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Spring 2024: CS 3850 Bioinformatics & Computational Biology

[Course Information] [Logistics] [Syllabus and Course Schedule ]


Course Information


Instructor: Oluwatosin Oluwadare Ph.D.

Time and Location

Course Description

This course introduces students to the computational techniques used in the field of bioinformatics, which combines biology, computer science, and statistics to analyze biological data. Students will learn how to use programming languages such as Python and R to solve problems in genomics, proteomics, and other areas of bioinformatics. Topics to be covered include the practical use of bioinformatics tools and databases; biological data processing; algorithm development; data normalization; machine Learning in bioinformatics.

Student Learning Outcomes

After successfully completing the course, students will be able to: Describe discrete representations of various biological structures; Manually simulate some fundamental bioinformatic algorithms on small data sets; Understand Biological Data Mining; Make use of existing implementations to perform basic analyses of biological data sets; Use algorithms to develop tools to solve biological problems.


List of Publication by Previous Students


This course has a reputation for motivating students to engage in multidisciplinary research in computational biology and bioinformatics, create novel bioinformatics algorithms, and publish their class research project in reputable venues. The list of recent journal articles by former undergraduate or graduate students (as First Authors)  based on the work done in this class is provided below.

  • Collins, B.; Oluwadare, O.; Brown, P. (2021) ChromeBat: A Bio-Inspired Approach to 3D Genome Reconstruction. Genes 2021, 12, 1757. https://doi.org/10.3390/genes12111757 [@ Genes , h-index: 76]

  • Hovenga, V.; Oluwadare, O.(2021) CBCR: A Curriculum Based Strategy For Chromosome Reconstruction Int. J. Mol. Sci. 22, no. 8: 4140. https://doi.org/10.3390/ijms22084140 [@ Int. J. Mol. Sci. , h-index: 183 ]

  • Vadnais, D., Middleton, M. & Oluwadare, O.(2022). ParticleChromo3D: a Particle Swarm Optimization algorithm for chromosome 3D structure prediction from Hi-C data. BioData Mining 15, 19 (2022). https://doi.org/10.1186/s13040-022-00305-x. [@ BioData Mining , h-index: 22]


  • Syllabus and Course Schedule


    As the instructor for this course, I reserve the right to adjust this schedule in any way that serves the educational needs of the students enrolled in this course. - Oluwatosin Oluwadare

    Acronyms: Homework = HW, Reading Assignment = RA, Course Project = CP -->
    Date # Lecture Assignment Lecture Notes Extra Reading
    Out Due
    Wed, Jan 17 1 Course Overview and Introduction [PPT] [Big data in biology: The hope and present-day challenges in it]
    Mon, Jan 22 2 Introduction [PPT]
    Wed, Jan 24 3 Cell Biology [PPT] Basics of Molecular Biology
    Mon, Jan 29 4 Course Project Overview and Cell Biology (cont'd) CP
    RA1
    Example of a Completed Course Project
    List of Publication from Previous Students
    Wed, Jan 31 5 Cell Biology (cont'd)
    Mon, Feb 05 6a Know Your Data
    (Data mining: concepts and techniques, Jiawei Han et al. Chapter 2)
    [PPT]
    Wed, Feb 07 6b Know Your Data (cont'd) and
    Data Preprocessing
    (Data mining: concepts and techniques, Jiawei Han et al. Chapter 3)
    [PPT] [Ch-Square Table.pdf]
    Mon, Feb 12 7 RA1 Presentation RA2 RA1
    Wed, Feb 14 8 Data Preprocessing (cont'd)
    Mon, Feb 19 9 DNA Sequencing UCCS Classes Canceled [PPT]
    Wed, Feb 21 10 DNA Sequencing High-Throughput Sequencing Technologies
    Mon, Feb 26 11 High throughput Sequencing - introduction, technologies, alignment, assembly... HW1
    Tue, Feb 28 12 RA2 Presentation RA2
    Mon, Mar 04 13 (HTS) Hi-C Analysis: from data generation to Contact Matrix and Course Review [PPT] Hi-C analysis: from data generation to integration
    Wed, Mar 06 14 Understanding the Chromosome/Genome 3D Architecture
    Mon, Mar 11 15 Containerization using Docker HW2 HW1 [PPT] PyMOL for Educational Use
    Wed, Mar 13 16 Genome 3D Structure Visualization and Analysis (Tools and Software Review) and
    Methods for Super-Resolution Enhancement of Hi-C Data
    [PPT]
    Mon, Mar 18 17 Midterm Exam
    Wed, Mar 20 18 Case Studies RA Section 2: Group 2: (a) to (e) — Hands on CP1 HW2
    Mon, Mar 25 X Spring Break: No class
    Wed, Mar 27 X Spring Break: No class
    Mon, April 1 19 Case Studies RA Section 2: Group 1: (a) to (e) — Hands on
    Wed, Apr 03 20 Course Project Hypothesis Presentation and Review CP2 CP1
    Mon, Apr 08 21 Machine Learning in Bioinformatics [PPT]
    Wed, Apr 10
    22 Machine Learning in Bioinformatics (cont'd)
    Mon, Apr 15 23 Machine Learning in Bioinformatics (Classification: NB,Logistic Regression,Decision Trees)
    Wed, Apr 17 24 Machine Learning in Bioinformatics(Random forest,SVM,NN)
    Mon, Apr 22 25 Machine Learning in Bioinformatics(NN, HMM,etc.)
    Wed, Apr 24 26 Machine Learning in Bioinformatics (Unsupervised: Clustering ) CP3 CP2
    Mon, Apr 29 27 Course Project Presentation
    Wed, May 01 28 Course Project Presentation CP3
    Mon, May 06 29 Finals week: No class



