{"_id":"58f5d59402c293230028f0a2","__v":0,"user":"5767bc73bb15f40e00a28777","version":{"_id":"55faf11ba62ba1170021a9aa","project":"55faf11ba62ba1170021a9a7","__v":37,"createdAt":"2015-09-17T16:58:03.490Z","releaseDate":"2015-09-17T16:58:03.490Z","categories":["55faf11ca62ba1170021a9ab","55faf8f4d0e22017005b8272","55faf91aa62ba1170021a9b5","55faf929a8a7770d00c2c0bd","55faf932a8a7770d00c2c0bf","55faf94b17b9d00d00969f47","55faf958d0e22017005b8274","55faf95fa8a7770d00c2c0c0","55faf96917b9d00d00969f48","55faf970a8a7770d00c2c0c1","55faf98c825d5f19001fa3a6","55faf99aa62ba1170021a9b8","55faf99fa62ba1170021a9b9","55faf9aa17b9d00d00969f49","55faf9b6a8a7770d00c2c0c3","55faf9bda62ba1170021a9ba","5604570090ee490d00440551","5637e8b2fbe1c50d008cb078","5649bb624fa1460d00780add","5671974d1b6b730d008b4823","5671979d60c8e70d006c9760","568e8eef70ca1f0d0035808e","56d0a2081ecc471500f1795e","56d4a0adde40c70b00823ea3","56d96b03dd90610b00270849","56fbb83d8f21c817002af880","573c811bee2b3b2200422be1","576bc92afb62dd20001cda85","5771811e27a5c20e00030dcd","5785191af3a10c0e009b75b0","57bdf84d5d48411900cd8dc0","57ff5c5dc135231700aed806","5804caf792398f0f00e77521","58458b4fba4f1c0f009692bb","586d3c287c6b5b2300c05055","58ef66d88646742f009a0216","58f5d52d7891630f00fe4e77"],"is_deprecated":false,"is_hidden":false,"is_beta":true,"is_stable":true,"codename":"","version_clean":"1.0.0","version":"1.0"},"category":{"_id":"58f5d52d7891630f00fe4e77","project":"55faf11ba62ba1170021a9a7","version":"55faf11ba62ba1170021a9aa","__v":0,"sync":{"url":"","isSync":false},"reference":false,"createdAt":"2017-04-18T08:58:21.978Z","from_sync":false,"order":31,"slug":"data-cruncher","title":"DATA CRUNCHER"},"parentDoc":null,"project":"55faf11ba62ba1170021a9a7","updates":[],"next":{"pages":[],"description":""},"createdAt":"2017-04-18T09:00:04.539Z","link_external":false,"link_url":"","githubsync":"","sync_unique":"","hidden":false,"api":{"results":{"codes":[]},"settings":"","auth":"required","params":[],"url":""},"isReference":false,"order":7,"body":"At the moment, Data Cruncher offers a set of predefined libraries curated by Seven Bridges bioinformaticians, which are automatically available every time the analysis is started. The libraries are installed using the following package managers:\n\n* **apt-get** (Linux libraries)\n* **pip** (Python libraries)\n* **conda** (Python and R libraries)\n* **bioconductor** (R libraries)\n\nHere is a list of libraries that are installed by default, classified by package manager:\n\n* **conda** (Python2 \\ Python3)\n * nomkl, ipywidgets, pandas, numexpr, matplotlib, scipy, seaborn, scikit-learn, scikit-image, sympy, cython, patsy, statsmodels, cloudpickle, dill, numba, bokeh, sqlalchemy, hdf5, h5py, vincent, beautifulsoup4, xlrd, bioconda, path.py, biopython, pymongo, cytoolz, pysam, pyvcf\n* **pip** (Python2 \\ Python3)\n * sbgsdk, sevenbridges-python, cigar\n* **conda** (R)\n * rpy2, r-base, r-irkernel, r-plyr, r-devtools, r-tidyverse, r-shiny, r-rmarkdown, r-forecast, r-rsqlite, r-reshape2, r-nycflights13, r-caret, r-rcurl, r-crayon, r-randomforest\n* **bioconductor** (R)\n * sevenbridges-r\n\nYou can also install libraries directly from the notebook and use them during the execution of your analysis. However, unlike default libraries, libraries installed in that way will not be automatically available next time the analysis is started. Future releases of Data Cruncher will allow you to define additional libraries during the analysis set up stage, and those libraries will also be available automatically with each run of the analysis.","excerpt":"","slug":"about-libraries-in-a-data-cruncher-analysis","type":"basic","title":"About libraries in a Data Cruncher analysis"}

About libraries in a Data Cruncher analysis


At the moment, Data Cruncher offers a set of predefined libraries curated by Seven Bridges bioinformaticians, which are automatically available every time the analysis is started. The libraries are installed using the following package managers: * **apt-get** (Linux libraries) * **pip** (Python libraries) * **conda** (Python and R libraries) * **bioconductor** (R libraries) Here is a list of libraries that are installed by default, classified by package manager: * **conda** (Python2 \ Python3) * nomkl, ipywidgets, pandas, numexpr, matplotlib, scipy, seaborn, scikit-learn, scikit-image, sympy, cython, patsy, statsmodels, cloudpickle, dill, numba, bokeh, sqlalchemy, hdf5, h5py, vincent, beautifulsoup4, xlrd, bioconda, path.py, biopython, pymongo, cytoolz, pysam, pyvcf * **pip** (Python2 \ Python3) * sbgsdk, sevenbridges-python, cigar * **conda** (R) * rpy2, r-base, r-irkernel, r-plyr, r-devtools, r-tidyverse, r-shiny, r-rmarkdown, r-forecast, r-rsqlite, r-reshape2, r-nycflights13, r-caret, r-rcurl, r-crayon, r-randomforest * **bioconductor** (R) * sevenbridges-r You can also install libraries directly from the notebook and use them during the execution of your analysis. However, unlike default libraries, libraries installed in that way will not be automatically available next time the analysis is started. Future releases of Data Cruncher will allow you to define additional libraries during the analysis set up stage, and those libraries will also be available automatically with each run of the analysis.