workshop2023.md

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title	2023 Lab Computational Genomics Workshop

• Fall 2023

• Location: Online

• Time: At your own pace

• Prerequisites: None

• Online Book: APllied Population Genetics

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Schedule Fall 2023

We will utilize the lab Slack channel #workshop to engage in a series of online courses, as well as to review relevant books and materials. This approach will allow us to enhance our fundamental computational skills essential for our work in the lab.

Basic Computational Skills with HPC

Week 1 (Aug 28): Linux Command Lines

• Learn about Linux Command lines and shell scripting (about 2 hours)
  • edX: Learn Linux Command Line
  • edX: Linux Commands & Shell Scripting
• Read HCC doc
• Learning Goals: establish an HCC account and become familiar with file operations

Week 2 (Sept 4): Git and GitHub

• Get started with Git and GitHub (about 7 hours in total)
  • Coursera course website
• Learning Goals: set up a GitHub account and get familiar with version control

Week 3 (Sept 11): Introduction to HPC

• Introduction to High-Performance and Parallel Computing (about 14 hours in total)
  • Coursera course website
• Learning Goals: get familiar with HPC and start a Git version controlled research project on HCC

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Basic R Programming for Population Genomics

A four week R Programming module on Coursera

Week 4 (Sept 18): Background and getting started (12 hours)

• Learning Goals: get familiar with R and RStudio
• HW due: present learning outcomes following the format of our weekly meeting report during one-on-one meeting
• Sept 20 (Wed), 10-12 am, PLSH 279M: Population Genomics Model 1

Week 5 (Sept 25): Programming with R (8 hours)

• Learning Goals: read, write, and manipulate genomic data

Week 6 (Oct 1): Loop functions and debugging (11 hours)

• Learning Goal: compute basic stat (i.e., MAF, missing rate, LD decay rate)
• HW due: Oct 10 during one-on-one meeting

Week 7 (Oct 9): Simulation and profiling (11 hours)

• Learning Goal: compute basic state on phenotypic datasets
• HW due: Oct 17 during one-on-one meeting
  • Identify a phenotypic dataset from what we have collected (maize or sorghum)
  • Come up with a testable hypothesis
  • Visualize the data and test the hypothesis

Week 8 (Oct 16): Fall break

Week 9 (Oct 23): Introduction and population genomics terminology

• Population Genomics Model 1
• Learning Goal: get population genomic information from sorghum sequencing data and sorghum and maize load paper
• HW: read the paper and discuss during one-on-one meeting

Week 10 (Oct 30): Diversity measurement

• Population Genomics Model 2
• Learning Goal: compute popgen stat from sorghum sequencing data
• HW: Calculate Fst using sorghum sequencing data

Week 11 (Nov 6): Scan for direct and linked selection

• Population Genomics Model 3
• Learning Goal: compute popgen stat from sorghum sequencing data
• HW: Calculate Tajima's D and pi_N/pi_S using sorghum sequencing data

Week 12 (Nov 15): Hands-on practices

• Dr. Gen Xu: BGEM population and GWAS

Week 13 (Nov 22): Hands-on practices

Week 14 (Nov 29): Hands-on practices

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Basic Machine Learning Skills using Python

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Basic Statistical Skills using R

Statistics and R

Linear Models and Matrix Algebra

Statistical Interference and Modeling for High-throughput Experiments

High-Dimensional Data Analysis