USC's Department of Translational Genomics at Keck's School of Medicine is offering an intensive two-year MS program in biomedical informatics focusing on bioinformatics within health-related fields.  This program is focused on training individuals who have strong backgrounds in laboratory-based biomedical sciences and seek the bioinformatic skills for analyzing, processing, and managing large-scale data. Graduates will be suited to work as applied bioinformaticians within academic research laboratories, clinical research laboratories, pharmaceutical companies, and biotechnology companies.

What is Translational Biomedical Informatics?

The goal of this program is to train applied bioinformaticians, providing students with the training, skillsets, and best practices for applying and integrating existing bioinformatics tools in the study of human health and disease.

  • Translational: Translating laboratory data to bedside or clinic
  • Biomedical: Relating to human biology, medicine, and disease
  • Informatics: Applied processing and analysis of data

This program is tailored for individuals with laboratory-based biomedical experience, biomedical sciences, or biomedical engineering. This program focuses on tool application and integration along pipelines, will scripting emphasized over coding. Graduates will have the analytical capabilities for analyzing datasets across molecular biology, systems biology, structural biology, and genomic sequencing datasets. A major emphasis is on data analysis and data processing associated with next-generation sequencing (NGS) data, understanding that the goal is to build core skill sets that remain relevant as new technologies emerge and change.

Learning Objectives

Upon graduation, students will:

  • Understand best practices for putting existing tools and bioinformatics datasets together to better understand biomedical problems;
  • Be able to analyze next-generation sequencing (NGS) including whole-genome, exome, and transcriptome sequencing (RNA-seq), as well as emerging methods in single-cell sequencing;
  • Understand project management and requirements in bioinformatics gathering skills to allow them to interface and interact with computational and engineering expertise to help design solutions;
  • Have experience and training utilizing modern frameworks for rapid prototyping, and how to extract information from a wide variety of databases;
  • Understand core responsibilities towards data security, privacy, and data sharing spanning open access frameworks to restricted and regulated frameworks;

What is the learning environment like?

This program uses both traditional classroom-based teaching and applied in-silico laboratory for assignments that are coupled with additional online materials. Bioinformatics, after all, is about working mostly on computers with a community that spans the world for help.  Within the program, each class varies.

Most courses alternate between online interactions with faculty followed by in-class lectures and laboratories.  The class is often focused on helping students apply concepts that were made available outside of the classroom.  Fundamentally, this is an applied program where the focus is on learning to become independent and solve new problems as they emerge.  It teaches processes, though in a way that is effectively learning by example.  For example, several courses have a strong inclusion of R, R-markdown, and R-shiny, where students develop web-applications to complete homework by submission via GitHub. These applications may include biomedical research or clinical problem commonly seen in the field.  Classroom time is often used for working with teams of students on their solutions and suggesting paths through obstacles.  In-person, classes are often interactive with students and lectures engaged in an ongoing dialogue where lecture materials were already made available and reviewed prior to the course.  Students who succeed use both online resources and in-person classroom time.

How does this program compare with computational biology or Ph.D. programs?

This is a critical differentiator and one that is behind the tremendous need for master's level bioinformaticians.

First, one must understand what this program is not.  It is not a program that teaches theory and also on the development of new tools to solve biomedical problems. This area is best described as computational biology and many great Ph.D. programs serve this area.   For example, computational biologists may develop a new alignment tool. They require strong algorithmic and software engineering foundations.  There are many tremendous programs that serve this area - and many at USC.

Master's level bioinformaticians focus on applying or building from existing tools to biomedical problems. They uniquely understand both the scope and types of bioinformatics tools available and are often linking together different tools into frameworks, platforms, and pipelines. They understand the context of the biomedical problem faced by their team members, often because they were in the laboratory and have a good appreciation for disease or clinical care.  Very simply, bioinformaticians are applied and are able to adapt and put different tools together, preferring existing, established, and validated frameworks.  They focus on quality control and best-practices and find themselves more often in applied settings working with real patient data or building frameworks that impact directly human health and disease.  Frameworks built by bioinformaticians are typically specific for a groups use, and go well beyond simple running software, but do take a deep understanding of how these tools are made, validated, and versioned.  Bioinformaticians know and understand the rules and regulations for managing data relating to human subjects - both in research and in the clinical care stream.