CS 3850 Bioinformatics & Computational Biology

Course Information

Instructor: Oluwatosin Oluwadare Ph.D.

Office: Engineering Building - ENG 244
Phone: 719-255-3004
E-mail: [email protected]

Time and Location

Time: Mon/Wed 3:05pm - 4:20 pm
Office hours: Tues: 10:30am-11:30pm, Thurs: 10:00am -12:00pm and by appointment.
Classroom: ENG 107
Course Management: Canvas

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
Date	#	Lecture	Out	Due	Lecture Notes	Extra Reading
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:

Oluwadare, O., Highsmith, M., & Cheng, J. (2019). An overview of methods for reconstructing 3-d chromosome and genome structures from hi-c data. Biological procedures online, 21(1), 7.
MacKay, K., & Kusalik, A. (2020). Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data. Briefings in functional genomics, 19(4), 292-308.

Some Case Study Methods for 3D genome structure reconstruction:

Trieu, T., & Cheng, J. (2017). 3D genome structure modeling by Lorentzian objective function. Nucleic acids research, 45(3), 1049-1058.
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.
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.
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,
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:

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.
Hong H. et al. (2020) DeepHiC: a generative adversarial network for enhancing Hi-C data resolution. PLoS Comput. Biol., 16, e1007287.
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,
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,
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,

Spring 2024: CS 3850 Bioinformatics & Computational Biology

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