Semester: Fall 2026

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

Location:

Mon/Fri: CDS B62

Wed: SAR103

Zoom: Posted on Blackboard

Office hours: By appointment — contact information on Blackboard

Joey Wednesdays, 10-11am LSEB 101

Monday, 3-4pm LSEB 101

Contents

Course Objectives

  • Learn the molecular mechanisms and basic data analysis steps that underlie 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

Topics covered include:

  • 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 covers modern bioinformatics with a specific focus on the analysis of next generation sequencing data. Lectures cover a mix of biological and computational topics necessary for the technical and conceptual understanding of current high-throughput genomics techniques, including the molecular mechanisms of the assays, basic data analysis workflows, and translating results into biological conclusions.

Students build computational workflows that perform end-to-end analyses of sequencing data from RNA-sequencing, ChIP-sequencing, and Single Cell RNA-sequencing experiments. The course emphasizes reproducibility and portability throughout.

Labs focus on practical activities with the tools and technologies needed to analyze and interpret sequencing data.

Prerequisites

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

Working familiarity with Git and the command line is strongly recommended.

Required Software

All you need is a laptop. Course computing runs on BU’s Shared Computing Cluster (SCC), accessed via a browser-based VSCode session — no local installation of bioinformatics tools is required. You will need a BU SCC account (obtainable through the Research Computing help desk) and a GitHub account before the first lab.

Instructor and TAs

Joey Orofino

Contact information available on Blackboard

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
  3. Work to accommodate language-related challenges (I will do my best to avoid idioms and slang)

Course Values and Policies

Respect. Every background, race, color, creed, religion, ethnic origin, age, sex, sexual orientation, gender identity, and nationality is welcome in this course. Disrespectful language, discrimination, or harassment of any kind are not tolerated and may result in removal from class or the University. Incidents can be reported to the instructor, the Bioinformatics Program leadership, or the BU Equal Opportunity Office.

Collaboration. Collaboration is encouraged. You may work with others, share ideas and code, and use any resources available to you — including the internet. Your written reports must reflect your own analysis, interpretation, and understanding of the results.

Attendance. Lab attendance is tracked through Git commits. Each lab session has associated tasks that should be committed to your repository during or shortly after the session. Regular commit activity is expected and counts toward the 20% participation grade.

AI and LLM tools. AI tools (ChatGPT, GitHub Copilot, Claude, etc.) may be used to help write and debug code. Written reports must be your own — submitting AI-generated text as your own scientific writing is not permitted.

Flexibility. If something comes up that affects your ability to participate, let me know and we’ll work it out. You don’t need to share details you’re not comfortable sharing. BU Student Health Services is available if you need additional support.

Projects Overview

Each project asks you to build a Nextflow pipeline that performs an end-to-end analysis of a real sequencing dataset, then write up the results as sections of a scientific publication. Projects increase in complexity and decrease in scaffolding as the semester progresses.

  • Project 1 — Genome Assembly: Assemble a bacterial genome from long reads, assess assembly quality, and annotate predicted genes.
  • Project 2 — RNA-seq: Quantify gene expression from paired-end RNA-seq data, identify differentially expressed genes, and interpret the results in a biological context.
  • Project 3 — ChIP-seq: Call transcription factor binding peaks, perform motif enrichment analysis, and compare binding profiles across conditions.
  • Final Project: An open-ended analysis of a dataset and question of your choosing, integrating methods from across the course.

All pipelines are built with Nextflow and run on the SCC using Singularity containers, with results version-controlled in Git.

A note on lab numbering: Labs are numbered by topic rather than chronological order. Some labs appear in the schedule out of numerical sequence — each number is a stable topic reference, not a position indicator.

Project Grading

Each project report asks you to write sections of a scientific publication and produce relevant figures and visualizations.

Grading works on a growth model. You will receive an unofficial grade per report along with detailed feedback. That grade is temporary — it will improve as long as you incorporate the feedback from each previous report. Projects account for 80% of your final grade; participation in class and lab accounts for the remaining 20%.

Course Schedule

Day Date Week Class Topic Project
Wed 9/2 1 Lecture Introduction  
Fri 9/4 1 Lab Lab 01 — Setup  
Mon 9/7   NO CLASS Labor Day  
Wed 9/9 2 Lecture Genomics, Genes, and Genomes
Next Generation Sequencing		 P1 assigned
Fri 9/11 2 Lab Lab 02 — Workflow Basics  
Mon 9/14 3 Lab Lab 03 — Nextflow Tooling  
Wed 9/16 3 Lecture Sequence Analysis Fundamentals  
Fri 9/18 3 Lab Lab 04 — Multi-Sample Pipelines  
Mon 9/21 4 Lecture Genomic Variation and SNP Analysis  
Wed 9/23 4 Lecture Long Read Sequencing  
Fri 9/25 4 Lab Lab 05 — Typed Channel Operators  
Mon 9/28 5 Lecture Sequence Analysis — RNA-Seq 1  
Wed 9/30 5 Lecture Sequence Analysis — RNA-Seq 2  
Fri 10/2 5 Lab Lab 06 — Containers (Docker)  
Mon 10/5 6 Lab Lab 07 — QC Pipeline with Singularity  
Wed 10/7 6 Lecture Biological Databases
Gene Sets and Enrichment		  
Fri 10/9 6 Lecture P1 Check-In and Review P1 due — P2 assigned
Mon 10/12   NO CLASS Indigenous People’s Day  
Tue 10/13 7 Lecture Genome Editing — CRISPR-Cas9
(Monday schedule substitute)		  
Wed 10/14 7 Lecture Sequence Analysis — ChIP-Seq  
Fri 10/16 7 Lab Lab 11 — RNAseq and DESeq2  
Mon 10/19 8 Lecture Sequence Analysis — ATAC-Seq  
Wed 10/21 8 Lecture P2 Check-In  
Fri 10/23 8 Lab Lab 09 — CRISPR Guide Design  
Mon 10/26 9 Lecture Microbiome: 16S and Metagenomics  
Wed 10/28 9 Lecture Metabolomics  
Fri 10/30 9 Lab Lab 12 — Differential Peak Analysis (ATACseq) P2 due — P3 assigned
Mon 11/2 10 Lecture Single Cell Analysis Part 1  
Wed 11/4 10 Lecture Single Cell Analysis Part 2  
Fri 11/6 10 Lab Lab 08 — Snakemake  
Mon 11/9 11 Lecture Single Cell Analysis Part 3  
Wed 11/11 11 Lecture Spatial Transcriptomics  
Fri 11/13 11 Lab Lab 10 — Genome Browsers  
Mon 11/16 12 Lecture P3 Check-In  
Wed 11/18 12 Lecture Single Cell Analysis Part 4 / Extended Topics  
Fri 11/20 12 Lab Lab 13 — Single Cell Setup P3 due — Final assigned
Mon 11/23 13 Lab Lab 14 — Single Cell QC  
  11/25   NO CLASS Thanksgiving Recess  
  11/28   NO CLASS Thanksgiving Recess  
Mon 11/30 14 Lab Lab 15 — Single Cell Preprocessing  
Wed 12/2 14 Lab Final Project Work Session  
Fri 12/4 14 Lab Lab 16 — Single Cell Pseudobulk  
Mon 12/7 15 Lab Single Cell Integration  
Wed 12/9 15 Lab Feedback  
  12/14     Final Exams Begin Final Project Due