Course Description
Advanced strategies and methodologies for large-scale data analyses in support of genomics, transcriptomics, proteomics, and studies of biological pathways and networks. Topics include gene finding, genomic rearrangements, microarray data analyses, protein function inference, protein-protein interaction prediction, and pathway and network prediction. Major data mining tools will be covered for each topic.
Athena Title
Bioinformatics II
Non-Traditional Format
This course will be taught 95% or more online.
Prerequisite
BCMB 3100 or BCMB 3100E or BCMB 3100H or BCMB 4010/6010 or BCMB 4020/6020 or GENE 3200-3200D or GENE 3200E or GENE 3200H
Pre or Corequisite
BCMB(BINF) 8210
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
The course participants will acquire (i) knowledge about the key problems and challenges, existing resources and databases, existing software tools, and strategies for analyzing large-scale data in support of genomics, transcriptomics, proteomics, and prediction and analysis of biological pathways and networks; and (ii) skills to design and implement simple computational tools to uncover hidden information from high-throughput biological experimental data. The course is designed for students who have taken the BCMB 8210 or MIBO 8110L course or equivalent and already have basic knowledge about genomic sequences, gene expression, protein structure and functions, and pathways and networks. The course will provide an in-depth coverage of each topic. Students are required to carry out a term project focused on one problem, provided by the course instructor, selected from one of the following four areas: genome analyses, gene expression data analyses, protein function prediction, or inference of biological pathways and networks. The outcome of the project will be a software tool for solving the given problem, along with a project report. By the end of the course each student should be able to: 1) analyze and interpret large-scale genomic DNA sequence and gene expression data by using existing software tools and resources. 2) predict protein functions by using existing software tools and resources; 3) predict simple biological pathways and network using existing tools, experimental data and other resources, and 4) design and implement simple computation tools for various data mining and computational predictions.
Topical Outline
1. Advanced methods for gene finding 2. Advanced methods for sequence motif finding 3. Ortholog and paralog mapping 4. Gene ontologies 5. Genomic structural polymorphism (rearrangements), single nucleotide polymorphism (SNP) 6. Gene expression alteration (micro-array data analyses) 7. Protein domain and motif identification 8. RNA structure prediction, and RNA gene finding 9. Structure-based protein function prediction 10. Protein-protein interaction networks 11. Pathway and network mapping, simulation, and prediction
Syllabus