Improving productivity while managing extensive molecular dynamics simulation data
- Published
- Accepted
- Subject Areas
- Bioinformatics, Human-Computer Interaction
- Keywords
- molecular dynamics, extended simulations, managing data, python, gui
- Copyright
- © 2018 Bandyopadhyay
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Improving productivity while managing extensive molecular dynamics simulation data. PeerJ Preprints 6:e26920v1 https://doi.org/10.7287/peerj.preprints.26920v1
Abstract
This paper discusses the difficulties experienced by bioinformaticians while working with extensive data generated from extended molecular dynamics simulations. For better experimental analysis, it often becomes crucial to conduct simulations up to extended periods of time. When with limited resources, running a complete simulation up to a desired length of time can become quite difficult to be performed at one go. So, a new approach is proposed to simplify handling such data for better productivity.
Author Comment
This is a method article proposed to make it easier for bioinformaticians to effectively manage multiple amounts of data files generated when performing prolonged molecular dynamics simulations as a series of separate and sequential tasks.
Supplemental Information
Source code of proposed tool
Includes both Python 2.7 and Python 3 versions along-with download link to test data
TMA.py written in Python 2.7
TMA_PY3.py written in Python 3