Week 1: Intro to the course and to omics data
Overview
Welcome to the autumn 2026 edition of the PLNTPTH 5006 course, Practical Computing Skills for Omics Data! In this first week of the course, you will get an overview of the course’s contents and an introduction to omics data.
This is the GitHub website for the course. It contains the course materials, including lecture slides, readings, and assignments. In other words, it contains most of the material you might normally find in CarmenCanvas. In this course, CarmenCanvas is only used for course announcements, grades, and a course calendar that includes assignment deadlines.
Learning goals
Across the following two slide decks, you will learn:
A) Introduction to the course
- A little about your instructor, TA, and fellow students.
- What to expect from this course, and some background on the course’s choice of topics.
- What is expected of you during this course.
- What your grade will be made up of.
B) An overview of omics data
- What omics data is.
- What the main high-throughput sequencing technologies are.
- How Illumina sequence libraries are structured.
- What reference genomes are, and what they are good for.
We most likely won’t finish the second part on Wednesday — we’ll continue it next Monday.
Assignments & exercises
Ungraded Assignment 1: Pre-course survey Deadline: Tue Aug 25, 5 pm
Ungraded Assignment 2: Ohio Supercomputer Center account setup Deadline: Sun Aug 30
Readings
You can do weekly readings at your own pace at some point during the week. When we do discuss a reading in class, it will be in the following week.
“Overview of Illumina Sequencing by Synthesis Workflow” Watch this 5-minute video to learn a bit more about how Illumina sequencing works. This is meant to provide a bit more background, given that the example data in this course will be from Illumina sequencing. It’s a very quick overview and you don’t need to understand all the details!
Poinsignon et al. (2023): Working with omics data: An interdisciplinary challenge at the crossroads of biology and computer science (OSU proxy link )
This book chapter provides an overview of different kinds of omics data. It is the most suitable introduction to omics data I could find, despite two caveats. First, you may find it striking that it appeared in a book called “Machine Learning for Brain Disorders”, but the chapter itself is not field-specific at all. Second, while it contains an unusual number of typos, there are no serious errors.
Optional self-study material
Introductory omics papers
Consider reading one of the following papers — browse through the paper titles, and see if any spark your interest. Many of these present omics and HTS data in a more field-specific context, such as that of plant pathology:
- Lee (2023): The principles and applications of high-throughput sequencing technologies (open access link )
- Nizamani et al. (2023): High-throughput sequencing in plant disease management: a comprehensive review of benefits, challenges, and future perspectives (OSU proxy link )
- Aragona et al. (2022): New-generation sequencing technology in diagnosis of fungal plant pathogens: A dream comes true? (open access link )
- Mahmood et al. (2022): Multi-omics revolution to promote plant breeding efficiency (open access link )
- Pinto and Bhatt (2024): Sequencing-based analysis of microbiomes (OSU proxy link )
- Manzoni et al. (2018): Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences (OSU proxy link )
- Heyden et al. (2025): Advancing species conservation and management through omics tools (OSU proxy link )