NYGC Sequence Informatics Workshop

The Unix shell has been around longer than most of its users have been alive. It has survived because it’s a powerful tool that allows users to perform complex and powerful tasks, often with just a few keystrokes or lines of code. It helps users automate repetitive tasks and easily combine smaller tasks into larger, more powerful workflows.

Use of the shell is fundamental to a wide range of advanced computing tasks, including high-performance computing. These lessons will introduce you to this powerful tool.

Prerequisites

FIXME

This lesson guides you through the basics of file systems and the shell. If you have stored files on a computer at all and recognize the word “file” and either “directory” or “folder” (two common words for the same thing), you’re ready for this lesson.

Schedule

	Setup	Download files required for the lesson
00:00	1. Introduction	What is a command shell and why would I use one?
00:40	2. Navigating and Editing Files	How can I move around on the computer/vm? How can I see what files and directories I have? How can I specify the location of a file or directory on the computer/vm? How can I specify the location of a file or directory in a bucket?
01:14	3. Working With Files and Directories	How can I create, copy, and delete files and directories? How can I edit files?
02:05	4. Repeating yourself	How can I combine existing commands to produce a desired output? How can I perform the same actions on many different files? How can I save and re-use commands?
02:55	5. Starting with data in R	How can I use data stored as a table of values (a data frame) for analysis?
03:45	6. Manipulating, analyzing and exporting data with tidyverse	How can I use data stored as a table of values (a data frame) for analysis?
04:35	7. Read Alignment and Small Variant Calling
04:35	8. Structural Variation in short reads	What is a structural variant? Why is structural variantion important?
06:05	9. Structural Variation in long reads	What are the advantages/disadvantages of long reads? How might we leverage a combination of long and short read data?
06:35	10. SV Exercises	How do the calls from short read data compare to those from the long read data? How do the
08:35	11. Cancer Genomics
08:40	12. Bulk RNA differential expression	How do I normalize a bulk RNA dataset? How can I summarize the relationship between samples? How can I compare expression based on biological or technical variables? How can I visualize the results of this comparison? How can I prepare differential expression results for input into downstream tools for biological insight? How can I interpret the results of functional enrichment tools?
11:40	13. Single-cell RNA	How can I prepare a single-cell dataset for input into analysis? How can I evaluate the quality of a single-cell RNA-Seq dataset? How can I visualize a single-cell dataset? How can I annotate cell types in a single-cell dataset?
14:40	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.