{"id":24652624,"url":"https://github.com/JRaviLab/compbio-gists","last_synced_at":"2025-10-07T12:31:12.020Z","repository":{"id":83526312,"uuid":"181930303","full_name":"jananiravi/compbio-gists","owner":"jananiravi","description":"Computational Biology \u0026 Bioinformatics Resources","archived":false,"fork":false,"pushed_at":"2023-03-20T22:39:58.000Z","size":37,"stargazers_count":11,"open_issues_count":0,"forks_count":3,"subscribers_count":5,"default_branch":"main","last_synced_at":"2024-03-20T10:20:41.417Z","etag":null,"topics":["bioinformatics","comparative-genomics","computational-biology","data-science","gists","molecular-evolution","phylogeny","r","shell","transcriptomics"],"latest_commit_sha":null,"homepage":"","language":"Shell","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jananiravi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-04-17T16:34:54.000Z","updated_at":"2023-09-02T11:30:53.000Z","dependencies_parsed_at":null,"dependency_job_id":"5f4311ef-3590-44ff-9182-3c733fd5c5e4","html_url":"https://github.com/jananiravi/compbio-gists","commit_stats":{"total_commits":19,"total_committers":2,"mean_commits":9.5,"dds":"0.052631578947368474","last_synced_commit":"c6691e876302af837230322cd1402920f890e2a7"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jananiravi%2Fcompbio-gists","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jananiravi%2Fcompbio-gists/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jananiravi%2Fcompbio-gists/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jananiravi%2Fcompbio-gists/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jananiravi","download_url":"https://codeload.github.com/jananiravi/compbio-gists/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":235628032,"owners_count":19020535,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","comparative-genomics","computational-biology","data-science","gists","molecular-evolution","phylogeny","r","shell","transcriptomics"],"created_at":"2025-01-25T19:44:56.222Z","updated_at":"2025-10-07T12:31:06.742Z","avatar_url":"https://github.com/jananiravi.png","language":"Shell","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Computational Biology \u0026 Bioinformatics Resources\n_With programming resources on R, Python, Unix, Git, and Stats._\n_Other non-compbio gists will be [here](https://gist.github.com/jananiravi)!_\n\u003e NOTE: When the recommendation is an online course, we recommend the *FREE* version.\n\n## Contributors\n[Janani Ravi](https://github.com/jananiravi) \u0026 [Arjun Krishnan](https://github.com/krishnanlab)\n\n\u003e NOTE: _You can request gist on a particular topic by adding an [issue](https://github.com/jananiravi/compbio-gists/issues) outlining the details of the problem. Keywords of interest are in the repo description above._\n\n## Table of Contents\n* [Cheatsheets](#cheatsheets)\n* [Unix](#unix)\n* [R](#r)\n* [Python](#python)\n* [Probability \u0026 Statistics](#probability-and-statistics)\n* [Biology](#biology)\n\n## Cheatsheets\nFor R/RStudio, Git/GitHub, Markdown, Unix/vi, Slack, … \u003cbr\u003e\nhttps://github.com/jananiravi/cheatsheets \n\n## Unix\n* [Command-line Bootcamp](http://rik.smith-unna.com/command_line_bootcamp/)\n* [Command-line Guide](http://commandline.guide/) | Also interactive, just like the bootcamp.