Semester: Fall 2025

Meeting time: Mon/Fri - 10:10-11:55am, Wed - 9:05-9:55am

Location:

Mon/Fri: CDS B62

Wed: SAR103

Zoom: By request only

Office hours: By appointment

Wednesdays, 10-12pm LSEB 101

Contents

Course Objectives

  • Learn the molecular mechanisms and basic data analysis steps that underly common next-generation sequencing experiments

  • Develop proficiency in creating bioinformatics workflows with an emphasis on reproducibility and portability

  • Gain experience generating and interpreting bioinformatics analyses in a biological context

Below you will find a selection of some of the prominent biological and computational topics that will be covered in the course:

  • High Throughput Sequencing Technologies (RNAseq, ChIPseq, scRNAseq) and various omics technologies (Proteomics Metabolomics, etc)
  • Computational Workflow Tools (snakemake, nextflow)
  • Reproducibility and Replicability Tools (Git, Docker, Conda)
  • Bioinformatics Databases and File Formats

Course Description

This course will expose students to modern bioinformatics studies with a specific focus on the analysis of next generation sequencing data. Lectures will cover a mix of both biological and computational topics necessary for the technical and conceptual understanding of current high-throughput genomics techniques. This will include brief discussions of the molecular mechanisms of the assays, basic data analysis workflows, and translating these results into biological conclusions.

Students will get hands-on experience developing computational workflows that perform an end-to-end analysis of sequencing data from ubiquitous NGS technologies including RNA-sequencing, ChIP-sequencing, and Single Cell RNA-sequencing. The course emphasizes the importance of reproducibility, and portability in modern bioinformatics.

Classes will be traditional lectures exploring the variety of topics regarding next generation sequencing and labs will focus on practical activities meant to develop experience working with the tools and technologies needed for the analysis and interpretation of sequencing data.

Course Values and Policies

Everyone is welcome. Every background, race, color, creed, religion, ethnic origin, age, sex, sexual orientation, gender identity, nationality is welcome and celebrated in this course. Everyone deserves respect, patience, and kindness. Disrespectful language, discrimination, or harassment of any kind are not tolerated, and may result in removal from class or the University. The instructors deem these principles to be inviolable human rights. Students should feel safe reporting any and all instances of discrimination or harassment to the instructor, to any of the Bioinformatics Program leadership, or the BU Equal Opportunity Office

Everyone brings value. Each of us brings unique experiences, skills, and creativity to this course. Our diversity is our greatest asset. Collaboration is highly encouraged. All students are encouraged to work together and seek out any and all available resources when completing projects in all aspects of the course, including sharing both ideas and code as well as those found on the internet. Any and all available resources may be brought to bear. However, consistent with BU policy, your reports should be written in your own words and represent your own work and understanding of the material.

Life happens. Your mental, physical and emotional health is far more important than any class. Make sure to take care of yourself and reach out to someone you trust (mentor, family member, or friend) if you ever feel you need to talk to someone. BU offers a number of resources through Student Health Services for managing situations involving grief, anxiety and depression, stress, homesickness and other common issues. I am also always here to listen without judgment and help you find any other resources. On a related note, if you need to miss class because of private matters, you do not need to disclose anything you aren’t comfortable sharing, please just let me know and I will work with you to help you catch up when you return. Your family, friends, and health should always come first.

Prerequisites

Basic understanding of biology and genomics. Any of these courses are adequate prerequisites for this course: BF527, BE505/BE605. Students should have some experience programming in a modern programming language (R, python, C, Java, etc).

Working familiarity with Git and command line interfaces is also heavily recommended.

Instructor and TAs

Joey Orofino

Contact information available on Blackboard

It should go without saying that our class is composed of people from a diverse set of backgrounds. Everyone will treat one another with respect and consideration at all times or be asked to leave the classroom.

As instructor, I will:

  1. Learn and correctly pronounce everyone’s preferred name/nickname
  2. Use preferred pronouns for those who wish to indicate this to me/the class
  3. Work to accommodate/prevent language related challenges (for instance I will do my best to avoid the use of idioms and slang)

Projects Overview

  • Project 1: Genome Assembly
  • Project 2: RNAseq
  • Project 3: ChIPseq
  • Project 4: Final Project

In order to generate reproducible, and portable NGS analysis workflows, we will be employing a combination of technologies including Nextflow, git, Conda, Docker, and HPC.

