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computational biology phd programs reddit

  • Computational Biology & Biomedical Informatics (PhD Program)

Computational biology and bioinformatics (CB&B) is a rapidly developing multidisciplinary field. The systematic acquisition of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation. Given the rate of data generation, it is well recognized that this gap will not be closed with direct individual experimentation. Computational and theoretical approaches to understanding biological systems provide an essential vehicle to help close this gap. These activities include computational modeling of biological processes, computational management of large-scale projects, database development and data mining, algorithm development, and high-performance computing, as well as statistical and mathematical analyses.

  • Programs of Study
  • PhD - Doctor of Philosophy
  • Yale Computational Biology and Bioinformatics
  • Computational Biology and Bioinformatics

Mark Gerstein

Director of Graduate Studies

Steven Kleinstein

Samantha Naziri

Departmental Registrar

Admission Requirements

Standardized testing requirements.

GRE is not accepted.

English Language Requirement

TOEFL iBT or IELTS Academic is required of most applicants whose native language is not English. BBS requires a score of at least 600 on the paper version, 250 on the computer-based exam, and 100 on the internet-based exam. Please take the test no later than November and no earlier than 24 months prior to submitting your application. Use institution code 3987 when reporting your scores; you may enter any department code.

You may be exempt from this requirement if you have received (or will receive) an undergraduate degree from a college or university where English is the primary language of instruction, and if you have studied in residence at that institution for at least three years.

Admission Information

The PhD program in Computational Biology and Bioinformatics participates in the Combined Program in the Biological and Biomedical Sciences (BBS) , and applicants interested in pursuing a degree in cell biology should apply to the Computational Biology and Biomedical Informatics Track within BBS.

Academic Information

Program Advising Guidelines

GSAS Advising Guidelines

Academic Resources

Academic calendar.

The Graduate School's academic calendar lists important dates and deadlines related to coursework, registration, financial processes, and milestone events such as graduation.

Featured Resource

Registration Information and Dates

https://registration.yale.edu/

Students must register every term in which they are enrolled in the Graduate School. Registration for a given term takes place the semester prior, and so it's important to stay on top of your academic plan. The University Registrar's Office oversees the systems that students use to register. Instructions about how to use those systems and the dates during which registration occurs can be found on their registration website.

Financial Information

Phd stipend & funding.

PhD students at Yale are normally fully-funded. During their programs, our students receive a twelve-month stipend to cover living expenses and a fellowship that covers the full cost of tuition and student healthcare.

  • PhD Student Funding Overview
  • Graduate Financial Aid Office
  • PhD Stipends
  • Health Award
  • Tuition and Fees

Alumni Insights

Below you will find alumni placement data for our departments and programs.

Graduate Programs

Computational biology.

The Center for Computational Molecular Biology (CCMB) offers Ph.D. degrees in Computational Biology to train the next generation of scientists to perform cutting edge research in the multidisciplinary field of Computational Biology.

During the course of their Ph.D. studies students will develop and apply novel computational, mathematical , and statistical techniques to problems in the life sciences. Students in this program must achieve mastery in three areas - computational science, molecular biology, and probability and statistical inference - through a common core of studies that spans and integrates these areas.

The Ph.D. program in Computational Biology draws on course offerings from the disciplines of the Center’s Core faculty members. These areas are Applied Mathematics, Computer Science, the Division of Biology and Medicine, the Center for Biomedical Informatics, and the School of Public Health. Our faculty and Director of Graduate Studies work with each student to develop the best plan of coursework and research rotations to meet the student’s goals in their research focus and satisfy the University’s requirements for graduation.

Applicants should state a preference for at least one of these areas in their personal statement or elsewhere in their application. In addition, students interested in the intersection of Applied Mathematics and Computational Biology are encouraged to apply directly to the  Applied Mathematics Ph.D. program , and also to contact relevant  CCMB faculty members .

Our Ph.D. program assumes the following prerequisites: mathematics through intermediate calculus, linear algebra and discrete mathematics, demonstrated programming skill, and at least one undergraduate course in chemistry and in molecular biology. Exceptional strengths in one area may compensate for limited background in other areas, but some proficiency across the disciplines must be evident for admission.

