Deep Sequencing
G4017
Next-generation sequencing (NGS) has become ubiquitous in biomedical research with numerous applications. This course will provide an in-depth introduction to principles of modern sequencing technologies, key computational algorithms and statistical models, and applications in disease genetics, cancer and systems biology. It will cover genome, exome and transcriptome sequencing approaches. Emphasis will be placed on understanding the interplay between experimental design, data acquisition, and data analysis so that students can apply these powerful tools in their own research.
Syllabus for Fall 2022
Instructors
Yufeng Shen,
Peter Sims, and
Chaolin Zhang
Topics
- History and development of modern sequencing technologies
- Introduction to statistics and algorithms
- Genome and exome sequencing and genetics of human diseases, such as autism, birth defects, and cancer
- RNA-Seq, epigenomics, and gene expression regulation (analysis and laboratory techniques for expression profiling, nascent transcript sequencing, CLIP-Seq, ribosome profiling, micro-RNA profiling, ATAC-seq, DNase hypersensitivity)
- Single-cell genomics
Goals
- To acquire introductory knowledge of modern high-throughput sequencing approaches including instrumentation and laboratory techniques
- To understand computational and statistical methods for analyzing genome sequencing data
- To formulate a biological question and investigate it by analyzing existing sequencing data
Requirements
This course is intended for graduate students or senior undergraduates interested in learning state-of-the-art sequencing approaches and their applications in biological and medical research.
Required: Basic knowledge in molecular biology, probability, and statistics
Preferred: working knowledge of UNIX and a programming language (R, MATLAB, Python, Ruby, or Perl); basic knowledge of genetics.
Grading
Class participation: 10%; Journal club: 10%; Mid-term exam: 40%; Final project and presentation: 40%