Computational Biology and Bioinformatics

The University of Southern California offers a comprehensive Master of Science in Computational Biology and Bioinformatics designed to equip students with the essential skills and knowledge required to excel at the intersection of biology, computer science, and data analysis. This program provides a rigorous curriculum that combines theoretical foundations with practical applications, preparing graduates for diverse careers in research, industry, and healthcare. Students will gain expertise in molecular biology, genomics, proteomics, and systems biology while developing advanced programming skills, statistical analysis techniques, and experience with high-throughput data analysis. The curriculum emphasizes competency in algorithm development, machine learning, and data visualization, enabling students to analyze complex biological datasets effectively. In addition to coursework, students benefit from hands-on research projects, internships, and collaboration opportunities with leading faculty and industry partners. The program aims to foster interdisciplinary thinking, innovation, and problem-solving skills to address some of the most pressing challenges in biomedical research, personalized medicine, and biotechnology. Graduates will be well-prepared for careers as bioinformaticians, computational biologists, data scientists, or further academic research. Through a diverse array of courses and research opportunities, the USC Computational Biology and Bioinformatics program encourages students to explore cutting-edge topics such as structural bioinformatics, drug discovery, and systems medicine, ensuring they are at the forefront of the rapidly evolving life sciences field.

The students must complete, with a “B” average, a minimum of 60 units of courses carrying graduate credit and approved by the guidance committee. The required courses include: BISC 542, CSCI 570, MATH 505A, MATH 541A, and MATH 578AB, in which students need to pass with “B” or above. The students must take at least one biology course in the area of molecular biology, genetics, or biochemistry. An additional 6 units of elective courses will be taken from the following list: BISC 502AB, BISC 505, BISC 510AB, MATH 502A, MATH 505B, MATH 541B, MATH555A, MATH 650, CSCI 521, CSCI 567, CSCI 670, CSCI 559 or other courses that are approved by the student’s advisor. Students must register for a minimum of 4 units of dissertation research (BISC 794ab). Students must be registered in BISC 542 (Computational Section) their first 3 years in the program (6 semesters). Students are required to enroll in BISC 593 (2 units) during the fall semester of their second year for TA preparation taken before or concurrently with a first TA assignment.

1st Year Fall

• MATH 505A   Statistics and probability theory: The course introduces the basic concepts of probability theory, various discrete and continuous random variables, law of large numbers and the central limit theorem (CLT), basic tools of probability theory. To get familiar with these tools many applications in Physics and Biology will be considered,.

• CSCI 570 Algorithm design and analysis: The course teaches design and analysis of algorithms. The main focus is on developing an understanding for the major algorithm design techniques. The practical side of algorithm design is also explored with examples in solving industry problems.

• BISC 542  Seminar in Computational Biology/Computational Biology Journal Club: Graduate students present their own research (if published or close to publication) or chosen publications from other researchers in the broad field of computational biology and bioinformatics, molecular and structural biology. Students practice analytical reading and oral presentation skills. Attending the departmental seminar series in Computational Biology is part of this course.

• BISC 502A Molecular Genetics and Biochemistry: Current genetic and biochemical analysis of replication, recombination, mutagenesis, and repair. Fundamentals of transcription and regulation of gene expression. Recent applications of genetic engineering and genome analysis.

                                                 Or

• BISC 320 Molecular Biology: Structure and synthesis of nucleic acids and proteins; molecular biology of prokaryotes and eukaryotes; genetics. The student will learn the structure and function of biological macromolecules, in particular nucleic acids (DNA and RNA) and proteins and how these molecules act to copy, express and accurately transmit genetic information. The course focuses on mechanisms of: DNA replication, transcription, translation (protein synthesis) and the genetic code, DNA repair, recombination and DNA rearrangements. Techniques used to study molecular biology are presented in the context of these major biological mechanisms.

