RNABioinformaticsPeer-reviewed journal article
Barnaba: Software for Analysis of Nucleic Acids Structures and Trajectories
Sandro BottaroiD, Giovanni BussiiD, Giovanni Pinamonti, Sabine ReißeriD, Wouter BoomsmaiD, Kresten Lindorff‐LarseniD
Abstract
RNA molecules are highly dynamic systems characterized by a complex interplay between sequence, structure, dynamics, and function. Molecular simulations can potentially provide powerful insights into the nature of these relationships. The analysis of structures and molecular trajectories of nucleic acids can be non-trivial because it requires processing very high-dimensional data that are not easy to visualize and interpret.\n\nHere we introduce Barnaba, a Python library aimed at facilitating the analysis of nucleic acids structures and molecular simulations. The software consists of a variety of analysis tools that allow the user to i) calculate distances between three-dimensional structures using different metrics, ii) back-calculate experimental data from three-dimensional structures, iii) perform cluster analysis and dimensionality reductions, iv) search three-dimensional motifs in PDB structures and trajectories and v) construct elastic network models (ENM) for nucleic acids and nucleic acids-protein complexes.\n\nIn addition, Barnaba makes it possible to calculate torsion angles, pucker conformations and to detect base-pairing/base-stacking interactions. Barnaba produces graphics that conveniently visualize both extended secondary structure and dynamics for a set of molecular conformations. The software is available as a command-line tool as well as a library, and supports a variety of 1le formats such as PDB, dcd and xtc 1les. Source code, documentation and examples are freely available at https://github.com/srnas/barnaba under GNU GPLv3 license.
- assay: protein structure
- research focus: software / tool
- research focus: dimensionality reduction
- research focus: clustering
- research focus: trajectory inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 93
- FWCI
- 1.51
- Field percentile
- top 17%
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