Package: polyboost
PolyBoostDescriptionPolyBoostis a post-analysis tool for the batch processing output ofPolyPhen-2that replaces thenaive Bayes classifierwith an extreme gradient boostingXGBoostclassifier.(Note: I am not affiliated with the PolyPhen-2 group).CitationPolyBoost: An enhanced genomic variant classifier using extreme gradient boostingis currently under peer review. If, for some reason, you need to cite this in the meantime, please contact me.QuickStartYou will need to install PolyBoost and obtain thebatch mode outputfrom PolyPhen2 predictions (http://genetics.bwh.harvard.edu/pph2/bgi.shtml). An example of the batch mode output is found in polyphen2-example.txt in this repository.Install PolyBoost with into Python 3.7 using:pip3 install polyboostIf you are using Windows, xgboost (a dependency) probably cannot be installed from PyPI. If you get an error message, follow the instructions for installation of XGBoost below and try this command again.After installation, run PolyBoost as follows:python -m polyboost.polyboost [PolyPhen2 Output File]Where [PolyPhen2 Output File] is the path to thebatch modeoutput from PolyPhen-2. Make sure the PolyPhen2 input file is in your working directory (i.e. the directory you are running that command from).Example:python -m polyboost.polyboost polyphen2-example.txtOn some systems with multiple python distributions, you may need to use python3 (or python3.7) instead of "python" to use the correct version of python.InstallationRequirementsPolyBoost requires Python 3.7, xgboost, numpy and scipy. PolyBoost will attempt to install all of these dependencies automatically.However, XGBoost maynotbe automatically installed because installation from PyPi does not work reliably on Windows at time of release. If an error occurs, install XGBoost (see below) before installing PolyBoost.Use of a Pythonvirtualenvis recommended, but not required.Python 3.7You will need to install Python 3.7 through standard methods.XGBoostInstallation of XGBoost is ostensibly as easy as:pip3 install xgboostI found, however, that I could not download this from the PyPi repository on Windows. In this case, you can download the XGBoost python wheel fromthis locationand install it like:pip3 install xgboost-{version}-{pythonversion}-{architecture}.whlExample: I installed XGBoost 0.90 for Python 3.7 (32-bit), so I used:pip3 install xgboost-0.90-cp37-cp37m-win32.whlI used the 32-bit module even though I am using 64-bit Windows because Python 3.7 was installed in 32 bit mode on my computer.If you have difficulty, detailed instructions for installing XGBoost for your platform can be found here:https://xgboost.readthedocs.io/en/latest/build.htmlPolyBoostInstall PolyBoost with:pip3 install polyboostNumpy and ScipyYou shouldnotneed to install numpy and scipy manually, but you can do so with:pip3 install numpy scipyOptionsNumber of Threads (--threads)You can specify the number of threads to run predictions. You must choose between 1 and 16 threads. If you make no selection, the default is to use 4 threads.Example using 8 threads:python -m polyboost.polyboost polyphen2-example.txt --threads 8Threshold (--threshold)You can manually choose a threshold between binary classification of "benign" and "damaging". The default choice is 0.5660484. This default was determined during classifier development by maximizing the Youden index (sensitivity + specificity - 1) of the receiver operating characteristic (ROC) curve on an external validation dataset.Example using a threshold value of 0.25:python3 -m polyboost.polyboost polyphen2-example.txt --threshold 0.25Output (--out)By default, PolyBoost outputs to the console (standard output). You can optionally output to a file using --out.Example redirecting to output.txtpython -m polyboost.polyboost polyphen2-example.txt --out output.txtOutput Exampleo_acc   o_pos   o_aa1   o_aa2   polyboost_score     polyboost_prediction
P26439  186     P       L       0.35185128          benign
P26439  205     L       P       0.09412336          benign
P26439  213     S       G       0.37042004          benign
P26439  216     K       E       0.60328233          damaging
P26439  222     P       H       0.06907171          benign
P26439  222     P       Q       0.39627028          benign
P26439  222     P       T       0.20633507          benign
P26439  236     L       S       0.7706197           damaging
P26439  245     A       P       0.17939752          benign
P26439  253     Y       N       0.044733346         benign
P26439  254     Y       D       0.2756629           benign
P26439  259     T       M       0.027224064         benignQuestions?Please e-mail me with questions. I will do my best to respond.