Graduate

Covers effective writing styles for scientific communication for bio-science and engineering graduate students. Covers instruction for writing grant applications, scientific manuscripts, and thesis proposals. Students practice by preparing, editing, and evaluating each of these documents.

Covers bioinformatics models and algorithms: the use of computational techniques to convert the masses of information from biochemical experiments (DNA sequencing, DNA chips, and other high-throughput experimental methods) into useful information. Emphasis is on DNA and protein sequence alignment and analysis.

Detailed insight into the techniques and technological trends in genomics and transcriptomics, building the necessary foundations for further research in genetic association studies, population genetic association studies, population genetics, diagnostics, medicine, and drug development. Students should already have a deeper understanding of the basic tools of molecular biotechnology than acquired in introductory courses in biotechnology, biochemistry, and molecular biotechnology.

Investigates the mind as an adaptive system, the evolutionary history of the brain, and mathematical theories of knowledge representation and learning in humans and machines. Students build Jupyter notebooks that interact with human cerebral cortex organoids to explore adaptive processes in neural circuits. Taught in conjunction with BME 118. Students cannot receive credit for this course and BME 118.

Focuses on established and novel strategies for protein and cell engineering. Explores concepts, design, and practical applications of engineered proteins, cells, and organisms as research tools and in therapeutic applications. Recommended for graduate students with interests in bioengineering.

Introductory and intermediate-level topics in computational genomics, DNA and RNA sequence analysis, mapping, quantification, detection of variants and their associations with disease. Covers topics in machine-learning, probabilistic graphical models, gene regulatory network inference, and single cell analysis. Students conduct related independent research.

Covers advanced topics in computational genomics, DNA and RNA sequence analysis, mapping, quantification, detection of variants and their associations with disease. Topics include machine-learning, probabilistic graphical models, gene regulatory network inference, and single cell analysis. Students participate in teams in a computational analysis competition.

Covers major recent advances in evolutionary genomics. Students learn to analyze and interpret scientific writing in depth. Students also present on work covered in the class and produce one research or review paper. Students may not receive credit for this course and course 132.

Teaches methods for RNA gene discovery; gene expression quantification; probabalistic modeling, secondary structure/trans-interaction prediction; mRNA splicing; and functional analysis. Emphasis on leveraging comparative genomics and employing high-throughput RNA sequencing data. Includes lectures, scientific literature discussion, problem sets, and final gene-discovery project.

Python and its Numpy, Scipy, and Matplotlib packages as well as Inkscape are used to generate publication quality figures from scientific data. Students cannot receive credit for this course and course 163.

Focuses on modern "precision" approaches to understanding human health, where every patient is unique. Explores basic and clinical discoveries and 'omics-based medicine for the prevention, diagnosis, and treatment of disease. Emphasis is on genomic approaches and applications to cancer.

Course focuses on the application of epi/genomic technologies of stem cells. Topics include 'omics to interrogate pluripotency, cell lineages, hierarchies, trajectories, epigenomic reprogramming. Class includes hands-on computational analysis of epi/genomics data. For graduate/postdoctoral trainees engaged in stem cell research. Enrollment is restricted to graduate students and by permission of the instructor. Basic programming knowledge is recommended.

Focuses on contemporary issues in commercializing biotechnology and genomics, emphasizing development of teamwork and communication skills. Topics include intellectual property management, fundraising, market analysis, and technology development as related to biotechnology start-ups. Students perform real-world tasks preparing for commercialization. Taught in conjunction with Biomolecular Engineering 175.

Basic stem cell concepts, experimental approaches, and therapeutic potential are discussed. Students gain experience in reading and critically evaluating the primary scientific literature. Students cannot receive credit for this course and BME 178.

Weekly seminar series covering topics of current research in computational biology, and bioinformatics. Current research work and literature in these areas are discussed. Short papers reflecting on presentations required. Available for Satisfactory/Unsatisfactory (or Pass/No Pass) grading only.

Weekly seminar series covering experimental research in nanopore technology and single-molecule analysis of polymerase function. Current research work and literature is discussed. Students lead some discussions and participate in all meetings.

