Curriculum: Computational Genomics Specialization Area

Computational Genomics entails efforts to digest the daunting quantity of genomic and proteomic data now available by systematic development and application of probability and statistics theories, information technologies and data mining techniques. Linguistics methods are viewed as promising tools towards elucidating sequence-structure-function relations, and complementing computational genomics studies. Computational genomics targets understanding gene/protein function, identifying and characterizing cellular regulatory networks and discerning the link between genes and diseases. Discovery and processing of this information is pivotal in the development of novel gene therapy strategies and tools.

Required Life Science Elective (3 credits/9 units)

CMU 03-730 Advanced Genetics

CMU 03-740

Advanced Biochemistry

CMU 03-741

Advanced Cell Biology

CMU 03-742

Molecular Biology

Pitt BIOSC 2100

Advanced Topics in Cell Biology

Specialization Electives (3 credits/9 units)

CMU 03-711 Computational Molecular Biology and Genomics
CMU 03-712 Computational Methods for Biological Modeling and Simulation

Pitt BIOINF 2054

Statistical Foundations for Bioinformatics Data Mining

Pitt BIOINF 2055

Practical Analysis of High-Throughput Genomic & Proteomic Data

Pitt BIOST 2070

Statistical Methods and Data Mining in Microarray Analysis

Pitt HUGEN 2022

Human Population Genetics

Pitt HUGEN 2033

Quantitative Genetics

Pitt HUGEN 2048

Linkage Analysis

Pitt MSCBIO 2020

Bioinformatics of Gene Regulation

Pitt MSMPHL 3315

Bioinformatics of Cancer Biology & Therapeutics