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
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 quarto source files (.qmd), go to the course repo, and clone the repo to navigate within your IDE (e.g. RStudio).
| Week | Topic | Dir. | HTML | Title |
|---|---|---|---|---|
| Data analysis | ||||
| 1 | Biological intro / GitHub | - |
github | RStudio, git, and GitHub |
| 2 | Exploring biol. data | eda |
EDA | Exploratory data analysis |
| brain RNA | Exploring brain RNA | |||
| 3–4 | Bioconductor | bioc |
objects | Bioc data objects |
| ranges | Genomic ranges | |||
| GRL | GRangesList: lists of ranges | |||
| anno | Accessing annotations | |||
| strings | Manipulating DNA strings | |||
| 4 | Tidy data for ’omics | tidyomics |
tidy intro | Tidiness-in-Bioconductor intro |
| tidy ranges | Tidy ranges tutorial | |||
| 5–7 | Distances & normalization | dist |
distances | Distances in high dimensions |
| transform_clust | Transformations and clustering | |||
| vst_math | Variance stabilization | |||
| batch | Batch effects and sources | |||
| sva | Surrogate variable analysis | |||
| ruv script | RUV and friends | |||
| Data modeling | ||||
| 8 | Hierarchical models | hier |
hierarchical | Hierarchical models |
| 9 | James-Stein and Stan | hier |
jamesstein | James-Stein estimator (Oct 13) |
| stan script | Hierarchical model in Stan | |||
| 9 | Fall break | No class (Oct 15) | ||
| 10 | Models and EM | model |
EM | Expectation maximization |
| motif | EM for finding DNA motifs | |||
| 11 | Markov models | markov |
hmm | Hidden Markov Models |
| 12 | Multiple testing | multiple |
multtest | FDR and Benjamini-Hochberg |
| localfdr | Local false discovery rate | |||
| 13–16 | Final project | In-class work (Nov 10, 12, 19, 24) + presentations (Dec 1) | ||
| 14 | Cancer transcriptomics & imaging | Guest: Katie Hoadley, Assoc. Prof., Genetics and LCCC (Nov 17) | ||
| Impromptu topics | ||||
| Gene regulatory networks | net |
network | Network analysis |
Other impromptu topics: genomic visualization, permutation / bootstrapping, single-cell, sequence alignment, ChIP-seq / ATAC-seq, perturbation assays / CRISPR, RNA splicing, data formats (FASTQ/BAM/VCF, 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 (Well-being day, no class; midterm due Wed Oct 7) | Oct 8 (hierarchical models, no quiz) |
| 9 | Oct 13 (James-Stein and Stan) | Oct 15 (Fall break, no class) |
| 10 | Oct 20 | Oct 22 |
| 11 | Oct 27 | Oct 29 |
| 12 | Nov 3 | Nov 5 |
| 13 | Nov 10 (final project work) | Nov 12 (final project work) |
| 14 | Nov 17 (guest: Katie Hoadley, transcriptomics & imaging in cancer) | Nov 19 (final project work) |
| 15 | Nov 24 (final project work) | (Thanksgiving) |
| 16 | Dec 1 |
The midterm is due Wednesday, Oct 7; deadlines with approved accommodations are adjusted accordingly.
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
- Bioconductor
- 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
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
This page was last updated on 10/08/2026.