Matplotlib

Manufactured by Anaconda

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It provides a wide range of tools for plotting data, including line plots, scatter plots, bar charts, histograms, and more. Matplotlib is a powerful and flexible data visualization tool that is widely used in the scientific and data analysis communities.

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Lab products found in correlation

Scikit learn, by Anaconda (2 mentions) Pandas, by Anaconda (1 mentions) Python, by Anaconda (1 mentions)

2 protocols using matplotlib

Software Tools for Data Analysis and Visualization

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The codes used in our work are based on standard, publicly available software packages. Pre- and post-processing data and the generation of figures were performed using the Anaconda (v4.10) packages Matplotlib v3.4, Scikit Learn v1.0, Scipy v1.5 and NetworkX v2.3. Figures were then assembled using Adobe Illustrator. Chimera (v1.13)^{69 (link)} was used for visualization of the 3D structures generated.

Boninsegna L., Yildirim A., Polles G., Zhan Y., Quinodoz S.A., Finn E.H., Guttman M., Zhou X.J, & Alber F. (2022). Integrative genome modeling platform reveals essentiality of rare contact events in 3D genome organizations. Nature Methods, 19(8), 938-949.

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Comprehensive Bioinformatics Analysis Pipeline

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The analysis was performed in an Anaconda Python environment with the following packages: Python (version 3.9), BioPython (version 1.79), Matplotlib (version 3.5.1), Plotly (version 4.14.3), Scikit-learn (version 1.0.2), Pandas (version 1.4.1), Pyteomics (version 4.5.3), Matplotlib-venn (version 0.11.6), Seaborn (version 0.11.2), UMAP-learn (version 0.5.2), HDBSCAN (version 0.8.28), and xlrd (version 2.0.1), Logomaker v0.8. An R environment within Anaconda was also used, consisting of R-essentials (version 4.1), R-base (version 4.1.2), and R-devtools (version 2.4.3). These tools and environments enabled data processing and creation of visualizations.

Bedran G., Polasky D.A., Hsiao Y., Yu F., da Veiga Leprevost F., Alfaro J.A., Cieslik M, & Nesvizhskii A.I. (2023). Unraveling the glycosylated immunopeptidome with HLA-Glyco. Nature Communications, 14, 3461.

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