    Reading Assignments


    Section 1: Chromosome Conformation data

  • de Wit, E., & De Laat, W. (2012). A decade of 3C technologies: insights into nuclear organization. Genes & development, 26(1), 11-24.
  • Han, J., Zhang, Z., & Wang, K. (2018). 3C and 3C-based techniques: the powerful tools for spatial genome organization deciphering. Molecular Cytogenetics, 11(1), 1-10.
  • Sati, S., & Cavalli, G. (2017). Chromosome conformation capture technologies and their impact in understanding genome function. Chromosoma, 126(1), 33-44.
  • (Not a Review) Lieberman-Aiden, E., Van Berkum, N. L., et al. (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. science, 326(5950), 289-293.
  • (Not a Review) Rao, S. S., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., ... & Aiden, E. L. (2014). A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 159(7), 1665-1680.

    Section 2: Special Topic in Genomics using Hi-C Data Analysis

    Select a topic from one of the options below and Review the publications in the group.

    Group 1: 3D genome structure reconstruction problem from high-resolution chromosome conformation capture data

    Review Articles:

    Some Case Study Methods for 3D genome structure reconstruction:

    1. Trieu, T., & Cheng, J. (2017). 3D genome structure modeling by Lorentzian objective function. Nucleic acids research, 45(3), 1049-1058.
    2. Oluwadare, O., Zhang, Y., & Cheng, J. (2018). A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data. BMC genomics, 19(1), 161.
    3. Meynier, T., & Rapsomaniki, M. (2021, December). Modeling the Three-Dimensional Chromatin Structure from Hi-C Data with Transfer Learning. In Annual Conference on Neural Information Processing Systems.
    4. Xiao Wang, Wei-Cheng Gu, Jie Li, Bin-Guang Ma (2023), EVRC: reconstruction of chromosome 3D structure models using error-vector resultant algorithm with clustering coefficient, Bioinformatics, Volume 39, Issue 11, November 2023,
    5. Hovenga, V., Kalita, J., & Oluwadare, O. (2023). HiC-GNN: A generalizable model for 3D chromosome reconstruction using graph convolutional neural networks. Computational and Structural Biotechnology Journal, 21, 812-836.

    Group 2: Super-Resolution Enhancement of HiC Data

    Review Article:

  • Murtaza, G., Jain, A., Hughes, M., Wagner, J., & Singh, R. (2023). A Comprehensive Evaluation of Generalizability of Deep Learning-Based Hi-C Resolution Improvement Methods. Genes, 15(1), 54.

    Some Case Study Methods for Super-Resolution Enhancement of HiC Data:

    1. Liu T., Wang Z. (2019a) HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data. Bioinformatics, 35, 4222–4228.
    2. Hong H. et al. (2020) DeepHiC: a generative adversarial network for enhancing Hi-C data resolution. PLoS Comput. Biol., 16, e1007287.
    3. Yangyang Hu, Wenxiu Ma (2021), EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework, Bioinformatics, Volume 37, Issue Supplement_1, July 2021, Pages i272–i279,
    4. Parker Hicks, Oluwatosin Oluwadare (2022), HiCARN: resolution enhancement of Hi-C data using cascading residual networks, Bioinformatics, Volume 38, Issue 9, March 2022, Pages 2414–2421,
    5. Bin Wang, Kun Liu, Yaohang Li, Jianxin Wang (2023), DFHiC: a dilated full convolution model to enhance the resolution of Hi-C data, Bioinformatics, Volume 39, Issue 5, May 2023, btad211,



  • Course Project


    There is one comprehensive course project. Each student will develop a core project they select out of the options provided in Section 2: Special Topic in Genomics using HiC-Data

    Project is Due: May 06, 2024
  • Download the Course project file template below depending on your publication choice and replace the "_YourLastname_" in the file name with your lastname
  • Reading Assignment & Course Project file template: (1) Template with Co-author (2) Template without Co-author
  • Different Submission of the course project will have a version number starting from version number zero label: v0


  • Presentation

    Each person has 25 minutes to present the selected project (about 20 minutes for presentation and 5 minutes for questions).


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