Masters in Translational Biomedical Informatics
Keck’s MS in Translational Biomedical Informatics can be your career at the intersection of health, science, and informatics. Whether you are a lab technician hoping to take your career to the next level, or a recent graduate looking for an alternative career in the biomedical sciences, our hands-on learning program can help you find your path. You’ll learn how to apply and use existing biomedical informatics tools to solve complex biomedical problems. You’ll learn how to work within complex regulatory environments developing solutions to improve human health and disease from datasets that are both small and massive.
Students in this program will gain an understanding of:
- Best practices for using existing tools and bioinformatics 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 in bioinformatics 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;
Bridging the Gap
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. 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 uses both traditional class-room based teaching and applied in silico laboratory coupled for an integrated learning experience. 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.
Training in Applied Bioinformatics
This program uses both traditional class-room based teaching and applied in silico laboratory for assignments that are coupled with additional on-line materials. Bioinformatics after all is about working mostly on computers with a community that spans the world for help!
Courses alternate between online interactions with faculty followed by in-class lectures and laboratories. 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.
Biomedical Informatics is an exploding field that can lead to careers in hospitals, clinical labs, research institutes & companies, and biotechnology companies.
Bioinformatic hiring initiatives can be found in such as Illumina, AstraZeneca, Covance, Eli Lilly, Genentech, Invitae, Johnson and Johnson, Loreal, Pfizer, Roche.
Bioinformaticians are in high demand in most research institutes and medical schools with focuses on life sciences. Hospitals and clinical labs – particularly those introducing high-complexity molecular testing – are part of a new opportunity supplementing existing tracks within hospital laboratory IT tracks.
- Biologists - Biomedical engineers - Medical students - Industry professionals
This program is tailored for individuals with laboratory-based biomedical experience and who have bachelors in biomedical sciences or biomedical engineering. This program focuses on tool application and integration along pipelines, will scripting emphasized over coding. This program is not for existing software engineers, computational biologists interesting in developing novel algorithms, but rather on the use and application of existing tools and standards within human health and disease. Graduates will have the analytical capabilities for analyzing datasets across molecular biology, systems biology, structural biology, and genomic datasets. They’ll understand complex regulatory environments and the unique requirements for working in a biomedical setting.
Empowering users through genomics and bioinformatics
The plating prowess and bioinformatic brains in the halls of Norris are available for the entire Keck academic and clinical community. “We want to leverage our strengths to increase the research potential of departments in a variety of disciplines, and to assist patients across the disease spectrum,” says Director John D. Carpten.
One of the ways this will be achieved is through the newly launched Keck Genomics Platform. Under the direction of Zarko Manojlovic, Ph.D., the service offers a range of high-tech, high-throughput sequencing, as well as the bioinformatic backbone and support to be able to interpret the data generated. Technicians at the center also have expertise in preparing complex and fragile samples, allowing researchers and physicians to test materials that have previously been considered unusable.
Together with David W. Craig, his team is working to create a cradle-to-grave next-generation sequencing data management and bioinformatics platform that focuses on downstream interpretation and accelerates discovery through close collaborations between experimental and informatic groups.
Time Commitment and Graduation Paths
A total of 28 units is needed to graduate. Most students take about 1.5 years, though it is possible to finish in 1 year whereas others take 2 years. A major distinction between the timelines is the extent that a student becomes involved in a lab or internships that go beyond the 4 units of a capstone project. These internships and laboratory-based opportunities provide real-world training environments facilitating transitions to Ph.D. programs or industry positions.
Capstone projects are a key part of the experience - courses can provide a foundation but really becoming emersed in a focus real sets students apart. A wide variety of laboratories inside and outside of the department provide capstone experiences. These can include internships at USC, but many students leverage and work within the program such that their capstone includes industry experience.
To facilitate capstone experiences, most courses are synchronized to be on Tuesday/Thursdays, allowing Monday/Wednesday/Friday for course work and becoming involved in capstone projects.
A 1 Year Intensive Plan
- TRGN510 Foundations (4 Units)
- TRGN514 Genomic Analysis I (4 Units)
- TRGN524 Genome Methods I (4 Units)
- TRGN515 Genomic Analysis II (4 Units)
- TRGN516 Databases and Datastructures (4 Units)
- Elective (4 Units)
- TRGN520 Capstone
This path is often preferred by those who already have their foot in the door in bioinformatics and are looking for a formal degree.
