At the moment, Data Cruncher offers a set of predefined libraries curated by Seven Bridges bioinformaticians, which are automatically available every time an analysis is started. The list of available libraries depends on the environment you are using (JupyterLab or RStudio) and the selected environment setup (set of preinstalled libraries that is available each time an analysis is started). Both of these settings are selected in the analysis creation wizard and cannot be changed once the analysis has been created.
JupyterLab
Depending on the purpose and objective of your JupyterLab analysis, you can select an environment setup that you find most suitable for the given analysis. The following table shows the available JupyterLab environment setups and some details about available tools and libraries in each of them:
Seven Bridges default
Here is a list of libraries that are installed by default:
- Python2 \ Python3:
- path.py, biopython, pymongo, cytoolz, pysam, pyvcf, ipywidgets, beautifulsoup4, sevenbridges-python, cigar, bioservices, intervaltree, appdirs, cssselect, bokeh, scikit-allel, cairo, lxml, cairosvg, rpy2
- R:
- r-ggfortify, r, r-stringi, r-pheatmap, r-gplots, bioconductor-ballgown, bioconductor-deseq2, bioconductor-metagenomeseq, bioconductor-biomformat, bioconductor-biocinstaller, sevenbridges-r, r-xml
The aforementioned libraries are installed on top of the libraries that are already available in datascience-notebook.
Machine learning - TensorFlow
This environment setup is optimized for machine learning and execution on GPU instances. It is based on the jupyter/tensorflow-notebook image (jupyter/scipy-notebook that includes popular packages from the scientific Python ecosystem, with the addition of popular Python deep learning libraries). Learn more about available libraries.
All available environment setups also contain sevenbridges-python and sevenbridges-r API libraries, as well as htop and openvpn as general-purpose tools. The libraries are installed using conda, as JupyterLab supports multiple programming languages and conda is a language-agnostic package manager. You can also install libraries directly from the notebook and use them during the execution of your analysis. For optimal performance and avoidance of potential conflicts, we recommend using conda when installing libraries within your analyses. However, unlike default libraries, libraries installed in that way will not be automatically available next time the analysis is started.
RStudio (beta)
If you select RStudio as the analysis environment, you can also select one of the available environment setups depending on the purpose of your analysis. This will help you optimize analysis setup and time to getting a fully-functional environment that suits your needs by having the needed libraries preinstalled in the selected environment setup. Here are the available options:
Seven Bridges default
This environment setup is based on the rstudio/verse image from The Rocker Project and contains tidyverse, devtools, tex and publishing-related packages. For more information about the image, please see its Docker Hub repository.
Here is a list of libraries that are installed by default:
- CRAN - BiocManager, ggfortify, pheatmap, gplots
- Bioconductor - ballgown, DESeq2, metagenomeSeq, biomformat, BiocInstaller
- sevenbridges-r
Machine learning
This environment setup is optimized for machine learning and execution on GPU instances. It is based on the rocker/ml-gpu image that is intended for machine learning and GPU-based computation in R. Learn more.
All available environment setups also contain the sevenbridges-r API library, as well as htop and openvpn as general-purpose tools.
Updated about a month ago