                                                 Or

• BISC 325 Genetics: Transmission genetics and genotype/phenotype; mapping methods; complex traits; genetics of human disease and population genetics. The aim of this course is to introduce students to the fundamental aspects of genetics, from the molecular level to the level of the organism and populations, including:  1) Fundamentals of gene structure, function, and transmission. 2) Methods of genetic manipulation, 3) Systems genetics, 4) Genetic analysis of populations and evolution

1st Year Spring

• MATH 578A Computational Molecular Biology: Applications of the mathematical, statistical and computational sciences to data from molecular biology. Algorithms for genomic sequence data: sequence and map assembly and alignment, RNA secondary structure, protein structure, gene-finding, and tree construction.
• MATH 541A Introduction to Mathematical Statistics: Parametric families of distributions, sufficiency. Estimation: methods of moments, maximum likelihood, unbiased estimation. Comparison of estimators, optimality, information inequality, asymptotic efficiency. EM algorithm, jacknife and bootstrap.
• BISC 542  Seminar in Computational Biology/ Computational Biology Journal Club: Description as above
• BISC 577A Computational Molecular Biology Laboratory: Computational biology faculties teach current research topics.

2nd Year Fall

• MATH 578B Computational Molecular Biology: Applications of the mathematical, statistical and comutational sciences to data from molecular biology. Statistics for genomic sequence data: DNA sequence assembly, significance of alignment scores, hidden Markov models, genetic mapping, models of sequence evolution, and microarray analysis.
• ELECTIVE COURSE 1: The student can chose among a variety of courses, including: BISC 502AB, BISC 505, BISC 510AB, MATH 502A, MATH 505B, MATH 541B, MATH555A, MATH 650, CSCI 521, CSCI 567, CSCI 670, CSCI 559 or other courses that are approved by the student’s advisor.
• Computational Biology Journal Club: Description as above
• BISC 593 Practicum in Teaching the Biological Sciences: Practical principles for the long-term development of effective teaching within college disciplines. Intended for teaching assistants in Dornsife College.

2nd Year Spring

• BISC 542 Computational Biology Journal Club: Description as above 
• ELECTIVE COURSE 2: The student can chose among a variety of courses, including: BISC 502AB, BISC 505, BISC 510AB, MATH 502A, MATH 505B, MATH 541B, MATH555A, MATH 650, CSCI 521, CSCI 567, CSCI 670, CSCI 559 or other courses that are approved by the student’s advisor.

Lab Rotation: All first year students need to take two faculty lab rotations, one per semester, in the first year. Students are expected to regularly meet with the faculty and participate in the lab meetings during the rotation.

Screening Procedure

The screening examination should be taken by the end of the 2nd semester in the program. If the student fails the examination, the department, at its discretion, may permit the student to take it again during the next year. The screening examination consists of written examinations on topics including molecular biology, computer algorithms, and mathematical probability and statistics.

Summer Research and Choosing Advisor: All first year students need to make arrangements for summer research before May 1 of the same calendar year. Students should inform the graduate student faculty advisor of the name of the computational biology faculty with whom they will work in the summer. Students are allowed to choose a non-computational biology faculty member as a co-advisor but supervision by a computational biology faculty is required.

Oral Qualifying Examination

All students take the qualifying examination within two semesters following successful completion of the screening examination.

The written portion of the qualifying examination consists of a dissertation proposal. This document should include: introduction, statement of the problem, literature survey, methodology, summary of preliminary results, proposed research, references, appendix (including one or two fundamental references).

The oral portion of the qualifying examination consists of presentation of the Ph.D. dissertation proposal. The student must demonstrate research potential. The oral portion of the qualifying examination will take place at a special examination day, where all the students will present their oral dissertation proposals. The date of the examination day will be a year after the screening exams. The guidance committee should be provided with a draft of the proposal at least ten days prior to the date of the oral examination day.

Transfer of Credit

No transfer of credit will be considered until the screening examination is passed. A maximum of 30 units of graduate work at another institution may be applied toward the course requirements for the Ph.D. A grade of B- (A = 4.0) or lower will not be accepted and, at most, two grades of B will be accepted. A Ph.D. candidate may petition the department for transfer of additional credit, after he or she passes the qualifying examination.

Dissertation

Following passage of the screening examination and approval of a dissertation proposal by the guidance committee, the student begins research toward the dissertation under the supervision of the dissertation committee. Although any faculty member within MCB can serve as advisor, the guidance committee must include at least one faculty member whose primary focus is computational biology. The primary requirement of the Ph.D. is an acceptable dissertation based on a substantial amount of original research conducted by the student.

• Entry into the Computational Biology and Bioinformatics PhD program requires a bachelor's degree (or equivalent) in a related subject from an accredited four-year college, sitting the Graduate Record Examinations (subject test is not required), and three letters of recommendation.
• Transcripts from all colleges and universities attended
• Three letters of recommendation
• GRE scores
• TOEFL scores
• Online Application (We no longer accept paper applications)

Please attach an unofficial transcript, statement of purpose, and any other supplemental application materials as supplemental documents in your online application. Please mail official transcripts to this address.

