The Graduate Group in Computational and Genomic Biology provides integrated training in computational and genomic biology by drawing on students with diverse backgrounds and various interests.

This specialization allows tremendous flexibility in course sequences and there are several paths you can take to fulfill DE requirements. Based on background and future interests, students can customize courses to accomplish individual research objectives. Click here to view specific DE Courses and learn more about course requirements. Below are examples of customized Courses of Study by current graduate students:

PhD in Molecular and Cell Biology Year One: Computer Science 294: Special Topics-Algorithms in Computational Molecular Biology Molecular & Cell Biology 211: Introduction to Structural Biology and Physical Biochemistry Molecular & Cell Biology 240: Advanced Genetic Analysis Molecular & Cell Biology C246: Topics in Computational Biology and Genomics Statistics 141C: Statistics for Bioinformatics Year Two: Public Health 243A: Special Topics in Biostatistics Year Three: Computer Science 281: Introduction to Probabilistic Graphical Models

"Berkeley is a unique place to be at this genomic era. The opportunities are endless for graduate students interested in computational biology and genomics. The interdisciplinary nature of the Berkeley campus nurtures formation of stimulating and productive collaborations. Graduate students have access to wisdom and advice of world-wide famous statisticians, biologists, computer scientists and many other top researches from other fields."

PhD in Molecular and Cell Biology Year 1: Molecular & Cell Biology 200: Advanced Biochemistry and Molecular Biology Molecular & Cell Biology 240: Advanced Genetics Analysis Molecular & Cell Biology 230: Advanced Developmental Biology Year 2: Engineering 77: Introduction to Computer Programming for Scientists and Engineers Math 1B: Calculus Computer Science 9C: C for Programmers Computer Science 61A: The Structure and Interpretation of Computer Programs Math 55: Discrete Mathematics Computer Science 61B: Data Structures Year 3: Math 53: Multivariable Calculus Statistics C141: Statistics for Bioinformatics Year 4: Math 54M: Linear Algebra and Differential Equations with Computers Molecular & Cell Biology 290: Graduate Seminar-Developmental Biology Seminar

"Life as a grad student at Cal is very stimulating. UC Berkeley is known as one of the scientific epicenters of the world. As my research project has grown and evolved into new areas, I have been able to take advantage of the expertise present on campus in ways I could not have foreseen. For example, when we decided that it would be nice to experimentally verify some of our computational predictions, we did not have to even leave the building to find an expert in the area of RNA processing (Don Rio)."

Research Summary
By analyzing EST sequences, we discovered that alternative splicing frequently generates targets for nonsense-mediated mRNA decay (NMD). This coupling of alternative splicing and NMD may represent a widespread mode of post-transcriptional gene regulation. In collaboration with experimentalists, we are verifying selected targets. In addition to increasing the understanding of how the affected genes are regulated, our results raise important questions about the roles and evolutionary histories of alternative splicing and NMD.

For images, see:
http://compbio.berkeley.edu
/people/ed/rust/

http://compbio.berkeley.edu
/people/ed/SeqCompEval/

"The campus-wide seminars are not only the best way to get familiar with the most exciting, challenging topics of computational biology and genomics but also provide means to socialize with fellow students who share similar research interests. Through these seminars, some of which are organized by the graduate students themselves, students have the opportunity to meet and learn from top researchers. As someone with mathematical background, I benefit enormously from having friends who are well-trained and among the top for consulting on molecular biology and genomics. Berkeley campus offers an international, stimulating, friendly and fun atmosphere for doing research in computational biology and genomics."

PhD in Biostatistics Year 1: Industrial Engineering 263A,B: Applied Stochastic Processes I and II Statistics 210A,B: Theoretical Statistics Statistics 260: Topics in Probability and Statistics Statistics C261: Quantitative/Statistical Research Methods in Social Sciences Public Health 249: Biostatistical/Epidemiologic Data Applications Public Health 295: Biostatistics Seminar Public Health 243B: Special Topics in Biostatistics (Causal Inference: theory and applications) Public Health 292: Master of Public Health (M.P.H) Seminar Public Health 296: Special Study (Lab for PH243B) Year Two: Public Health 240: Risk Research Methods Statistics 230: Linear Models Statistics 243: Introduction to Statistical Computing Statistics 296: Resources for Statistical Computing Statistics 260: Topics in Probability and Statistics Public Health 240B: Biostatistical Methods- Survival Analysis and Casuality Public Health 292: Master of Public Health (M.P.H.) Seminar Public Health 295: Seminars

Year Three: Statistics 241A: Statistical Learning Theory Statistics 241B: Advanced Topics in Learning and Decision-Making Public Health 243A: Special Topics in Biostatistics: Analysis of Censored Data and Causal Inference Molecular and Cell Biology C246: Special Topics-Topics in Computational Biology and Genomics Year Four: Computer Science 294: Special topics in Machine Learning Public Health 243A: Special Topics in Biostatistics- Multivariate Statistical Methods for Computational Biology