\n* [Linux Journey](https://linuxjourney.com)\n* A Unix workshop: [course materials](https://www.dropbox.com/s/1ltlyhtdbccymep/w1-files.zip?dl=0)\n    * Day1 - [Video](https://www.youtube.com/watch?v=liC5uM8czyo) \u0026 [Slides](https://www.dropbox.com/s/ggv7ijwateim7zt/day1_Unix.pdf?dl=0)\n    * Day2 - [Video](https://www.youtube.com/watch?v=ArbOG6YpakU) \u0026 [Slides](https://www.dropbox.com/s/xorsuvk1cugiyw8/day2_Unix.pdf?dl=0)\n    * Day3 - [Video](https://www.youtube.com/watch?v=PHmfgIuOMFQ) \u0026 [Slides](https://www.dropbox.com/s/88wu7svvfur8upw/day3_Unix.pdf?dl=0)\n* Command-line refresher from [Software Carpentry](http://swcarpentry.github.io/shell-novice/)\n\n## R\n### General introduction to R\n* [Swirl](http://swirlstats.com) ('R Programming' \u0026 'Data Analysis’ lessons)\n* [Programming with R](http://swcarpentry.github.io/r-novice-inflammation/)\n* [RStudio Education](https://education.rstudio.com/)\n* [Finding Your Way To R](https://education.rstudio.com/learn/) | [Beginners](https://education.rstudio.com/learn/beginner/)\n* [RStudio Essentials](https://resources.rstudio.com/)\n* [R Cheatsheets](https://www.rstudio.com/resources/cheatsheets/)\n\n#### Data Visualization\nA few useful resources to share along with the tidyverse/ggplot\n1. To pick the right kind of visualization, given your data type:\nhttps://www.data-to-viz.com/ \n2. Graph galleries w/ sample codes for R/python-newbies: \u003cbr\u003e\n[R Graph Gallery](https://www.r-graph-gallery.com/) | [Python Graph Gallery](https://python-graph-gallery.com/)\n3. [ggplot extension gallery](https://exts.ggplot2.tidyverse.org/gallery/) | https://github.com/ggplot2-exts/gallery \n\n### R for data science and machine learning\n* [Data Science Course in a Box](https://datasciencebox.org/) - Introductory data science course covering data acquisition and wrangling, exploratory data analysis, data visualization, inference, modeling, and effective communication of results (with tidyverse, R Markdown, and version control). The course also introduces interactive visualization and reporting, text analysis, and Bayesian inference.\n* [RStudio | The Essentials of Data Science](https://resources.rstudio.com/the-essentials-of-data-science)\n* [R for Reproducible Scientific Analysis](http://swcarpentry.github.io/r-novice-gapminder/)\n\n### eBooks for R\n* R for Data Science | R4DS | Hadley Wickham, Garrett Grolemund |\n[eBook](https://r4ds.had.co.nz/)\n* Hands-On Programming with R | HOPR | Garrett Grolemund |\n[eBook](https://rstudio-education.github.io/hopr/)\n* Happy Git and GitHub for the useR | Jenny Bryan |\n[eBook](https://happygitwithr.com/)\n* [Learning Statistics with R](https://learningstatisticswithr.com/) | Danielle Navarro |\n[eBook](https://learningstatisticswithr.com/book/)\n* Computational Genomics with R | Altuna Akalin |\n[eBook](http://compgenomr.github.io/book/) | _Work in progress_\n* R Programming for Data Science | Roger Peng |\n[eBook](https://leanpub.com/rprogramming)\n* R Graphics Cookbook | Winston Chang |\n[eBook](https://r-graphics.org/)\n\n## Python\n\n### General introduction to Python\n* [Learning Python the Hard Way](https://learnpythonthehardway.org/book/)\n* [Google Python Class](https://developers.google.com/edu/python/)\n    * [Videos to follow along](https://www.youtube.com/playlist?list=PLfZeRfzhgQzTMgwFVezQbnpc1ck0I6CQl)\n* Introduction to Interactive Programming in Python\n    * [Part 1](https://www.coursera.org/learn/interactive-python-1)\n    * [Part 2](https://www.coursera.org/learn/interactive-python-2)\n\n### Python for data science and machine learning\n* Courses to learn introductory computer science, programming, computational thinking, and data science  (video lectures + notes + assignments):\n    * [Introduction to Computer Science and Programming in Python](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/)\n    * [Introduction to Computational Thinking and Data Science](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/)\n    * [A Whirlwind Tour of Python](https://jakevdp.github.io/WhirlwindTourOfPython/): [PDF](http://www.oreilly.com/programming/free/files/a-whirlwind-tour-of-python.pdf) and [Jupyter Notebooks](https://github.com/jakevdp/WhirlwindTourOfPython)\n    * [Scipy Lecture Notes](http://www.scipy-lectures.org/) – Awesome