mmtfPyspark is a python package that provides APIs and sample applications for distributed analysis and scalable mining of 3D biomacromolecular structures, such as the Protein Data Bank (PDB) archive. mmtfPyspark uses Big Data technologies to enable high-performance parallel processing of macromolecular structures. mmtfPyspark use the following technology stack:
- Apache Spark a fast and general engine for large-scale distributed data processing.
- MMTF the Macromolecular Transmission Format for compact data storage, transmission and high-performance parsing
- Hadoop Sequence File a Big Data file format for parallel I/O
- Apache Parquet a columnar data format to store dataframes
This project is under development.
Run mmtf-pyspark in your Web Browser
The Jupyter Notebooks in this repository can be run in your web browser using two freely available servers: Binder and CyVerse/VICE. Click on the buttons below to launch Jupyter Lab. It may take several minutes for Jupyter Lab to launch.
Navigate to the demos directory to run any of the example notebooks.
Binder is an experimental platform for reproducible research developed by Project Jupyter. Learn more about Binder. There are specific links for each notebook below, however, once Jupyter Lab is launched, navigate to any of the other notebooks using the Jupyter Lab file panel.
CyVerse (experimental version)
The new VICE (Visual Interactive Computing Environment) in the CyVerse Discovery Environment enables users to run Jupyter Lab in a production environment. To use VICE, sign up for a free CyVerse account.
The VICE environment supports large-scale analyses. Users can upload and download files, and save and share results of their analyses in their user accounts (up to 100GB of data). The environment is preloaded with a local copy of the entire Protein Data Bank (~148,000 structures).
We strongly recommend that you have anaconda and we require at least python 3.6 installed. To check your python version:
If Anaconda is installed, and if you have python 3.6, the above command should return:
Python 3.6.4 :: Anaconda, Inc.
mmtfPyspark and dependencies
Since mmtfPyspark uses parallel computing to ensure high-performance, it requires additional dependencies such as Apache Spark. Therefore, please read follow the installation instructions for your OS carefully:
Hadoop Sequence Files
This project uses the PDB archive in the form of MMTF Hadoop Sequence File. The files can be downloaded by:
curl -O https://mmtf.rcsb.org/v1.0/hadoopfiles/full.tar tar -xvf full.tar curl -O https://mmtf.rcsb.org/v1.0/hadoopfiles/reduced.tar tar -xvf reduced.tar
For Mac and Linux, the Hadoop sequence files can be downloaded and saved as environmental variables by running the following command:
curl https://raw.githubusercontent.com/sbl-sdsc/mmtf-pyspark/master/bin/download_mmtf_files.sh -o download_mmtf_files.sh . ./download_mmtf_files.sh
How to Cite this Work
Bradley AR, Rose AS, Pavelka A, Valasatava Y, Duarte JM, Prlić A, Rose PW (2017) MMTF - an efficient file format for the transmission, visualization, and analysis of macromolecular structures. PLOS Computational Biology 13(6): e1005575. doi: 10.1371/journal.pcbi.1005575
Valasatava Y, Bradley AR, Rose AS, Duarte JM, Prlić A, Rose PW (2017) Towards an efficient compression of 3D coordinates of macromolecular structures. PLOS ONE 12(3): e0174846. doi: 10.1371/journal.pone.01748464
Rose AS, Bradley AR, Valasatava Y, Duarte JM, Prlić A, Rose PW (2018) NGL viewer: web-based molecular graphics for large complexes, Bioinformatics, bty419. doi: 10.1093/bioinformatics/bty419
Rose AS, Bradley AR, Valasatava Y, Duarte JM, Prlić A, Rose PW (2016) Web-based molecular graphics for large complexes. In Proceedings of the 21st International Conference on Web3D Technology (Web3D '16). ACM, New York, NY, USA, 185-186. doi: 10.1145/2945292.2945324
Project Jupyter, et al. (2018) Binder 2.0 - Reproducible, Interactive, Sharable Environments for Science at Scale. Proceedings of the 17th Python in Science Conference. 2018. doi: 10.25080/Majora-4af1f417-011
Merchant N, Lyons E, Goff S, Vaughn M, Ware D, Micklos D, et al. (2016) The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences. PLoS Biol 14(1): e1002342. doi: 10.1371/journal.pbio.1002342
Rego N, Koes, D (2015) 3Dmol.js: molecular visualization with WebGL, Bioinformatics 31, 1322–1324. doi: 10.1093/bioinformatics/btu829
The MMTF project (Compressive Structural BioInformatics: High Efficiency 3D Structure Compression) is supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The CyVerse project is supported by the National Science Foundation under Award Numbers DBI-0735191, DBI-1265383, and DBI-1743442. URL: www.cyverse.org