About the Translational Biomedical Informatics Master’s Program
The Department of Translational Genomics within USC’s Keck School of Medicine will be offering a Masters of Science (MS) in Translational Biomedical Informatics beginning with inaugural class in 2017. This intensive two-year MS program is focused on training individuals who have strong backgrounds in laboratory-based biomedical sciences and seek the 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.
The objective of this Masters In Translational Biomedical Informatics is to train applied bioinformaticians, providing students with the the training, skillsets, and best practices for applying and integrating existing bioinformatics tools in the study of human health and disease. This program is tailored for individuals with laboratory-based biomedical experience and whom have bachelors in biomedical sciences. 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 skillsets that remain relevant as new technologies emerge and change.
This program is taught within the Keck School of Medicine and USC, and focused for applied biomedical informatics for studying human health and disease. In preparation for spectrum of careers that span the research to the clinic, students will understand their critical role in working with data under a spectrum of regulatory bodies. They will learn their role in data management and sharing. They will learn how to develop interactive frameworks for working with experimentalist teams, allowing others to explore and interact with datasets in an agile and interactive manner. They will learn skills of project management, best-practices and version control, while also learning to function as a liaison between engineers and biologists developing specifications and standard operating procedures.
Students in this program will gain an understanding of:
- Best practices for putting existing tools and informatics datasets together to better understand biomedical problems;
- Analysis of next-generation sequencing (NGS) including whole-genome, exome, and transcriptome sequencing (RNA-seq), as well as emerging methods in single-cell sequencing;
- Project management and requirements gathering skills to allow them to interface and interact with computational and engineering expertise to help design solutions;
- Experience and training utilizing modern frameworks for rapid prototyping, and how to extract information from a wide variety of databases;
Core responsibilities towards data security, privacy, and data sharing spanning open access frameworks to restricted and regulated frameworks.
This program is taught within the USC Keck School of Medicine. While students will learn many bioinformatics methods online, this program will require attending classes in person in a modern classroom environment. Most courses utilize significant online content and resources. Students will be expected to have a 2013+ MacBook or MacBook Pro laptop without exception.
This program provides training in applied bioinformatics for biomedical settings. This program is not designed or suited for students that lack experience working within a biomedical laboratory, and presumes accepted students understand experimental design particularly in the biomedical sector. Accepted students will be expected to comprehend the overall goals of experiment, hypothesis building, and hypothesis testing. While students can select additional electives in statistics provided through other departments, applicants are expected to have a basic knowledge of biostatistics through either prior coursework or research experience.