0.1555.1

installation, documentation, conda recipe,  and GroupedAnalysis updates

AmpliconSuite-pipeline.py

@@ -14,7 +14,7 @@
 
 from paalib import check_reference, cnv_prefilter
 
-__version__ = "0.1546.1"
+__version__ = "0.1555.1"
 
 PY3_PATH = "python3"  # updated by command-line arg if specified
 metadata_dict = {}  # stores the run metadata (bioinformatic metadata)
@@ -146,7 +146,7 @@ def run_cnvkit(ckpy_path, nthreads, outdir, bamfile, seg_meth='cbs', normal=None
         sys.exit(1)
 
     metadata_dict["cnvkit_cmd"] = metadata_dict["cnvkit_cmd"] + cmd
-    logging.info("\nCleaning up temporary files")
+    logging.info("\nCleaning up temporary CNVkit files")
     cmd = "rm -f {}/*tmp.bed {}/*.cnn {}/*target.bed {}/*.bintest.cns".format(outdir, outdir, outdir, outdir)
     logging.info(cmd)
     call(cmd, shell=True)
@@ -741,7 +741,7 @@ def detect_run_failure(align_stderr_file, AA_outdir, sname, AC_outdir):
     try:
         with open(AA_REPO + args.ref + "/last_updated.txt", 'r') as file:
             datestring = file.read()
-            logging.info("Data repo constructed on " + datestring)
+            logging.info(args.ref + " data repo constructed on " + datestring)
 
     except FileNotFoundError:
         logging.warning("Data repo appears to be out of date. Please update your data repo!\n")
@@ -768,7 +768,7 @@ def detect_run_failure(align_stderr_file, AA_outdir, sname, AC_outdir):
 
         determined_ref = check_reference.check_ref(args.sorted_bam, faidict, args.samtools_path)
         if not determined_ref and not args.ref:
-            logging.error("Please make sure AA data repo is populated.")
+            logging.error("Could not determine ref build. Please make sure AA data repo is populated.")
             sys.exit(1)
 
         elif not args.ref:

README.md

@@ -64,11 +64,7 @@ Can be used on most modern Unix systems (e.g. Ubuntu 18.04+, CentOS 7+, macOS).
     rm [reference_build.tar.gz]
     ```
 
-3. Lastly, obtain the Mosek optimization tool license (free for academic use) and place it in `$HOME/mosek/`. AA will not work without it.
-
-
-4. (Optional) Add the Arial font. These usually already present on macOS but not Linux.
-   * See the custom installation document in the documentation folder. 
+3. Lastly, obtain the Mosek optimization tool license (free for academic use) and place it in `$HOME/mosek/`. AA will not work without it. 
 
 
 ### Option D: Singularity & Docker images 

conda-recipe/README.md

@@ -0,0 +1,11 @@
+### Standalone AmpliconSuite-pipeline Conda recipe
+
+This version of the conda recipe is for a standalone build and install process. If you want to build this without using
+bioconda to fetch the actual bioconda recipe, you can do the following 
+
+```bash
+conda build AmpliconSuite-pipeline/conda-recipe/ 
+conda create -n "ampsuite" python>=3.8.0
+conda activate ampsuite
+conda install -n ampsuite -c local -c mosek ampliconsuite mosek
+```

conda-recipe/build.sh

@@ -1,5 +1,7 @@
 #!/bin/bash
 
+set -ex
+
 # pull source code for AA and move scripts into a library
 TARGET="AmpliconArchitect"
 TARGET_VERSION="1.3.r5"
@@ -25,9 +27,5 @@ rm v${TARGET_VERSION}.zip
 # make the bin dir if it doesn't exist
 mkdir -p $PREFIX/bin
 
-# copy driver scripts
-cp AmpliconSuite-pipeline.py ${PREFIX}/bin/AmpliconSuite-pipeline.py
-cp GroupedAnalysisAmpSuite.py ${PREFIX}/bin/GroupedAnalysisAmpSuite.py
-
 # Python command to install the package.
 $PYTHON setup.py install --install-data aa_data_repo/ --single-version-externally-managed --record=record.txt

conda-recipe/meta.yaml

@@ -8,17 +8,16 @@ source:
 
 build:
     number: 0
-    skip: False
 
 requirements:
     build:
-        - python
+        - python >=3
         - setuptools
         - unzip
         - wget
 
     run:
-        - python
+        - python >=3
         - bwa
         - cnvkit
         - Flask

documentation/CUSTOM_INSTALL.md

@@ -42,7 +42,13 @@ AmpliconSuite-pipeline has been tested with Ubuntu (16.04 and above) and CentOS
 `python3`. It also requires `R` version >= 3.5, which is non-standard on Ubuntu 16.04/14.04.
 
 ## Getting `mscorefonts` onto your system.
-AmpliconArchitect figures will attempt to use the Arial font, and will fall back to the default `matplotlib` font. On macOS, Arial will likely already be present. 
-Install the `mscorefonts` package
-   - `conda install mscorefonts` or
-   - (Ubuntu) `sudo apt update && sudo apt install ttf-mscorefonts-installer`. Then do `sudo fc-cache -f -v` to rebuild the font cache.
+AmpliconArchitect figures will attempt to use the Arial font, and will fall back to the default `matplotlib` "Deja Vu Sans" font. On macOS, Arial will likely already be present. 
+Install the `mscorefonts` package one of two ways:
+
+a) First run `conda install mscorefonts` then launch python and do
+```python
+import matplotlib.font_manager
+matplotlib.font_manager._load_fontmanager(try_read_cache=False)
+```
+
+b) (Ubuntu) `sudo apt update && sudo apt install ttf-mscorefonts-installer`. Then do `sudo fc-cache -f -v` to rebuild the font cache.

setup.py

@@ -7,4 +7,5 @@
     packages=find_packages(exclude=['images', 'docker', 'singularity']),
     include_package_data=True,
     package_data={'': ['*.json', '*.sh'], 'ampliconclassifierlib': ['resources/*']},
+    scripts=['AmpliconSuite-pipeline.py', 'GroupedAnalysisAmpSuite.py'],
 )