length mapped to the reference
  • read_length_max: Maximum read length mapped to the reference
  • read_length_median: Medium read length mapped to the reference
  • read_length_mode: Modal read length mapped to the reference
  • gc_content: Average GC content of the reads mapped to the reference
  • read_aligned_length: Average aligned read length mapped to the reference
  • read_aln_score: Average alignment score of the reads mapped to the reference
  • mapping_quality: Average mapping quality of the reads mapped to the reference
  • edit_distances: Average edit distance of the reads mapped to the reference
  • read_ani_mean: Average ANI of the reads mapped to the reference
  • read_ani_std: Standard deviation of ANI of the reads mapped to the reference
  • read_ani_median: Median ANI of the reads mapped to the reference
  • bases_covered: Number of bases covered by the reference
  • max_covered_bases: Maximum number of bases covered in the reference
  • mean_covered_bases: Average number of bases covered in the reference
  • coverage_mean: Mean depth of the reference
  • coverage_mean_trunc: Mean depth of the reference after removing the 10% and 90% of the coverage values (default: TAD80 as calculated here)
  • coverage_mean_trunc_len: Length of the reference after being truncated by the TAD(X) values
  • coverage_covered_mean: Mean depth of the reference only counting covered bases
  • reference_length: Real reference length
  • bam_reference_length: Length reported by the BAM file
  • breadth: Breadth of coverage
  • exp_breadth: Expected breadth of coverage. Using the formula: expected_breadth = 1 - e^-coverage
  • breadth_exp_ratio: Ration between the observed and the expected depth of coverage
  • n_bins: Number of bins used to calculate the read coverage distribution
  • site_density: Site density of the reference
  • entropy: Entropy of the read coverage distribution
  • norm_entropy: Normalized entropy of the read coverage distribution
  • gini: Gini coefficient of the read coverage distribution
  • norm_gini: Normalized Gini coefficient of the read coverage distribution
  • c_v: Coefficient of variation of the coverage
  • d_i: Dispersion index
  • cov_evenness: Eveness of coverage as calculated here.
  • tax_abund_read: Counts estimated using the number of reads and normalized by the reference length.
  • tax_abund_aln: Counts estimated using the number of alignments and normalized by the reference length.
  • tax_abund_tad: Counts estimated using the estimated number of reads in the TAD region and normalized by the length of the TAD region
  • n_reads_tad: Number of reads estimated in the TAD region using the formula C = LN / G, where C stands for the TAD coverage, N for the length of the TAD region and L for the average read length mapped to the reference.

Applications and recommendations

One of the main applications of bam-filter is to reliably identify which potential organisms are present in a metagenomic ancient sample, and get relatively accurate taxonomic abundances, even when they are present in very low abundances. The resulting BAM file then can be used as input for metaDMG. We rely on several measures to discriminate between noise and a potential signal, analyzing the mapping results at two different levels:

• Is the observed breadth aligned with the expected one?
• Are the reads spread evenly across the reference or they are clumped in a few regions?

To assess the first question we use the concepts defined here. We estimate the ratio between the observed and expected breadth as a function of the coverage. If we get a breadth_exp_ratio close to 1, it means that the coverage we observed is close to the one we expect based on the calculated coverage. While this measure is already a strong indicator of a potential signal, we complement it with the metrics that measure the normalized positional entropy and the normalized distribution inequality (Gini coefficient) of the positions in the coverage. For details on how are calculated check here. These two metrics will help to identify those cases where we get a high breadth_exp_ratio but the coverage is not evenly distributed across the reference but instead is clumped in a few regions. One thing to be aware of is that we need to bin the reference to calculate those metrics. In our case, we use the ability of numpy.histogram to identify the numbers of bins, either using the Sturges or the Freedman-Diaconis rule. Finally, we use the knee point detection algorithm to identify the optimal values where to filter the Gini coefficient as a function of the positional entropy.