Additional Resources

CCMB computing resources include a set of multiprocessor computer clusters and data storage servers with 392 processors. The CCMB Cluster is the largest dedicated computing system on campus for computational biology and bioinformatics applications. See also answers to  frequently asked questions .

Application Information

Application requirements, gre subject:.

Not required

GRE General:

Personal statement:.

Applicants will be asked a series of short form questions regarding their interest in computational biology, their research experiences, and their goals for the future. 1) Describe the life experiences that inspired you to pursue a career in science. 2) Describe at least one research experience you have had that prepared you intellectually/ scientifically for a career in computational biology. 3) Explain at least one challenge you have overcome in life or research to pursue a scientific career and what you have learned from this experience. 4) Why would you like to pursue your PhD in the Brown CCMB program? (Include at least two faculty members who you would like to work with at Brown and why.) 5) Discuss how you aspire to contribute to our mission to promote diversity and inclusion through your research, teaching, or service.

Dates/Deadlines

Application deadline, completion requirements.

Six graduate–level courses, two eight–week laboratory rotations, preliminary research presentation, dissertation, oral defense

Contact and Location

Center for computational molecular biology, location address, mailing address.

Computational Biology PhD

computational biology phd programs reddit

The main objective of the Computational Biology PhD is to train the next generation of scientists who are both passionate about exploring the interface of computation and biology, and committed to functioning at a high level in both computational and biological fields.

The program emphasizes multidisciplinary competency, interdisciplinary collaboration, and transdisciplinary research, and offers an integrated and customizable curriculum that consists of two semesters of didactic course work tailored to each student’s background and interests, research rotations with faculty mentors spanning computational biology’s core disciplines, and dissertation research jointly supervised by computational and biological faculty mentors.

The  Computational Biology Graduate Group  facilitates student immersion into UC Berkeley’s vibrant computational biology research community. Currently, the Group includes over 46 faculty from across 14 departments of the College of Letters and Science, the College of Engineering, the College of Natural Resources, and the School of Public Health. Many of these faculty are available as potential dissertation research advisors for Computational Biology PhD students, with more available for participation on doctoral committees.

Graduate School

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Quantitative and Computational Biology

General information, program offerings:, department for program:, director of graduate studies:, graduate program administrator:.

The Program in Quantitative and Computational Biology (QCB) is intended to facilitate graduate education at Princeton at the interface of biology and the more quantitative sciences and computation. Administered from The Lewis-Sigler Institute for Integrative Genomics, QCB is a collaboration in multidisciplinary graduate education among faculty in the Institute and the Departments of Chemistry, Computer Science, Ecology and Evolutionary Biology, Molecular Biology, and Physics. The program covers the fields of genomics, computational biology, systems biology, evolutionary and population genomics, statistical genetics, and metabolomics and proteomics.

Program Highlights

An Outstanding Tradition:  Chartered in 1746, Princeton University has long been considered among the world’s most outstanding institutions of higher education, with particular strength in mathematics and the quantitative sciences. Building upon the legacies of greats such as Turing, von Neumann, Tukey, Compton, Feynman, and Einstein, Princeton established the Lewis-Sigler Institute of Integrative Genomics in 1999 to carry this tradition of quantitative science into the realm of biology.

World Class Research:  The Lewis-Sigler Institute and the QCB program focus on attacking problems of great fundamental significance using a mixture of theory, computation, and experimentation.

World Class Faculty:  The research efforts are led by the QCB program’s 50+ faculty, who include a Nobel Laureate, members of the National Academy of Sciences, Howard Hughes Investigators, and numerous faculty who have received major national research awards (e.g., NIH Pioneer, NIH Innovator, Packard, NSF PECASE, NSF CAREER, etc.).

Personalized Education:  A hallmark of any Princeton education is personal attention. The QCB program is no exception. Lab sizes are generally modest, typically 6 – 16 researchers, and all students have extensive direct contact with their faculty mentors. Many students choose to work at the interface of two different labs, enabling them to build close intellectual relationships with multiple principal investigators.

Stimulating Environment:  The physical heart of the QCB program is the Carl Icahn Laboratory, an architectural landmark located adjacent to biology, chemistry, physics, and mathematics on Princeton’s main campus. Students have access to a wealth of resources, both intellectual and tangible, such as world-leading capabilities in DNA sequencing, mass spectrometry, and microscopy. They also benefit from the friendly atmosphere of the program, which includes tea and cookies every afternoon. When not busy doing science, students can partake in an active campus social scene and world class arts and theater events on campus.

