This repository shows how to use ☁️Google Cloud Platform public cloud services to scale bioinformatics data analysis tasks using cloud best practices for GCP. This use cases featured as exampled are called any and all of the following: genomic-scale data workflows, pipelines, analysis or batch jobs.

This content is intended for researchers - in particular, this guide is for those who are NEW to working with GCP.
You have a number of options on how to use the materials provided in this course. A summary is shown below left.

This Repo includes content you can read, watch or run:

• 📗 READ - one page of this Repo (MD page)
• 📺 WATCH - linked YouTube screencasts
• 📙 RUN - Jupyter Notebook example
• TRY - linked GitHub Repos
• 📘 EXPAND - linked (external) resources
• 🔍 SCAN - search a list in this Repo

NOTE: If you are NEW to bioinformatics and have a computational background, see my 'study Repo' which includes links to explanations of bioinformatics concepts, tools and platforms - link

⭐️ SAVE MONEY run (and pay for) scalable analysis jobs only when you need to run them
⭐️ SAVE TIME use vendor-managed infrastructure & best-practice patterns for fast repeatable research
📗 READ the FAQ for GCP bioinformatics for this Repo
📕 READ Nature article: "Cloud computing for genomic data analysis and collaboration"
📗 READ the top 4 most common use cases for using the public cloud for bioinformatics researchers

We love contributions! See this short style guide when making pull requests to this repo.