Bioinformatics Track
The Bioinformatics track combines research training opportunities in addition to advanced courses in the field of biostatistics, computational sciences, and applied mathematics. Students from all themes are welcome to join the Bioinformatics track.
Bioinformatics students are required to take the following courses:
Spring Modules:
INFO 701- Intro to Bioinformatics (3 hours)
Introduction to bioinformatics and computational biology, with emphasis on concepts and application of informatics tools to molecular biology. It covers biological sequence analysis, gene prediction, genome annotation, gene expression analysis, protein structure prediction, evolutionary biology and comparative genomics, bioinformatics databases, cloud computing, basic R-based data analysis, simple programming skills using Perl, Linux/Unix environment and command lines, visual analytics, and social/legal aspects of open science. It will have a class research project component.
INFO 703- Biological Data Management (3 hours)
The introduction of biological data management concepts, theories, and applications. Basic concepts such as relational data representation, relational database modeling, and relational database queries will be introduced in the context of SQL and relational algebra. Advanced concepts including ontology representation and database development workflow will be introduced. Emerging big data concepts and tools, including Hadoop and NoSQL, will be introduced in the context of managing semi-structured and unstructured data. Application of biological data management in biology will be covered using case studies of high-impact widely used biological databases. A class project will be required of all participants. Pre-requisite is INFO 601.
+ 2 additional GBS/GBSC Spring modules *
+ 1 additional GBS/GBSC Summer module *
*Module courses are chosen at the discretion of theme specific requirements.
Advanced Courses:
Students will have the opportunity to choose 3 advanced courses from the following:
INFO 702- Algorithms in Bioinformatics (3 hours)
This course introduces various fundamental algorithms and computational concepts for solving questions in bioinformatics and functional genomics. These include graph algorithms, dynamic programming, combinatorial algorithms, randomized algorithms, pattern matching, classification and clustering algorithms, hidden Markov models and more. Each concept will be introduced in the context of a concrete biological or genomic application. A broad range of topics will be covered, ranging from gene identification, genome reconstruction, microarray data analysis, phylogeny reconstruction, sequence alignments, to variant detection. Pre-requisite is INFO 701 or with instructor permission.
INFO 704- Next Generation Sequencing Data Analysis (3 hours)
The introduction of next-generation sequencing (NGS) technologies and the various new genomics applications. Basic analysis begins with NGS data representations using FASTQ, BAM, and VCF files. Major NGS applications in the characterization of DNA, RNA, methylation, ChIP, and chromatin structure analysis will be described. Topics will cover alignment, whole genome de novo assembly, variant detection, third generation sequencing technologies, functional genomics, metagenomics, single cell genomics, genetic diseases and cancer genomics. NGS workflows and translational applications in disease biology and genome medicine will also be emphasized. Pre-requisite is INFO 701.
INFO 711/BST 621 Intermediate Statistical Analysis I (3 hours)
Students will gain a thorough understanding of basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and non-parametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency table data, simple linear regression, and simple analysis of variance. Students are taught to conduct the relevant analysis using current software such as the Statistical Analysis System (SAS).
INFO 790/NUR 790 Data Mining and Statistical Learning (3 hours)
Students will learn to discover and implement meaningful insights and knowledge from data. This course covers major concepts and algorithms of data mining. The course will be taught using the SAS Enterprise Miner program. The final project will demonstrate all the data mining techniques covered in the course and furthermore expose students working with real data. At the end of the course students will be proficient in utilizing data mining techniques to exploit data patterns and behavior, gain insider understanding of the data, and produce new knowledge that healthcare decision-makers can act upon. Furthermore, SAS Certified Predictive Modeler certification exam will be offered at the end of the course. Instructor permission is required.
Students interested in joining the Bioinformatics track will need to further discuss theme requirements and course selection with their theme directors.
If you have any further questions, please contact Jake Chen (