feat: bazam wrapper (#580)

<!-- Ensure that the PR title follows conventional commit style (<type>:
<description>)-->
<!-- Possible types are here:
https://github.com/commitizen/conventional-commit-types/blob/master/index.json
-->

### Description

<!-- Add a description of your PR here-->

### QC
<!-- Make sure that you can tick the boxes below. -->

* [x] I confirm that:

For all wrappers added by this PR, 

* there is a test case which covers any introduced changes,
* `input:` and `output:` file paths in the resulting rule can be changed
arbitrarily,
* either the wrapper can only use a single core, or the example rule
contains a `threads: x` statement with `x` being a reasonable default,
* rule names in the test case are in
[snake_case](https://en.wikipedia.org/wiki/Snake_case) and somehow tell
what the rule is about or match the tools purpose or name (e.g.,
`map_reads` for a step that maps reads),
* all `environment.yaml` specifications follow [the respective best
practices](https://stackoverflow.com/a/64594513/2352071),
* wherever possible, command line arguments are inferred and set
automatically (e.g. based on file extensions in `input:` or `output:`),
* all fields of the example rules in the `Snakefile`s and their entries
are explained via comments (`input:`/`output:`/`params:` etc.),
* `stderr` and/or `stdout` are logged correctly (`log:`), depending on
the wrapped tool,
* temporary files are either written to a unique hidden folder in the
working directory, or (better) stored where the Python function
`tempfile.gettempdir()` points to (see
[here](https://docs.python.org/3/library/tempfile.html#tempfile.gettempdir);
this also means that using any Python `tempfile` default behavior
works),
* the `meta.yaml` contains a link to the documentation of the respective
tool or command,
* `Snakefile`s pass the linting (`snakemake --lint`),
* `Snakefile`s are formatted with
[snakefmt](https://github.com/snakemake/snakefmt),
* Python wrapper scripts are formatted with
[black](https://black.readthedocs.io).
* Conda environments use a minimal amount of channels, in recommended
ordering. E.g. for bioconda, use (conda-forge, bioconda, nodefaults, as
conda-forge should have highest priority and defaults channels are
usually not needed because most packages are in conda-forge nowadays).

bio/bazam/environment.yaml

@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+  - bioconda
+  - nodefaults
+dependencies:
+  - bazam =1.0
+  - snakemake-wrapper-utils ==0.5

bio/bazam/meta.yaml

@@ -0,0 +1,10 @@
+name: "bazam"
+description: Bazam is a smarter way to realign reads from one genome to another. If you've tried to use Picard SAMtoFASTQ or samtools bam2fq before and ended up unsatisfied with complicated, long running inefficient pipelines, bazam might be what you wanted. Bazam will output FASTQ in a form that can stream directly into common aligners such as BWA or Bowtie2, so that you can quickly and easily realign reads without extraction to any intermediate format. Bazam can target a specific region of the genome, specified as a region or a gene name if you prefer.
+url: https://github.com/ssadedin/bazam
+authors:
+  - Christopher Schröder
+input:
+  - BAM/CRAM file
+  - reference genome
+output:
+  - fastq file

bio/bazam/test/Snakefile

@@ -0,0 +1,28 @@
+rule bazam_interleaved:
+    input:
+        bam="mapped/{sample}.bam",
+        bai="mapped/{sample}.bam.bai",
+    output:
+        reads="results/reads/{sample}.fastq.gz",
+    resources:
+        mem_mb=12000,
+    log:
+        "logs/bazam/{sample}.log",
+    wrapper:
+        "master/bio/bazam"
+
+
+rule bazam_separated:
+    input:
+        bam="mapped/{sample}.cram",
+        bai="mapped/{sample}.cram.crai",
+        reference="genome.fasta",
+    output:
+        r1="results/reads/{sample}.r1.fastq.gz",
+        r2="results/reads/{sample}.r2.fastq.gz",
+    resources:
+        mem_mb=12000,
+    log:
+        "logs/bazam/{sample}.log",
+    wrapper:
+        "master/bio/bazam"

bio/bazam/test/genome.fasta

@@ -0,0 +1,2 @@
+>Sheila
+GCTAGCTCAGAAAAAAAAAA

bio/bazam/wrapper.py

@@ -0,0 +1,34 @@
+__author__ = "Christopher Schröder"
+__copyright__ = "Copyright 2022, Christopher Schröder"
+__email__ = "christopher.schroeder@tu-dortmund.de"
+__license__ = "MIT"
+
+from snakemake.shell import shell
+from snakemake_wrapper_utils.java import get_java_opts
+
+java_opts = get_java_opts(snakemake)
+
+log = snakemake.log_fmt_shell(stdout=False, stderr=True)
+bam = snakemake.input.bam
+
+# Extra parameters default value is an empty string
+extra = snakemake.params.get("extra", "")
+
+if bam.endswith(".cram"):
+    if not (reference := snakemake.input.get("reference", "")):
+        raise ValueError(
+            "input 'reference' is required when working with CRAM input files"
+        )
+    reference_cmd = f"-Dsamjdk.reference_fasta={reference}"
+else:
+    reference_cmd = ""
+
+# Extract arguments.
+if reads := snakemake.output.get("reads", ""):
+    out_cmd = f"-o {reads}"
+elif (r1 := snakemake.output.get("r1", "")) and (r2 := snakemake.output.get("r2", "")):
+    out_cmd = f"-r1 {r1} -r2 {r2}"
+else:
+    raise ValueError("either 'reads' or 'r1' and 'r2' must be specified in output")
+
+shell("(bazam {java_opts} {reference_cmd} {extra} -bam {bam} {out_cmd}) {log}")

test.py

@@ -4853,4 +4853,18 @@ def test_calc_consensus_reads():
     run(
         "meta/bio/calc_consensus_reads/",
         ["snakemake", "--cores", "1", "--use-conda", "-F", "results/consensus/sampleA.bam"],
+    )
+
+@skip_if_not_modified
+def test_bazam_interleaved():
+    run(
+        "bio/bazam",
+        ["snakemake", "--cores", "1", "--use-conda", "-F", "results/reads/a.fastq.gz"],
+    )
+
+@skip_if_not_modified
+def test_bazam_separated():
+    run(
+        "bio/bazam",
+        ["snakemake", "--cores", "1", "--use-conda", "-F", "results/reads/a.r1.fastq.gz"],
     )