A 18 Month Deep Dive Plan
- TRGN510 Foundations (4 Units)
- TRGN514 Genomic Analysis I (4 Units)
- TRGN515 Genomic Analysis II (4 Units)
- TRGN516 Databases and Data structures (4 Units)
- TRGN525 Elective (4 Units)
- TRGN520 Capstone (4 Units)
In this preferred path, students are often involved in laboratories that lead to papers or other industry internships.
- 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;
General requirements include at least 28 units of required courses as follows:
CORE LECTURE COURSES (REQUIRED: 24 UNITS)
TRGN-510. BASIC FOUNDATIONS IN TRANSLATIONAL BIOMEDICAL INFORMATICS (4 UNITS).
The goal of this introductory platform course is to teach core fundamentals that will allow a someone trained in biology or medicine how to use modern computing and bioinformatics tools to rapidly and reproducibly answer biological questions within an applied setting. The focus is significant on how researchers can use existing tools together to explore novel biomedical questions in ways that retain reproducibility. This course is for all students to have the core fundamentals for the rest of the program and will have bridge together courses that form the Masters in Translational Biomedical Informatics program. Please be aware the students are expected to have a Mac laptop with Sierra or later operating system installed for enrollment.
This course is a core requirement but may be substituted with INF 510 Principles of Programming for Informatics. The INF510 course provides a more focused training specific to python whereas the TRGN510 focuses more on the use of R, bash, and other scripting languages including python in the context of biomedical applications. For more information on INF510, please see https://classes.usc.edu/term-20161/course/inf-510/
TRGN-514. INTRODUCTION TO HUMAN GENOMIC ANALYSIS METHODS (4 UNITS).
This course is part of a two-course series and complements courses offered as part of a master's in biomedical informatics. This course is necessary to both teach modern genomics analysis, but to provide students with the broader skillset to adapt and grow in the field as technologies change. More than most fields, they will frequently change tools and frequently build single-use solutions. This course will focus on implementing, versioning, best practices, planning, and delivery specific to translational research by example using a series of emerging methodologies. Please be aware the students are expected to have a Mac laptop with Sierra or later operating system installed for enrollment.
This course is a core requirement
TRGN-515. ADVANCED HUMAN GENOMIC ANALYSIS METHODS (4 UNITS).
This course is part two of a two-course series and complements courses offered as part of a master's in biomedical informatics. This course will continue the process of both teaching modern genomics analysis while providing students with the broader skillset to adapt and grow in the field as technologies change. Students will learn the fundamentals of genomics, transcriptomics, proteomics, and epigenomics technologies and will learn how their application and use drive analytical problems. Students will be expected to be familiar with and now experienced with many foundational skillsets introduced in earlier courses that are necessary for biomedical informatics. This course continues to build those by reinforcement with an increased focus on timeliness and flexibility within the more complex analysis. Please be aware the students are expected to have a Mac laptop with Sierra or later operating system installed for enrollment.
This course is a core requirement
TRGN-516. TRANSLATIONAL GENOMICS, APPLIED DATABASES AND DATA STRUCTURES (4 UNITS)
The objective of this course is to provide advanced bioinformatics training in the use of databases and development of databases for sharing results and tracking information. The course will cover how to work with databases and understanding the regulatory environment around their use. A major part of this course will be on applied projects wherein teams students will be asked to use a case-study based approach to identify appropriate datasets, use analytic tools to analyze data, evaluating hypotheses, and interpret results. The first major foci are the current standards and key resources in human annotation and gene ontology. Please be aware the students are expected to have a Mac laptop with Sierra or later operating system installed for enrollment.
This course is a core requirement but may be substituted with INF 550 Overview of Data Informatics in Large Data Environment with prior permission. The TRGN516 course is focused on biomedical applications and the management of biomedical data, particularly within a healthcare context. INF550 provides a deeper technical view using applications that are much broader. In that context, TRGN has a narrower focus on healthcare applications and the associated regulated frameworks, whereas INF550 provides a deeper technical basis within databases and data structures.
TRGN-520. TRANSLATIONAL BIOMEDICAL INFORMATICS CAPSTONE PORTFOLIO (AT LEAST 4)
This course will provide students the opportunity to build a portfolio in the form of a web-based application that can capture the projects developed and completed through this course, and also show-cases one larger cap-stone project. The overall objective is to provide students provides the culminating, integrative curricular experience and an overarching project tailored to the career direction they are targeting and build a reactive widely accessible “WebApp” that showcases their project.
ELECTIVES (AT LEAST 8)
PM 570 Statistical Methods in Human Genetics. (4)
An introductory course in the statistical methods used in the analysis of human genetic data.
PM 538 Introduction to Biomedical Informatics. Overview of current topics, enabling technologies, research initiatives, and practical considerations in biomedical informatics.