Program Offerings

Program offering: ph.d..

Core courses, QCB515, QCB537, QCB538, and COS/QCB551, are required for all students, as is a Responsible Conduct in Research (RCR) course, QCB 501. Three elective courses must be taken from the list below, including at least one from the quantitative course list and one from the biological course list. Courses not on the approved lists may be taken as electives with approval from the DGS.

Quantitative Courses (must take at least one)

Biological Courses (must take at least one)

Selected undergraduate courses of interest (Note: these do not count towards course requirements)

Additional pre-generals requirements

Research Colloquium: QCB Graduate Colloquium QCB Graduate Colloquium is a research colloquium that has been developed for QCB graduate students, held weekly on an afternoon during the fall and spring terms. First, second, and fourth-year graduate students have the opportunity to present their research to peers. 

Rotations All students are required to complete a minimum of three research rotations during their first year of graduate study, with a maximum of four, to explore possible research advisers.

General exam

The general examination is usually taken in January of the second year, and consists of an 8-10 page written thesis proposal and a two-hour oral exam on the student’s thesis proposal.

Qualifying for the M.A.

The Master of Arts (M.A.) degree is normally an incidental degree on the way to a full Ph.D. and is earned after a student successfully passes the general examination. It may also be awarded to students who, for various reasons, leave the Ph.D. program, provided the student has completed all coursework, pre-generals requirements, and the written portion of the generals examination.

A student must teach a minimum of one full-time assignment or teach two part-time assignments. Students will typically teach in year 4 of the program.

Post-Generals requirements

Committee Meetings Research progress is overseen by a thesis committee selected by the student after passing the general exam. The committee consists of the thesis adviser(s) and two additional faculty members. At least one member must be QCB faculty. The thesis committee must be approved by the DGS. Annual thesis committee meetings are mandatory. 

Dissertation and FPO

The dissertation and final public oral exam (FPO) are required for all Ph.D. students. All students must write and successfully defend their dissertation according to Graduate School rules and requirements. 

Director of Graduate Studies

Executive committee.

Associated Faculty

For a full list of faculty members and fellows please visit the department or program website.

Permanent Courses

Courses listed below are graduate-level courses that have been approved by the program’s faculty as well as the Curriculum Subcommittee of the Faculty Committee on the Graduate School as permanent course offerings. Permanent courses may be offered by the department or program on an ongoing basis, depending on curricular needs, scheduling requirements, and student interest. Not listed below are undergraduate courses and one-time-only graduate courses, which may be found for a specific term through the Registrar’s website. Also not listed are graduate-level independent reading and research courses, which may be approved by the Graduate School for individual students.

CHM 541 - Chemical Biology II (also QCB 541)

Cos 551 - introduction to genomics and computational molecular biology (also mol 551/qcb 551), cos 557 - artificial intelligence for precision health (also qcb 557), mat 586 - computational methods in cryo-electron microscopy (also apc 511/mol 511/qcb 513), qcb 501 - topics in ethics in science (half-term), qcb 505 - topics in biophysics and quantitative biology (also phy 555), qcb 508 - foundations of statistical genomics, qcb 515 - method and logic in quantitative biology (also chm 517/eeb 517/mol 515/phy 570), qcb 570 - biochemistry of physiology and disease, qcb 590 - extramural research internship in quantitative and computational biology.

CALS

Computational Biology

two students sit at computers in front of a large window

Computational Biology Program

The computational biology ph.d. program is training the next generation of computational scientists to tackle research using the big genomic, image, remote sensing, clinical, and real world data that are transforming the biological sciences..

The graduate field of Computational Biology offers Ph.D. degrees in the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological systems.

Computation has become essential to biological research. Genomic databases, protein databanks, MRI images of the human brain, and remote sensing data on landscapes contain unprecedented amounts of detailed information that are transforming almost all of biology. The computational biologist must have skills in mathematics and computation as well as in biology. A key goal in training is to develop the ability to relate biological processes to computational models.

