Omics: Big Data in Biology

Learn the basic principles of omics knowledge, NGS technologies, biomedical databases, bioinformatics tools and machine learning.


Dr. Shaolei Teng is a Bioinformatician and Biostatistician. He is an Associate Professor and Associate Chair in the Department of Biology at Howard University. He received his PhD in Biochemistry and Molecular Biology from Clemson University and completed his postdoctoral training in Bioinformatics and Genomics at Cold Spring Harbor Laboratory. Dr. Teng’s research interests are to develop and apply bioinformatics approaches for analyzing the genetic variations associated with human diseases and discovering biological knowledge hidden in the massive data sets. Dr. Teng is working on areas including machine learning, next-generation sequencing and protein structure modeling. Dr. Teng has published more than 40 papers in the peer-reviewed journals such as Briefings in Bioinformatics, Molecular Psychiatry, BMC Genomics, Amino Acids and Biophysical Journal. Dr. Teng is serving as the PI for NSF Excellence in Research, Co-PIs for NSF Harnessing the Data Revolution, NSF Targeted Infusion Project and DoD Center of Excellence in AI/ML, and Students’ Research Mentors for multiple education grants.

Course Description
Biology becomes a Big Data science with the advent of high-throughput technologies such as Next-Generation Sequencing (NGS) and Proteomics technologies. The NGS provide powerful system-scale sequencing tools to detect numerous mutations in millions of human individuals and study the gene functions and expressions in the system level. Other technologies are also used to generate the huge datasets for epigenomics, proteomics and metabolomics research. However, biological meanings behind these datasets remain elusive. There is a significant need for students to learn the computational methods to analyze the omics data and discover biological knowledge hidden in the heterogeneous datasets. This course is intended to expose students to the latest developments in the Omics Big Data analysis. There are two major goals for this course: 1) To understand the knowledge of gene functions and expressions and omics data analysis in genomics, transcriptomics, and proteomics fields; 2) To gain practical experience in the application of state-of-the-art omics to bioinformatics and biomedical research. This course will be focused on the students pursuing STEM careers to construct solid foundations for omics and bioinformatics, and to develop abilities to apply their knowledge to practical problems in Big Data analysis.

Course Objectives

Students will learn the basic principles of omics knowledge, NGS technologies, biomedical databases, bioinformatics tools and machine learning. Upon the completion of the lectures, students are expected to:

  • Learn the genomics, transcriptomics, and proteomics
  • Describe the basic aspects of NGS and other omics technologies.
  • Understand the roles of sequence variants in human diversity and diseases.
  • Explain the RNAseq for gene expression and gene regulatory network.
  • Discuss the omics results in a biological and biomedical context.
  • Relate the omics analysis to human diseases and health disparities.