Subsequent projects will gradually add more complexity and tasks once you’ve gained experience with the fundamentals. Simultaneously, the amount of scaffolding and direct instructions will also be reduced.

Project Grading

Your final report for each project will ask you to write various sections of a scientific publication as well as produce certain figures and visualizations.

The grading system for this class works on a growth model. You will receive an unofficial grade per report, and be given detailed feedback on where to incorporate changes and edits. This grade is temporary and will improve or remain the same as long as you incorporate the feedback from each previous report or maintain the the same consistency.

If you want to think about it in terms of percentage, projects will account for 80% of your grade and participation in class / lab will account for the remaining 20%.

Course Schedule

Day Date Week Class Topic Project
Wed 9/3 1 Lecture Introduction  
Fri 9/5 1 Lab
Lab - Setup		 P1 assigned
Mon 9/8 2 Lecture Genomics, Genes, and Genomes  
Wed 9/10 2 Lecture Genomic Variation and SNP Analysis  
Fri 9/12 2 Lab Lab - Workflow Basics  
Mon 9/15 3 Lecture Computational Pipeline Strategies
SCC cluster usage		  
Wed 9/17 3 Lab Project 1 Check-In  
Fri 9/19 3 Lab Lab - Scaling Up and using the SCC  
Mon 9/22 4 Lecture Next Generation Sequencing

Sequence Analysis Fundamentals		  
Wed 9/24 4 Lecture Long Read Sequencing  
Fri 9/26 4 Lab Lab - Nextflow Practice P1 Due - P2 Assigned
Mon 9/29 5 Lecture Genome Editing - CRISPR Cas9  
Wed 10/1 5 Lab Project 1 Review  
Fri 10/3 5 Lab Lab - CRISPR Guides  
Mon 10/6 6 Lecture Sequence Analysis - RNA-Seq 1

Writing a methods section		  
Wed 10/8 6 Lecture Sequence Analysis - RNA-Seq 2  
Fri 10/10 6 Lab Lab - Containers (Docker)  
Tue 10/14 7 Lecture Biological Databases

Gene Sets and Enrichment
  
Wed 10/15 7 Lab Project 2 Check-In
  
Fri 10/17 7 Lab LAB - RNAseq Time Series and interaction analyses P2 Due - P3 assigned
Mon 10/20 8 Lecture Sequence Analysis - ChIP-Seq  
Wed 10/22 8 Lecture Microbiome: 16s  
Fri 10/24 8 Lab LAB - Genome Browsers  
Mon 10/27 9 Lecture Sequence analysis - ATAC-Seq  
Wed 10/29 9 Lab Project 3 Check-In  
Fri 10/31 9 Lab Lab - Using NF-Core Pipelines  
Mon 11/3 10 Lecture Microbiome: Metagenomics  
Wed 11/5 10 Lecture Proteomics  
Fri 11/7 10 Lab Lab - Differential Peak Analysis  
Mon 11/10 11 Lab Project 3 Final Check-In  
Wed 11/12 11 Lecture Metabolomics  
Fri 11/14 11 Lab Lab - Snakemake P3 due - Final Project Assigned
Mon 11/17 12 Lab Project 3 Discussion  
Wed 11/19 12 Lecture Single Cell Analysis Part 1  
Fri 11/21 12 Lecture Single Cell Analysis Part 2

LAB - Single Cell QC
  
Mon 11/24 13 Lecture Single Cell Analysis Part 3  
  11/26   NO CLASS    
  11/28   NO CLASS    
Mon 12/1 14 Lab LAB - Single Cell Workflow  
Wed 12/3 14 Lecture Spatial Transcriptomics  
Fri 12/5 14 Lab LAB - Single Cell Pre-Processing  
Mon 12/8 15 Lab LAB - Single Cell Integration  
Wed 12/10 15 Lab Feedback