document to learn numerics, science, and data with Python\n* Data Wrangling:\n   * [Data Wrangling in Python with Pandas - Kaggle](https://www.kaggle.com/learn/pandas)\n   * [Video series on data analysis with Pandas](https://www.dataschool.io/easier-data-analysis-with-pandas/) – Excellent set of short videos\n* Visualization:\n   * [Data Visualization with Python - Kaggle](https://www.kaggle.com/learn/data-visualisation)\n   * [Python Plotting for Exploratory Data Analysis](http://pythonplot.com/)\n* Machine Learning:\n   * [Introduction to ML in Python - Kaggle](https://www.kaggle.com/learn/machine-learning) (Checkout both Levels 1 \u0026 2)\n   * [Another intro to ML with scikit-learn](https://www.dataschool.io/machine-learning-with-scikit-learn/) – This one contains videos and accompanying JuPyter notebooks + blog posts.\n   * [A Quick Demo to ML with Scikit Learn Python Package](https://github.com/mmmayo13/scikit-learn-classifiers/blob/master/sklearn-classifiers-tutorial.ipynb) – A nice demo+tour of scikit learn.\n   * [Deep Learning with Python and TensorFlow - Kaggle](https://www.kaggle.com/learn/deep-learning)\n   * [Embeddings with Python and TensorFlow - Kaggle](https://www.kaggle.com/learn/embeddings) – Build deep learning models that handle sparse categorical variables\n   * [Machine Learning Explainability](https://www.kaggle.com/learn/machine-learning-explainability)\n* General mutli-topic resources:\n   * [A Step-by-step Guide to Python for Data Science](http://www.dataschool.io/launch-your-data-science-career-with-python/)\n   * Always checkout the latest PyCon Conference tutorials and talks, almost all of which are available online. [For e.g., here's a list from PyCon 2017](https://krishnanlab.slack.com/files/arjunkrish/F5MEK7GAK/Python_Videos_of_Interest_to_Lab).\n\n### Probability and statistics\n* [Think Stats](https://greenteapress.com/wp/think-stats-2e/) (book + code + solutions; for Python programmers).\n* [Learning statistics with R](https://learningstatisticswithr.com/book/) (book + code + solutions; for R programmers).\n* [Points of Significance](https://www.nature.com/collections/qghhqm/pointsofsignificance) - an awesome collection of short articles on a variety of topics in statistical data analysis.\n* [OpenIntro to Probablity and Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os)\n\n#### Statistical learning\n\u003e A great resource (book + videos + slides + exercises + example code + solutions) for simultaneously learning both statistical learning and R. [_Statistical learning_ is just another term for _machine learning_ done from a slightly statistical-modeling point-of-view.]  \n* An Introduction to Statistical Learning with Applications in R | Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani\nhttp://www-bcf.usc.edu/~gareth/ISL/index.html\n    * You can download the latest version of the book as a PDF on that site: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf\n    * I would encourage watching these excellent course lecture videos (by the authors, who’re world-class scientists) that follow the book closely: http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/\n    * There are additional slides \u0026 videos from another good course taught based on this book: https://www.alsharif.info/iom530\n\n## Biology\n* [Learn genetics](https://learn.genetics.utah.edu/)\n* [IBiology](https://www.ibiology.org/biology-videos/)\n* [DNA seen through the eyes of a coder](https://ds9a.nl/amazing-dna/) - If you have a computational/quantitaive background, you'll esp. love this!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FJRaviLab%2Fcompbio-gists","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FJRaviLab%2Fcompbio-gists","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FJRaviLab%2Fcompbio-gists/lists"}