BME 528 Medical Diagnostics, Therapeutics and Informatics. Picture archive communication system (PACS) design and implementation; clinical PACS-based imaging informatics; telemedicine/teleradiology; image content indexing, image data mining; grid computing in large-scale imaging informatics; image-assisted diagnosis, surgery and therapy.
DSCI 510 Principles of Programming for Informatics. Programming in Python for retrieving, searching, and analyzing data from the Web. Programming in Java. Learning to manipulate large data sets.
DSCI 550 Overview of Data Informatics in Large Data Environments. This course is one of the foundation courses in the Informatics program. It also exposes students to the cutting-edge data management concepts, systems, and techniques for managing large scale of data, to ensure that students have adequate background for further exploring big data analytics in follow-up courses.
DSCI 500 Neuroimaging and Systems Neuroscience. Overview of elemental neuroanatomy and brain systems with an emphasis on a neuroimaging perspective in the human and mouse. Open only to Neuroimaging and Informatics majors.
DSCI 540 Neuroimaging Data Processing Methods. Comprehensive investigation of data processing methods, software strategies, and workflow design and execution methodologies.
TRGN526 - Clinical Bioinformatics in Genomic Testing (2) Covers basic understandings of clinical bioinformatics methodologies and practices, along with the genomic technologies used for clinical diagnostic purposes.
TRGN 524 Core Principles In Biotechnology I (4) It will introduce students to tools and applications that will be instrumental throughout the Biomedical Bioinformatics and Translational Biotechnology Masters programs. This course targets individuals who have some previous training in biomedical sciences, and aims to provide them with the foundations, basic principles, and core concepts in biotechnology and its applications to basic science, health and disease.
TRGN 539 Translational Biotechnology Practicum (2 – 4 units/semester, max. 4) Students enrolled in the Translational Biotechnology program are required to engage in a practical project to be conducted in research laboratories or corporate environment under the supervision of USC faculty and corporate mentors/liaisons. Students, under the advisement from program director and faculty, can choose to work in academic or industry setting, locally or globally. This course is a practical experiential training that will integrate elements of the Translational Biotechnology curriculum into an applied project, giving students hands-on experience in the biomedical, biotechnology and pharmaceutical fields. Students may work on a significant project related to their professional aspiration. The student and the mentor determine the nature and extent of this independent study. In some arrangement, the student may be assigned to work with an associate member of the mentor’s team, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the student’s progress and performance. A Translational Biotechnology faculty will coordinate this course. The coordinator is responsible for determining the appropriateness of the project in meeting degree requirements. The coordinator also serves as a liaison between the Translational Biotechnology program and the mentor.
PM 591: Machine Learning for the Health Sciences (4.0 units). Introduces Masters and Ph.D. students in the Health Sciences to Machine Learning methods and their Biomedical applications. Typically Spring
- TRGN 525. Foundations, Concepts, Core Principles In Biotechnology II
- PM 570 Statistical Methods in Human Genetics.
- PM 538 Introduction to Biomedical Informatics.
- BME 528 Medical Diagnostics, Therapeutics and Informatics.
- PM 570 Statistical Methods in Human Genetics.
- DSCI 510 Principles of Programming for Informatics.
- DSCI 549 Data Science
- DSCI 550 Overview of Data Informatics in Large Data Environments.
- NIIN 500 Neuroimaging and Systems Neuroscience.
- NIIN 540 Neuroimaging Data Processing Methods.
- PM 570 Statistical Methods in Human Genetics. (4)
- PM 538 Introduction to Biomedical Informatics. (4)
- PM 591: Machine Learning for the Health Sciences (4.0 units)
- TRGN526 - Clinical Bioinformatics in Genomic Testing (2) +Added
- TRGN539. Translational Biotechnology Practicum (2 - 4) +Added
- TRGN 524 Core Principles In Biotechnology I +Added, no longer core
- TRGN543 Biotechnology Entrepreneurship and Commercialization (2)
- TRGN544 Biotechnology Entrepreneurship and Commercialization II (2)
Additional courses may be used as substitutions (up to 1) with advisor approval.
Internship opportunities have been conducted through TRGN539 with advisor approval. Industry internships are one path, but a path that requires you to identify the sponsor. Previous students have found internships at various companies in the biomedical sector including Illumina, Sage, Foundation Medicine, Regeneron, among others. In each case, the student identified internships independently through various resources and websites, and there has been no singular path.
The capstone project is the culmination of your master’s degree in translational biomedical informatics.