Your statement of purpose for the Graduate School application should explain both why you wish to pursue graduate study in Molecular & Computational Biology and, more specifically, why you wish to do so at USC. We admit a small number of students each year in order to offer them full financial support; we therefore seek to find applicants whose interests best suit our program.

Unofficial copies of GRE (and TOEFL for international applicants) scores are acceptable for evaluation purposes, but if offered admission, an applicant will be required to send official documentation.

Please submit your letters of recommendation electronically through the Apply Yourself application system. When you have submitted the online application, arrange for your transcripts, GRE scores, and TOEFL  scores (for international applicants)  to be sent directly to us by mail to this address

NOTE: When listing undergraduate/graduate GPAs (grade point averages) on the application, please use the system of your university. If you received a percentage grade, provide the average percentage. If you were graded on a scale other than 4.0 (with 4.0 equal to an "A"), please indicate the average grade AND indicate the scale (for example, 8.5/10).

Application Fee

The Application Fee is $90.00.

Scholarships

It is the policy of the Computational Biology and Bioinformatics Program to provide support for all students throughout their graduate career. These awards are teaching and research assistantships (TA/RA), College Merit Fellowships, and Provost's Fellowships. There is no separate application. Students are also encouraged to apply for financial aid from appropriate federal agencies or other sources of aid for graduate study. U.S. citizens requesting additional need-based financial aid should complete that portion of the application.

Teaching and research assistantships provide full tuition remission, payment of student health center fee and student health insurance, and a monthly stipend for living expenses for the academic year. TAs split their time between specific teaching duties as determined by the department and their laboratory work. The amount of time devoted to teaching duties is no more than 20 hours per week. RAs devote most of their time in research leading to their dissertations. Continuing support from available RAships and TAships beyond the first year is contingent on the student maintaining the academic standards required by the program (not least of which is a 3.0 GPA).

Students generally obtain RAships support during the three summer months. TAships are limited during this period. Students should make arrangements with a faculty advisor during their first year to provide for this. This arrangement is usually not a problem. The university offers several awards open to all departments. The department nominates exceptional candidates for the College Merit Fellowships and Provost's Fellowships, the university's most prestigious award. These awards carry additional stipends and, in some cases, funding of research-oriented travel and supplies.

The Master of Science in Computational Biology and Bioinformatics at the University of Southern California is a comprehensive program designed to equip students with the essential skills and knowledge needed to excel in the rapidly evolving fields of computational biology, bioinformatics, and data science. The program emphasizes both theoretical foundations and practical applications, preparing graduates to analyze complex biological data, develop computational tools, and contribute to advancements in biomedical research and healthcare.

Students in the program gain a solid grounding in biological sciences, computer science, mathematics, and statistics. The curriculum covers key topics such as algorithms, machine learning, genomics, proteomics, systems biology, and data analysis techniques. Through coursework, students learn to implement computational methods to interpret large-scale biological datasets, including genomic, transcriptomic, and proteomic data. The program also fosters proficiency in programming languages commonly used in bioinformatics, such as Python, R, and C++.

Research opportunities are integral to the master's program, allowing students to collaborate with faculty on cutting-edge projects in areas like personalized medicine, drug discovery, and biological data visualization. The program encourages participation in interdisciplinary research teams, providing valuable experience in solving real-world biological problems via computational approaches.

The faculty comprises leading researchers in computational biology and bioinformatics, offering mentorship and guidance to students. USC’s strong ties to biomedical institutions and biotech companies facilitate internships, industry collaborations, and potential career placements for graduates. The program is designed to be flexible, accommodating both full-time and part-time students, with options for thesis or non-thesis tracks to suit individual career goals.

Graduates of the program are prepared for diverse careers including bioinformatics analyst, computational biologist, data scientist in healthcare, research scientist, or further academic pursuits in PhD programs. The program’s multidisciplinary nature ensures that students develop versatile skills applicable to academia, industry, and government agencies involved in biomedical research and health sciences innovation.

Overall, the USC Computational Biology and Bioinformatics master’s program offers a rigorous and interdisciplinary education, leveraging USC’s extensive research resources and industry connections to prepare students for impactful careers at the forefront of biological and computational sciences.