English

Abstractions, Algorithms and Data Structures for Structural Bioinformatics in PyCogent

Biomolecules 2018-10-01 v1 Data Structures and Algorithms Software Engineering

Abstract

To facilitate flexible and efficient structural bioinformatics analyses, new functionality for three-dimensional structure processing and analysis has been introduced into PyCogent -- a popular feature-rich framework for sequence-based bioinformatics, but one which has lacked equally powerful tools for handling stuctural/coordinate-based data. Extensible Python modules have been developed, which provide object-oriented abstractions (based on a hierarchical representation of macromolecules), efficient data structures (e.g. kD-trees), fast implementations of common algorithms (e.g. surface-area calculations), read/write support for Protein Data Bank-related file formats and wrappers for external command-line applications (e.g. Stride). Integration of this code into PyCogent is symbiotic, allowing sequence-based work to benefit from structure-derived data and, reciprocally, enabling structural studies to leverage PyCogent's versatile tools for phylogenetic and evolutionary analyses.

Keywords

Cite

@article{arxiv.1407.5218,
  title  = {Abstractions, Algorithms and Data Structures for Structural Bioinformatics in PyCogent},
  author = {Marcin Cieslik and Zygmunt Derewenda and Cameron Mura},
  journal= {arXiv preprint arXiv:1407.5218},
  year   = {2018}
}

Comments

36 pages, 4 figures (including supplemental information)

R2 v1 2026-06-22T05:08:09.162Z