There are four aspects:
- Identifying a Lab
- Capstone Enrollment
Importantly, it is the vehicle to gain applied experience working within the research field, and ideally, it will aid in future career steps. There is flexibility in the format and deliverables, though a requirement of a capstone proposal is required to enroll in the course.
Tools for Identifying A Lab
There is no single best way to find a mentor, though it is recommended you start the process after becoming nominally proficient in basic bioinformatic and computing tools. For many students, this will be after there the first semester, though for some it may be sooner depending on their individual backgrounds. Your department will be able to give some advice on this topic, and the process can vary tremendously depending on your long term goals. While for most the mentor will be a USC faculty member, it is possible for the mentor to be an outside individual to be considered on a case-by-case if certain requirements can be made (such as the ability to share openly your work and work product).
The starting point for many emails to prospective labs. You will want to introduce yourself and have a short 1 to 2 paragraph query and introduction. You will want to mention some of your bioinformatics expertise or focus (R/R Shiny, Databases, Unix, Next-generation Sequencing).
There is no single way to identify a mentor, but utilizing colleagues, the internet and going to presentations are foundational. Clearly, starting by looking at the research and faculty within the Department of Translational Genomics is a great starting point. The research faculty are:
- Brooke Hjlem, Ph.D.
- Troy McEachron, Ph.D.
- David Craig, Ph.D.
- Bodour Salhia, Ph.D.
- Enrique Velazquez, Ph.D. MPH
- John Carpten, Ph.D.
- Zarko Manolojvich, Ph.D.
- Xiaowu Gai, Ph.D.
Keck School of Medicine
There are many faculty that work closely with the department and many faculty at USC who would make excellent mentors. Collaboration is at the heart of translational genomics. Departments indicating strong interest in Translational Biomedical Informatics Students:
- Department of Medicine
- Best initial contact is Matthew Salomon who leads informatics and is aware of different groups who have needs
- Department of Psychiatry
- Specific requests for those interested in databasing with genomics by Steven Siegel
- Stem Cell
- Various individuals, but Andrew McMahon has opportunities both in his lab and in others. RNA-seq, ChIP-seq, ATAC-seq, single-cell RNA-seq, single-cell-ATAC-seq, and Hi-C to identify the molecular mechanisms regulating maintenance and commitment of stem/progenitor populations in generation and repair of the mouse and human kidney
- Zilka Institute
- Berislav V. Zlokovic or individual labs may be the best approach
- Xiaowu Gai is a great contact who may be aware of many opportunities
Faculty who have expressed interest in bioinformatic students:
|Adam de Smith, PhD||Genetic Epidemiology||Assistant Professor|
|Andrew P. McMahon, PhD||Stem Cell||Professor|
|Caryn Lerman||Cancer Center||Professor|
|Dana Goldman, PhD||Public Policy||Professor|
|Daniel J. Weisenberger, PhD||Medicine||Associate Professor|
|Daniella Meeker, PhD||Preventive Medicine||Assistant Professor|
|Darcy Spicer, MD||Medicine||Associate Professor|
|Darryl Shibata, MD||Pathology||Professor|
|Jaclyn A. Biegel, PhD||Pediatrics||Professor|
|Jerry SH Lee, PhD||Medicine||Associate Professor|
|Jonathan David Buckley, MD, PhD||Preventive Medicine||Professor|
|Juan Pablo Lewinger, PhD||Preventive Medicine||Assistant Professor|
|Julie E. Lang, MD||Surgery||Associate Professor|
|Juliet Ann Emamaullee||Surgery||Assistant Professor|
|Justin Ichida, PhD||Stem Cell Biology||Assistant Professor|
|Kimberly Siegmund, PhD||Preventive Medicine||Professor|
|Linda Michelle Polfus, PhD||Genetic Epidemiology||Assistant Professor|
|Matthew Salomon||Medicine||Assistant Professor|
|Michael Anthony Bonaguidi, PhD||Stem Cell Biology||Assistant Professor|
|Michael R. Lieber, MD, PhD||Pathology||Professor|
|Paul Marjoram, PhD||Preventive Medicine||Professor|
|Peter Kuhn, PhD||Broad CIRM Center||Professor|
|Pinchas Cohen, MD||Gerontology||Professor|
|Rangasamy Ramanathan, MD||Pediatrics||Professor|
|Ricky Bluthenthal, PhD||Institute for Health||Associate Professor|
|Sarah Hamm-Alvarez, PhD||Ophthalmology||Associate Professor|
|Shahab Asgharzadeh, MD||Pediatrics||Associate Professor|
|Steve Kay, PhD||Zilkha Neurogenetic||Professor|
|Steven Siegel, MD, PhD||Psychiatry||Chair|
|Timothy J Triche, MD, PhD||Pediatrics||Professor|
|W. Martin Kast, PhD||Molecular Microbiology||Professor|
|Wendy Cozen, DO, MPH||Preventive Medicine||Professor|
USC Health Science Profiles - Learn about Keck Faculty
An automated tool that has information about each faculty. This tool is great for doing searches within areas!
