diff --git a/README.rst b/README.rst index 4ddea58..a19c837 100644 --- a/README.rst +++ b/README.rst @@ -164,6 +164,12 @@ Re-label events with probability >= 0.3 as PASS:: dysgu filter --pass-prob 0.3 input.vcf > output.vcf +Use normal bams to filter common/germline structural variants:: + + dysgu filter input.vcf normal.bam > output.vcf + dysgu filter input.vcf normals/*.bam > output.vcf + dysgu filter input.vcf list_of_normals.txt > output.vcf + ➕ Merging SVs -------------- @@ -201,6 +207,7 @@ cohort SVs can be merged using `dysgu merge`, before filtering to get unique SVs dysgu merge *.vcf > merged.vcf dysgu filter --normal-vcf merged.vcf sample1.vcf *.bam > sample1_unique.vcf + dysgu filter --normal-vcf merged.vcf sample1.vcf list_of_normals.txt > sample1_unique.vcf Here, sample1.vcf and merged.vcf can contain multiple samples, although if sample1.vcf is multi-sample, you must provide '--target-sample' to indicate which sample to filter. The output sample1_somatic.vcf will be a single sample vcf containing unique SVs. diff --git a/dysgu/main.py b/dysgu/main.py index 6714c90..bf328ab 100644 --- a/dysgu/main.py +++ b/dysgu/main.py @@ -245,7 +245,7 @@ def run_pipeline(ctx, **kwargs): dest = os.path.expanduser(kwargs["working_directory"]) logging.info(f"Destination: {dest}") bname = os.path.splitext(os.path.basename(kwargs["bam"]))[0] - tmp_file_name = f"{dest}/{bname if bname != '-' else dest}.{pfix}.bam" + tmp_file_name = f"{dest}/{bname if bname != '-' else os.path.basename(kwargs['working_directory'])}.{pfix}.bam" ctx.obj["output"] = tmp_file_name ctx.obj["reads"] = "None" max_cov_value = sv2bam.process(ctx.obj) diff --git a/setup.py b/setup.py index 1fa3e74..1e87c30 100644 --- a/setup.py +++ b/setup.py @@ -164,7 +164,7 @@ def get_extra_args(): url="https://github.com/kcleal/dysgu", description="Structural variant calling", license="MIT", - version='1.6.1', + version='1.6.2', python_requires='>=3.7', install_requires=[ # runtime requires 'setuptools>=63.0',