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

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:

1. Identifying a Lab
2. Proposal
3. Capstone Enrollment
4. Presentation/Delivery

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.

Capstones

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).

Department

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. 
• Bodour Salhia, Ph.D. 
• Enrique Velazquez, Ph.D. MPH
• 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
• CHLA
  • Xiaowu Gai is a great contact who may be aware of many opportunities

Faculty Collaborators

Faculty who have expressed interest in bioinformatic students:

Faculty/Link	Department	Position
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!

https://profiles.sc-ctsi.org/search/

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.

Proposal

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
• Web-sites

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.

Readme.MD

#Title
Proposed Title
#Student
#Mentor
John Doe, Ph.D.
john.doe@usc.edu#Proposed Work
150 to 250 Words
#Proposed Deliverable
150 to 250 Words