PDBImages: A Command Line Tool for Automated Macromolecular Structure Visualization

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Bioinformatics Pub Date : 2023-12-12 DOI:10.1093/bioinformatics/btad744

Adam Midlik, Sreenath Nair, Stephen Anyango, Mandar Deshpande, David Sehnal, Mihaly Varadi, Sameer Velankar

引用次数: 0

Abstract

Summary PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics. Availability and Implementation PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images.

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PDBImages：自动化大分子结构可视化的命令行工具

摘要 PDBImages 是一个创新的开源 Node.js 软件包，它利用了流行的大分子结构可视化软件 Mol* 的强大功能。PDBImages 专为科学界设计，提供了一种为 PDB 和 AlphaFold DB 模型生成高质量图像的方法。PDBImages 能以无浏览器模式直接渲染图像并将其保存到文件中，这种独特的功能使其与众不同，为用户提供了简化、自动化的大分子结构可视化流程。在此，我们将详细介绍 PDBImages 的实现过程，列举其多种图像类型，并详细说明其用户友好型设置。这一功能强大的工具为研究人员可视化、分析和共享他们的工作开辟了新的途径，促进了对生物信息学的深入理解。可用性与实现 PDBImages 作为 npm 软件包可从 https://www.npmjs.com/package/pdb-images 获取。源代码可从 https://github.com/PDBeurope/pdb-images 获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.