Computational Biology Research - Gene Expression

Name: Computational Biology Research - Gene Expression
Rating: 4.5 (9 reviews)

Understand how gene expression research can be used to bring precision medicine.

Created byInhan Lee

Last updated 9/2020

English

English [Auto],

What you'll learn

Gene expression
microarray
Disease research in RNA level (example: Parkinson's and Alzheimer's diseases will be used as examples)
t-test
enrichment testing
NCBI tools
Gene Ontology
Signalling Pathway tools
computational biology
genetics
research skills

Course content

6 sections • 27 lectures • 8h 26m total length

Instructor introduction18:30
Transcription: house-keeping genes and cell-function specific genes
Transcription: House-keeping genes and cell-function specific genes28:49
At the end of the lecture, there are discussion points. Please write your own logical reasons why you choose those genes express in a certain way.
Breakout room discussion 1
Transcription Factor14:33
Regulatory transcription factors bind promoters to recruit RNA polymerase, modulating gene transcription; activators and repressors shift expression, altering cell phenotype in response to stress, like cortisol.
Breakout room discussion 2
Translation: DNA mutation effects on protein14:40
Explore how translation converts nucleotides to amino acids via a triplet code, and how DNA mutations—point, frameshift, missense, nonsense—alter proteins, including silent changes, premature stops, and truncated forms.
Breakout room discussion 3
Translation: ribosome and more of expression regulation9:41
Trace translation at the ribosome, where tRNA anticodons pair with mRNA to assemble amino acids into peptides, then explore how primary to quaternary structures shape function.
Breakout room discussion 4

Precision Medicine24:06
Genes and Diseases24:35
Disease Resource19:21
Learn to use credible disease resources and understand that gene expression, not gene sequence, can be modulated by lifestyle, and that genes function in networks affecting homeostasis.
Disease Presentation6:05
Please decide a disease for you to research. It should be a complex disease (caused by multiple genes and environmental effects or unknown). Make a presentation slide on that disease following the guidelines shown here.
Section 2

transcripton20:31
Explore transcription and RNA processing, including positive and negative strands, exons and introns, five-to-three prime orientation, and alternative splicing, with UCSC genome browser and gene expression omnibus.
UCSC genome browser19:17
Explore the UCSC genome browser to locate EGFR on chromosome 7 and view exons, introns, and the assembly’s coding sequence.
Gene Expression and microarray9:21
Explore how identical DNA yields different cell types through RNA expression and transcription, and learn how microarrays measure thousands of gene expressions via probe hybridization and fluorescent signals.
Microarray questions for microarray virtual lab prepared by U of Utah2:20
Please use the prompts to answer them and understand the concepts of microarray.
Section 3

Gene Expression Omnibus20:24
Learn to use the gene expression omnibus to locate microarray data for disease research, identify human datasets with clear two-group comparisons, and compare microarray with sequencing-based expression profiling.
GEO2R part 126:16
Learn to compare gene expression between groups using microarray data, define up and down regulation, and use GEO2R to analyze Parkinson's datasets while handling outliers and assessing statistical power.
t-test and p-value28:17
Explore how to assess gene expression differences using the t-test and p-value, including null hypothesis, means, standard deviation, and sample size to compare two groups.
GEO2R part 220:06
Perform GEO2R part 2 analyses to compare control and Parkinson's gene expression using P values, t tests, logFC, and chromosome location, then save results for Google Sheets and STRING.
Section 4

How to Read a Research Paper28:48
Gene Ontoogy12:21
Explore gene ontology to classify genes by go process, select top or regulated genes via p-value and log fold change, and compare biological process, molecular function, and cellular component.
String-db part 119:03
String-db part 2 (enrichment)20:05
KEGG pathway22:37
Explore gene regulation mechanisms, from DNA methylation to microRNAs, and learn how KEGG pathways map activation and suppression in cancer and HPV infection.
GeneCards and Research12:44
Explore GeneCards to look up a gene, view aliases, expression patterns, disorders, pathways, and gene function, and connect findings to disease context.

Final Overview11:44
miRcore volunteer program (MVP)21:14
GIDAS (guide-us, Genes in Diseases and Symptoms)12:45
Explore Guidice, a high school club that democratizes medical research through genetics projects, fundraising, and public outreach, while preparing members to present at conferences and start new clubs.
AI and Machine learning37:57
Explore artificial intelligence and machine learning in biology, covering data driven learning, regression and classification, with examples from genomics, protein folding, and skin cancer.
Final Assessment

Requirements

High school biology class or similar knowledge

Description

This course is designed to facilitate transcriptome research (analyze gene expression data) for people with high school biology-level knowledge. It covers overall transcriptome research that scientists use with application to a disease of focus.

The course covers 1) precision medicine and current biotechnology advances, 2) their implications for our day-to-day health, 3) roles of genes in health and diseases, and 4) the complex influence of daily behaviors on gene functions.

This course emphasizes the impact of real-life environmental factors on and ways in which research can solve certain kinds of problems.

It also covers biology baasics and statistical concepts helpful in doing transciptome research.

Please note that the current version consists of combined lectures from the summer camps we run yearly. This is so that prospective students who want to join our miRcore volunteer programs this school year can take it and join in a timely manner. While the audiences in the lectures are summer campers, the curriculum was prepared as an online course. Please understand some discrepancies in dates and groups in the lectures.

Who this course is for:

Students (of any age!) with a love of learning & research
miRcore volunteer program candidates
Anyone with a broad interest in genetics

Computational Biology Research - Gene Expression

What you'll learn

Explore related topics

Course content

Background5 lectures • 1hr 26min

Genes and Diseases (DNA focus)4 lectures • 1hr 14min

Gene Expression4 lectures • 51min

Gene expression data and statistical analysis4 lectures • 1hr 35min

Protein functions and Pathways6 lectures • 1hr 56min

Closing up4 lectures • 1hr 24min

Requirements

Description

Who this course is for: