Computational Biology - UNC BIOS/BCB 784

Course information

Title

Introduction to Computational Biology – BIOS/BCB 784

Instructors

• Assoc. Prof. Michael Love, love [at] unc [dot] edu
• TA Yishu Zhang (Biostatistics PhD candidate), yzhang4 [at] unc [dot] edu

Please include “784” in the subject line when emailing instructors.

Time and place

• Fall 2026, 8/18/2026–12/1/2026
  Tue/Thur 12:30–1:45pm
  Michael Hooker Research Center, room 0003

Syllabus

• BIOS/BCB 784 – Fall 2006

Prerequisites

• R: This course makes extensive use of R and assumes basic familiarity with base R (not packages) as a prerequisite. A self-quiz is available here, with answers here. See also this list of base R functions.
• Statistics: The second half of the course involves Maximum Likelihood Estimation (MLE), optimization over latent variables, and basic application of Bayes theorem, drawing regularly on conditional probabilities and expectation. Students should be comfortable with these topics before enrolling. 784 is targeted to 2nd or 3rd year Biostatistics MS/PhD students and BCB students who have taken BCB 720. Email with “784” in the subject line for any questions.

Schedule of lectures

For Rmd or qmd files, go to the course repo, or clone the repo and navigate within your IDE.

Note: Class content will be put on a day-by-day schedule by August 10, 2026.

Week	Topic	Dir.	HTML	Title
	Data analysis
1	Biological intro / GitHub	-	github	RStudio, git, and GitHub
2–3	Exploring biol. data	eda	EDA	Exploratory data analysis
			brain RNA	Exploring brain RNA
3–5	Bioconductor	bioc	objects	Bioc data objects
			ranges	Genomic ranges
			GRL	GRangesList: lists of ranges
			anno	Accessing annotations
			strings	Manipulating DNA strings
5–6	Tidy data for ’omics	tidyomics	tidy intro	Tidiness-in-Bioconductor intro
			tidy ranges	Tidy ranges tutorial
6–7	Distances & normalization	dist	distances	Distances in high dimensions
			transform_clust	Transformations and clustering
			vst_math	VST math
			batch	Batch effects and sources
			sva	Surrogate variable analysis
			ruv script	RUV and friends
	Data modeling
8–9	Hierarchical models	hier	hierarchical	Hierarchical models
			jamesstein	James-Stein estimator
9–10	Models and EM	model	EM	Expectation maximization
			motif	EM for finding DNA motifs
11–12	Markov models	markov	hmm	Hidden Markov Models
12	Gene regulatory networks	net	network	Network analysis
13	Multiple testing	multiple	multtest	FDR and Benjamini-Hochberg
			localfdr	Local false discovery rate
14–16	Final project			In-class work (wk 14–15) + presentations (Dec 1)
	Impromptu topics
	Genomic visualization
	Permutation / bootstrapping
	Single-cell
	Sequence alignment
	ChIP-seq / ATAC-seq
	Perturbation assays / CRISPR
	Spatial transcriptomics
	RNA splicing
	Data: FASTQ/BAM/VCF
	Data: HDF5/Zarr/Parquet
	Computational efficiency
	Reproducible workflows

Fall 2026 Calendar

Week	Tuesday	Thursday
1	Aug 18 (intro)	Aug 20
2	Aug 25	Aug 27
3	Sep 1	Sep 3
4	Sep 8	Sep 10
5	Sep 15	Sep 17
6	Sep 22	Sep 24
7	Sep 29	Oct 1
8	Oct 6	Oct 8
9	Oct 13	Oct 15
10	Oct 20	Oct 22
11	Oct 27	Oct 29
12	Nov 3	Nov 5
13	Nov 10	Nov 12
14	Nov 17	Nov 19
15	Nov 24	(Thanksgiving)
16	Dec 1

Reading list

• What is the role of the computational biologist / statistician?
  • All biology is computational biology Florian Markowetz
  • Questions, Answers and Statistics Deborah Nolan
  • 50 Years of Data Science David Donoho
  • The Future of Data Analysis John Tukey (this article, discussed by Donoho, is from 1962)
  • Ten Simple Rules for Effective Statistical Practice Kass, Caffo, Davidian, Meng, Yu, and Reid
  • Statistical Modeling: The Two Cultures Leo Breiman
• Exploratory data analysis
  • dplyr - A Grammar of Data Manipulation
  • ggplot2
  • The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
• Bioconductor
  • Orchestrating high-throughput genomic analysis with Bioconductor Huber et al
• Distances and normalization
  • Differential expression analysis for sequence count data Simon Anders and Wolfgang Huber
  • Tackling the widespread and critical impact of batch effects in high-throughput data Leek et al
  • Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis Jeffrey Leek and John Storey
  • Normalization of RNA-seq data using factor analysis of control genes or samples Risso et al
  • Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses Stegle et al
  • More on factor analysis methods:
  • RUV - Using control genes to correct for unwanted variation in microarray data Gagnon-Bartsch and Speed et al., 2012
  • RUV - Removing Unwanted Variation from High Dimensional Data with Negative Controls Gagnon-Bartsch et al., 2013
  • RUVSeq - Normalization of RNA-seq data using factor analysis of control genes or samples Risso et al., 2014
  • ZINB-WaVE - A general and flexible method for signal extraction from single-cell RNA-seq data Risso and Perraudeau et al., 2018
  • NewWave - A scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data Agostinis et al., 2022
  • GLM-PCA - Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model Townes et al., 2019
• Multiple testing
  • Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing Yoav Benjamini and Yosef Hochberg
  • A direct approach to false discovery rates John Storey
  • Statistical significance for genomewide studies John Storey and Robert Tibshirani
  • Large-scale simultaneous hypothesis testing Bradley Efron
  • Empirical Bayes Analysis of a Microarray Experiment Efron et al
  • Measuring reproducibility of high-throughput experiments Li et al
• Expectation maximization
  • What is the expectation maximization algorithm? Chuong B Do and Serafim Batzoglou
  • Gaussian mixture models and the EM algorithm Ramesh Sridharan
  • EM algorithm notes Andrew Ng
  • MEME: discovering and analyzing DNA and protein sequence motifs Bailey et al
• Hierarchical models
  • Linear models and empirical Bayes methods for assessing differential expression in microarray experiments Gordon Smyth
  • Analyzing ’omics data using hierarchical models Hongkai Ji and X Shirley Liu
  • Stein’s Paradox in Statistics Bradley Efron and Carl Morris
  • Stein’s estimation rule and its competitors - an empirical Bayes approach Bradley Efron and Carl Morris
• Signal processing
  • An Introduction to Hidden Markov Models Lawrence Rabiner and Biing-Hwang Juang
  • Hidden Markov models approach to the analysis of array CGH data Fridlyand et al
• Network analysis
  • Static And Dynamic DNA Loops Form AP-1 Bound Activation Hubs During Macrophage Development Phanstiel et al

Resources

• Online R Classes and Resources
• Rafael Irizarry and Michael Love, “Data Analysis for the Life Sciences” Free PDF, HTML
• Kasper Hansen, “Bioconductor for Genomic Data Science”
• Aaron Quinlan, “Applied Computational Genomics” (Slides)
• Jennifer Bryan et al, Stat 545
• Florian Markowetz, “You Are Not Working for Me; I Am Working with You”
• Tips to succeed in Computational Biology research

Some R resources

• Quick-R
• R for Matlab users
• R Cookbook
• R Reference Card
• Advanced R
• Bioconductor Reference Card

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This page was last updated on 08/19/2026.

• GitHub repo
• License