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Course Description

The human reference genome took decades and vast resources to complete. In contrast, a transcriptome reflects how the genome is deployed in a specific context, but is vastly more complex and elusive. Great strides in characterizing transcriptomes have been enabled by advancements in sequencing technology over the past two decades. The objective of this course is to gain an understanding of the ways transcriptomes are analyzed with modern sequencing methods and emerging technologies. In this course, students will move beyond thinking of gene expression as getting lists of up and down genes, and gain an appreciation for how the transcriptome is quantified and analyzed - including splicing,  single-cell analysis, and spatially resolved assays. Beginning with quantified gene expression or “read counts”, the student will learn to design experiments that investigate gene regulation, tissue specific expression, and cell type specificity through hands-on exercises done with open-source software in R . The student will also gain an understanding of current and cutting-edge sequencing technology to know what transcriptomic assays best fit their larger research questions.

This course will use open-source (free) software including the RStudio GUI, which require command-line knowledge to install. All exercises will be adjusted to run on local hardware (most standard laptops).

This course is for graduate students and researchers that wish to build on their computational skills and apply that knowledge to practical problems of RNA analysis.

Learner Outcomes

When you complete the course successfully, you will be able to:

  • Plan a data analysis strategy for RNA sequencing experiments
  • Execute a data analysis strategy for bulk, single-cell, and spatial transcriptomic datasets 

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.

Textbook Information

There is no textbook for purchase required for this course.

Prerequisites

BIOF 501, BIOF339 or Familiarity with R
Basic command line usage or Unix/Linux

If you are unsure that you meet the prerequisite requirements, please contact registrar@faes.org and provide information about your course of interest and background knowledge.

Refund
Follow the link to review FAES Tuition Refund Policy.

Scholarship and Funding

Are you a self-funded student? FAES offers scholarship options. Click here for more information and to apply. 

Looking for resources to help you acquire funding for your continued education? Click here for our funding justification guide. 

Photo Release

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Section Title
High Resolution Analysis of Transcriptomes: From Read Counts to Cell Counts
Type
Online Asynchronous
Dates
Oct 22, 2025 to Dec 09, 2025
Total Cost (Includes $75 non-refundable technology fee per course when applicable)
Eligible Discounts Can Be Applied at Checkout (2 Credits) $775.00
Potential Discount(s)
Available for Academic Credit
2 Credit(s)
Instructor(s)
  • Dominic Acri
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