Department of Chemistry and Biochemistry, Augustana University, Sioux Falls, South Dakota, USA.
Biochem Mol Biol Educ. 2022 Sep;50(5):431-436. doi: 10.1002/bmb.21621. Epub 2022 Apr 11.
This article reports a workshop from the 2021 IUBMB/ASBMB Teaching Science with Big Data conference held virtually in June 2021 where participants learned to explore and visualize large quantities of protein PBD data using Jupyter notebooks and the Python programming language. This activity instructs participants using Jupyter notebooks, Python functions, loading data with Python, and visualize data using the matplotlib and seaborn Python plotting libraries. It also allows participants to explore large quantities of data to discover trends such amino acid abundance, dihedral angles patterns, and secondary protein structure trends. All files used in this activity, including data files, Jupyter notebooks, and completed Jupyter notebooks, are freely available at https://github.com/weisscharlesj/BiopythonRamachandran under the CC BY-NC-SA 4.0 Creative Commons license.
本文报道了 2021 年 IUBMB/ASBMB 教学科学大数据会议的一个研讨会，该研讨会是在 2021 年 6 月虚拟举行的，参与者们学习了如何使用 Jupyter 笔记本和 Python 编程语言探索和可视化大量蛋白质 PBD 数据。该活动通过 Jupyter 笔记本、Python 函数、使用 Python 加载数据以及使用 matplotlib 和 seaborn Python 绘图库来指导参与者。它还允许参与者探索大量数据，以发现趋势，如氨基酸丰度、二面角模式和二级蛋白质结构趋势。本活动中使用的所有文件，包括数据文件、Jupyter 笔记本和已完成的 Jupyter 笔记本，均可在 https://github.com/weisscharlesj/BiopythonRamachandran 上免费获得，遵循 CC BY-NC-SA 4.0 知识共享许可协议。
