Udemy

Computational Gene Expression Analysis with Python

Essential skills for bioinformatics.
Free tutorial
Rating: 3.8 out of 5 (251 ratings)
6,032 students
1hr 16min of on-demand video
English
English [Auto]

Bioinformatics
Gene Expression
Computational Biology
Network Analysis
GEO2R
STRING Network Analysis
KEGG Pathways
Microarray
Proteins
DNA
RNA
Transcriptomics
Research
Biotechnology
Python
Programming

Requirements

  • High School General Biology
  • Basic Computer Knowledge
  • Interest in Research or Completing a Science Project

Description

TLDR: Learn to analyze and quantify differences in gene expression using public datasets from the Gene Expression Omnibus. Obtain a detailed understanding of how gene expression analysis works, i.e. what is fold change? See examples of how Python can be used to analyze and visualize gene expression data.


You will learn how to use tools like GEO2R, StringDB, PantherDB, and more to analyze publicly available gene expression data!


The course will guide you on choosing a research topic, finding a dataset, processing the data, and analyzing the data graphically with several tools, like StringDB. As a bonus, you will get insight into how to write a paper about your project.


Example topics for research include:

  1. Identifying potential biomarkers for cancer (useful in diagnostics)

  2. Analyzing changes in gene expression when a sample is treated with X drug or under Y condition

  3. Differences in gene expression between early and late stage cancer (useful in prognosis and drug development)

The example project being done in this course is for identifying blood biomarkers for early stage Parkinson's disease.


Materials needed:

1. Computer

2. Google Account (for Google Sheets + Colab) or Excel

3. Internet Connection


If there is enough interest, another course will be created that features gene expression analysis with machine learning.

Who this course is for:

  • Middle and high school students interested in completing an original computational biology science project
  • Students interested in bioinformatics and computational biology

Instructors

Bioinformatics Researcher
Andrew Gao
  • 3.8 Instructor Rating
  • 251 Reviews
  • 18,246 Students
  • 1 Course

Andrew is an avid researcher and is the founder and CEO of the world's largest community and nonprofit of youth researchers, The Helyx Initiative.


He is a 2020 ISEF Finalist, one of only 3 students with life sciences projects from San Diego to qualify. He has also been published as first author in a peer-reviewed Elsevier Journal with Impact Factor 2.9 (around top 25% of journals).


Andrew has research experience in gene expression analysis, DNA methylation, disease diagnosis, and more. He has taught at several educational research camps and programs, like Helyx's Bioinformatics Camp.


Background:

1. First author publication in Microbial Pathogenesis

2. 2020 ISEF Finalist

3. San Diego Science Fair 1st Place in Medicine/Health Sciences

4. Kaiser Permanente Award

5. IRIC Insposcience Silver Medal

6. 3 year intern at UCSD Supercomputer Center

7. Summer intern at the Scripps Institute for bioinformatics tool development

8. Founder and CEO of The Helyx Initiative

Instructor at Udemy
Sarah Gao
  • 4.3 Instructor Rating
  • 276 Reviews
  • 7,724 Students
  • 2 Courses

Sarah Gao is a trailblazing environmental researcher and advocate from San Diego, California! Through her novel software-driven research on using seeds to purify water sustainably and affordably, she is proud to have won top prizes at the county and state science fairs and qualified for ISEF. Sarah is also dedicated to sharing her love for science. She created 150 educational graphics about chemistry, aiming to inspire girls in STEM. Sarah values resilience, science communication, and the beauty of nature.

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