NIH Reporter: Identifies labs with funding
Often the labs with the greatest opportunities have NIH grants. This database is great - remember to limit to USC to see all the different labs with substantial NIH funding.
After working some time in your lab or research environment, you'll want to start defining what will be your Capstone deliverable. Remember, in the beginning, you want to learn and develop from the lab. The right capstone deliverable will typically be pretty clear after a few months, but it should be something that really showcases what you are learning and the experience you are gaining.
Your proposal will be turned in as a Github titled “capstone” in markdown format as the initiating
Readme.md. You will create the course PI as a collaborator. There is purposeful flexibility in the type of capstone projects & its deliverable. In the end, they should be done with a mentor. The experience of working within a lab is critical and the deliverable may vary. There will be some back-and-forth on the proposal to make sure expectations are clear. You will be notified by the program director by email when the proposal is accepted.
It is important to understand that the deliverable and timing of the capstone course can capture your entire time within the master’s program. For example, if you are presenting at a conference, you would include work done in all the semesters within the program.
Example deliverables include:
- Publications (e.g. Journal)
- Scientific Poster at a Major Conference
- Technical White Paper, e.g. Biorxv
- GitHub Repositories
There is not a requirement for a traditional academic style presentation, and various forms can be used including video, websites, the publication (PDF), and other media. Ideally, the presentation can be done in a way that retains your ability to show people the work in the future.
#Title Proposed Title #Student #Mentor John Doe, Ph.D. email@example.com#Proposed Work 150 to 250 Words #Proposed Deliverable 150 to 250 Words
Admission requirements include a minimum GPA of 3.0 and an undergraduate major in biological sciences, or at least 6 bioscience courses in the molecular, cellular, genetics and biochemistry topics.
Formal application via USC Office of Graduate Admission’s online portal is required.
- Completed online application.
- Statement of purpose (approximately 500 words): Tell us where you came from, where you are going to, and how enrolling in the Translational Biotechnology Program may help you reach your academic or career goals.
- Resume or CV: There is no specific format requirement for a Resume or CV.
- Letters of recommendation (minimum three): We accept letters from both academic and professional evaluators. At least two of the letters should address your bioscience aptitude. All letters must be on the letterhead of the organization or department.
- Transcripts from ALL previously attended post-secondary institutions regardless of whether a degree was obtained.
- Standardized test score (waved for 2021): A minimum score of 300 on the Graduate Record Examinations (GRE) General Test is required. In lieu of GRE, DAT (minimum 18), MCAT (minimum 28 pre-2015 or 505 post-2015) or USMLE may be submitted. Applicants beneath these standardized test score requirements or have not yet taken the exam are asked to consult with program director for advisement.
- International students whose first language is not English are required to provide evidence of English Proficiency: TOEFL (iBT) 90, with no less than 20 on each section, or IELTS 6.5, with 6 or above on each band.
- Students with strong bioscience preparation but with lower language proficiency scores could consider applying to USC’s International Academy Pre-master’s programs.
- Applicants considered for admission may be interviewed (in person or via video conferencing) with the program director and/or other faculty.
Ready to Apply?
COST OF ATTENDANCE AND FINANCIAL AID
Estimated budgets for tuition, fees, books, supplies, room, board, and other living expenses can be found here. Tuition is charged at the same rate for both in-state and out-of-state residents.
Students may apply for financial aid through USC Financial Aid Office, which provides additional information for federally backed student loans, private financing and a federal work-study program. Limited funding opportunities can be found at the Office of Academic Honors and Fellowships and USC Graduate School.
USC OFFICE OF INTERNATIONAL SERVICES
The Office of International Services (OIS) provides continuing support services for international students. Professionally trained counselors and student peer counselors are available to advise international students on important issues such as immigration regulations, academic progress, financial concerns, housing and cross-cultural adjustment. Through this office, students can receive information about social and cultural activities.
Applications Due April 1st, 2020. Early Notifications for those applying before April 1st, 2021.
MS In Translational Biomedical Informatics
Keck School of Medicine of USC
1450. Biggy Street, 2nd Floor
Los Angeles, CA 90033