Presents current computational biology research to identify genomics-based signatures of cancer onset, progression, and treatment response. Examples of such investigations include: genetic pathway interpretation of multivariate high-throughput datasets; discovery of mutations in whole-genome sequence; identifications and quantification of gene isoforms, alleles, and copy number variants; and machine-learning tools to predict clinical outcomes. Students present their own research, host journal clubs, and attend lectures and teleconferences to learn about research conducted by national and international projects.

Weekly seminar series covering experimental research in protein structure, function, and engineering. Current research work and literature in this area are discussed. Students lead some discussions and participate in all meetings.

Current topics in genomics including high-throughput sequencing, genome assembly, and comparative genomics. Students design and implement independent research projects. Weekly laboratory meetings are held to discuss these projects and related research in the field.

Weekly seminar covering topics in current research on blood cell development and stem cell biology. Current research and literature in these areas discussed. Students lead some discussions and participate in all meetings.

Weekly seminar series covering topics of current computational and experimental research in live cell biotechnology. Current research work and literature in this area are discussed. Students lead some discussions and participate in all meetings. (Formerly offered as Seminar in Comparative Genomics.)

Research seminar of the UCSC Computational Genomic Laboratory and Platform Teams. Students receive hands-on instruction in modern computational methods to address research questions. Topics include: genomic and transcriptomic sequence analysis methods, comparative and evolutionary genomics, big-data genomic analysis, biomedical data sharing, and precision medicine. Students attend and participate in monthly lab meetings, monthly all-hands meetings, where students give a quick report on their progress and the next month's goals, and a bi-weekly journal club, where pairs of students present and discuss a paper of their choosing with the lab. Students also participate in hands-on, active computational laboratory-based research. Student evaluation is based on suitable progress toward research goals and graduate program progress.

Weekly seminar series covering topics and experimental research in computational genetics. Current research work and literature in this area discussed. Students lead some discussions and participate in all meetings.

Covers current topics in computational and experimental research in transcriptomics. Current research work and literature discussed. Weekly laboratory meetings held to discuss these projects and related research in the field.

Weekly seminar covering topics of research in the development of new tools and technologies to detect and study genes and proteins. Latest research work and literature in these areas are discussed. Students lead some discussions and participate in all meetings.

Weekly seminar series covering topics in research on stem cell genomics. Current research and literature in this area is discussed. Students lead some discussions and participate in all meetings.

Weekly seminar series covering topics of current computational and experimental research in computational functional genomics. Current research work and literature in this area discussed. Students lead some discussions and participate in all meetings.

Journal club and research presentations in immunogenomics. Enrollment is by consent of the instructor and is restricted to graduate students, juniors, and seniors.

Seminar course consisting of the Russell Lab's weekly meeting to cover lab business, present and discuss new results, practice presentations, and read recent scientific literature. May be repeated for credit. Enrollment is restricted to graduate students and is by permission of the instructor.

Covers major recent topics in evolutionary and population genomics. Consists primarily of discussions of recent literature and updates on group members' research. Enrollment is available only to members of the Corbett-Detig laboratory.

Weekly seminar series covering topics of bioinformatics and biomolecular engineering research. Current research work and literature in this area discussed. Students lead some discussions and participate in all meetings.

Independent research in bioinformatics under faculty supervision. Although this course may be repeated for credit, not every degree program accepts a repeated course toward degree requirements. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Although course may be repeated for credit, not every degree program accepts a repeated course toward degree requirements. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Although course may be repeated for credit, not every degree program accepts a repeated course toward degree requirements. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Although course may be repeated for credit, not every degree program accepts a repeated course toward degree requirements. Students submit petition to sponsoring agency.

Independent study or research under faculty supervision. Although course may be repeated for credit, not every degree program accepts a repeated course toward degree requirements. Students submit petition to sponsoring agency. Enrollment is restricted to graduate students.

Thesis research conducted under faculty supervision. Although course may be repeated for credit, not every degree program accepts a repeated course towards degree requirements.Students submit petition to sponsoring agency.

Thesis research conducted under faculty supervision. Although course may be repeated for credit, not every degree program accepts a repeated course towards degree requirements.Students submit petition to sponsoring agency.

Thesis research conducted under faculty supervision. Although course may be repeated for credit, not every degree program accepts a repeated course towards degree requirements.Students submit petition to sponsoring agency.