This repository is dedicated to listing and discussing various R packages and Python modules that are particularly useful in the fields of Genomics and Data Visualization. Whether you are conducting genomic data analysis, visualizing complex datasets, or both, this curated list aims to provide you with the tools necessary to achieve your goals.
Bioconductor offers tools for the analysis and comprehension of high-throughput genomic data. Bioconductor is based on the R statistical programming language, and is open source and open development. It has a wide variety of packages available for genomics data analysis.
An R package designed to represent and manipulate genomic intervals and data associated with those intervals.
DESeq2 is a package for differential gene expression analysis based on the negative binomial distribution.
Biopython is a set of freely available tools for biological computation written in Python by an international team of developers.
PySAM provides an interface for reading, manipulating, and writing genomic data sets in SAM/BAM format.
A system for declaratively creating graphics, based on The Grammar of Graphics. ggplot2 provides a powerful way of making complex and beautiful graphics.
An R package that creates interactive web graphics via the open source JavaScript graphing library plotly.js.
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Feel free to contribute by suggesting other tools and packages that are useful for genomics and data visualization. Your contributions are welcome!