bam-readcount is a utility that runs on a BAM or CRAM file and generates low-level information about
sequencing data at specific nucleotide positions. Its outputs include observed bases,
readcounts, summarized mapping and base qualities, strandedness information,
mismatch counts, and position within the reads. (see "Output" section below)
Originally designed to help filter genomic mutation calls, the metrics bam-readcount outputs
are also useful as input for variant detection tools and for resolving ambiguity between
variant callers.
The latest release version of bam-readcount is available as a Docker image
on DockerHub
docker pull mgibio/bam-readcount
For details see the
docker-bam-readcount
repository.
Requires a C++ toolchain and cmake. For details see
BUILD.md.
git clone https://github.com/genome/bam-readcount
cd bam-readcount
mkdir build
cd build
cmake ..
make
# Executable is
bin/bam-readcount
Run with no arguments for command-line help:
$ bam-readcount
Usage: bam-readcount [OPTIONS] <bam_file> [region]
Generate metrics for bam_file at single nucleotide positions.
Example: bam-readcount -f ref.fa some.bam
Available options:
-h [ --help ] produce this message
-v [ --version ] output the version number
-q [ --min-mapping-quality ] arg (=0) minimum mapping quality of reads used
for counting.
-b [ --min-base-quality ] arg (=0) minimum base quality at a position to
use the read for counting.
-d [ --max-count ] arg (=10000000) max depth to avoid excessive memory
usage.
-l [ --site-list ] arg file containing a list of regions to
report readcounts within.
-f [ --reference-fasta ] arg reference sequence in the fasta format.
-D [ --print-individual-mapq ] arg report the mapping qualities as a comma
separated list.
-p [ --per-library ] report results by library.
-w [ --max-warnings ] arg maximum number of warnings of each type
to emit. -1 gives an unlimited number.
-i [ --insertion-centric ] generate indel centric readcounts.
Reads containing insertions will not be
included in per-base counts
The optional [region] should be in the same format as samtools:
chromosome:start-stop
The optional -l (--site-list) file should be tab-separated, no
header, one region per line:
chromosome start end
When using CRAM files as input, if a reference is specified with -f, it will override whatever is in
the CRAM header. Otherwise, the reference(s) encoded in the CRAM header or a lookup by
MD5 at ENA will be used.
Add bam-readcount counts to VCF
- VAtools allows you to add read-counts to VCF from modern variant callers. Additional details Create csv file
- brc-parser parser to convert bam-readcount output to comma seperated long format file.
Output is tab-separated with no header to STDOUT, one line per
position:
chr position reference_base depth base:count:avg_mapping_quality:avg_basequality:avg_se_mapping_quality:num_plus_strand:num_minus_strand:avg_pos_as_fraction:avg_num_mismatches_as_fraction:avg_sum_mismatch_qualities:num_q2_containing_reads:avg_distance_to_q2_start_in_q2_reads:avg_clipped_length:avg_distance_to_effective_3p_end ...
There is one set of :-separated fields for each reported base with
statistics on the set of reads containing that base:
| Field | Description |
|---|---|
| base | The base, eg C |
| count | Number of reads |
| avg_mapping_quality | Mean mapping quality |
| avg_basequality | Mean base quality |
| avg_se_mapping_quality | Mean single ended mapping quality |
| num_plus_strand | Number of reads on the plus/forward strand |
| num_minus_strand | Number of reads on the minus/reverse strand |
| avg_pos_as_fraction | Average position on the read as a fraction, calculated with respect to the length after clipping. This value is normalized to the center of the read: bases occurring strictly at the center of the read have a value of 1, those occurring strictly at the ends should approach a value of 0 |
| avg_num_mismatches_as_fraction | Average number of mismatches on these reads per base |
| avg_sum_mismatch_qualities | Average sum of the base qualities of mismatches in the reads |
| num_q2_containing_reads | Number of reads with q2 runs at the 3’ end |
| avg_distance_to_q2_start_in_q2_reads | Average distance of position (as fraction of unclipped read length) to the start of the q2 run |
| avg_clipped_length | Average clipped read length |
| avg_distance_to_effective_3p_end | Average distance to the 3’ prime end of the read (as fraction of unclipped read length) |
With the -p option, each output line will have a set of {}-delimited
results, one for each library:
chr position reference_base depth library_1_name { base:count:avg_mapping_quality:avg_basequality:avg_se_mapping_quality:num_plus_strand:num_minus_strand:avg_pos_as_fraction:avg_num_mismatches_as_fraction:avg_sum_mismatch_qualities:num_q2_containing_reads:avg_distance_to_q2_start_in_q2_reads:avg_clipped_length:avg_distance_to_effective_3p_end } ... library_N_name { base:count:avg_mapping_quality:avg_basequality:avg_se_mapping_quality:num_plus_strand:num_minus_strand:avg_pos_as_fraction:avg_num_mismatches_as_fraction:avg_sum_mismatch_qualities:num_q2_containing_reads:avg_distance_to_q2_start_in_q2_reads:avg_clipped_length:avg_distance_to_effective_3p_end }
For those who learn best by example, a brief tutorial is available here that uses bam-readcount to identify the Omicron SARS-CoV-2 variant of concern from raw sequence data.
For support, please search
bam-readcount on
Biostars as many of the most frequently asked
questions about bam-readcount have been answered there. For problems not addressed there,
please open an github issue or make a BioStar post.
We welcome contributions! See Contributing for more details