The field provides interdisciplinary training and research opportunities in a range of subareas of computational biology including comparative and functional genomics, systems biology, molecular and protein networks, population genomics and genetics, bioinformatics, model system genomics, agricultural genomics, and medical genomics.

Students majoring in computational biology are expected to obtain a broad, interdisciplinary knowledge of fundamental principles in biology, computational science, and mathematics. But because the field covers a wide range of areas, it would be unrealistic to expect a student to master each facet in detail. Instead, students choose from specific subareas of study: They are expected to develop competence in at least one specific subdomain of biology and in relevant subareas of computational science and mathematics.

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Program Contacts

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Associate Professor

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Assistant to the Chair and Graduate Field Administrator

Computational Biology

University of California, Berkeley

About the Program

Under the auspices of the Center for Computational Biology, the Computational Biology Graduate Group offers the PhD in Computational Biology as well as the Designated Emphasis in Computational and Genomic Biology, a specialization for doctoral students in associated programs. The PhD is concerned with advancing knowledge at the interface of the computational and biological sciences and is therefore intended for students who are passionate about being high functioning in both fields. The designated emphasis augments disciplinary training with a solid foundation in the different facets of genomic research and provides students with the skills needed to collaborate across disciplinary boundaries to solve a wide range of computational biology and genomic problems.

Visit Group Website

Admission to the University

Applying for graduate admission.

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Admission to the Program

Applicants for the Computational Biology PhD are expected to have a strong foundation in relevant stem fields, achieved by coursework in at least two computational biology subfields (including, but not limited to, advanced topics in biology, computer science, mathematics, statistics). Typical students admitted to the program have demonstrated outstanding potential as a research scientist and have clear academic aptitude in multiple disciplines, as well as excellent communication skills. This is assessed based on research experience, coursework & grades, essays ( statement of purpose & personal history ), and letters of recommendation. Three letters of recommendation are required, but up to five can be submitted.

The GRE is no longer accepted or used as part of the review (this includes both the general and subject exams). The program does *not* offer a Masters degree in Computational Biology.

Doctoral Degree Requirements

Normative time requirements, normative time to advancement: two years.

Please refer to the PhD page on the CCB website for the most up-to-date requirements and information.

Year 1 Students perform three laboratory rotations with the chief aim of identifying a research area and thesis laboratory. They also take courses to advance their knowledge in their area of expertise or fill in gaps in foundational knowledge. With guidance from the program, students are expected to complete six total graded courses by the end of the second year (not including the Doc Sem, Seminar Series or Ethics courses). Please see the program's website for more detailed course and curriculum requirements.

Year 2 Students attend seminars, complete course requirements, and prepare a dissertation prospectus in preparation for their PhD oral qualifying examination. With the successful passing of the orals, students select their thesis committee and advance to candidacy for the PhD degree.

Normative Time in Candidacy: Three years

Years 3 to 5 Students undertake research for the PhD dissertation under a three or four-person committee in charge of their research and dissertation. Students conduct original laboratory research and then write the dissertation based on the results of this research. On completion of the research and approval of the dissertation by the committee, the students are awarded the doctorate.

Total Normative Time: 5-5.5 years

Time to advancement.

Course List
CodeTitleUnits
Courses Required
Doctoral Seminar in Computational Biology2
Introduction to Research in Computational Biology (rotation units, Fall semester)2-12
Introduction to Research in Computational Biology (rotation units, Spring semester)2-12
Computational Biology Seminar/Journal Club1
Introduction to Probability at an Advanced Level (Stat 200A and 201A are the same content, but offered on different schedules. Students only take one of these.)4
Introduction to Statistics at an Advanced Level (Stat 200B and 201B are the same content, but offered on different schedules. Students only take one of these.)4
The Structure and Interpretation of Computer Programs (or demonstrate they have completed the equivalent in another course; a syllabus is required for approval. Note: Students will need to complete CS61B and CS70 or the equivalent in order to enroll in upper division CS courses. )4
CS 61A is a minimum requirement and students who demonstrate they have completed the equivalent in another course (via syllabus), should take an advanced CS course of their choosing in it's place.
Three additional courses, drawn from existing campus offerings. These courses are intended to resolve deficiencies in training and ensure competency in the fundamental knowledge of each discipline. Students are expected to develop a course plan for remaining program requirements (such as biology coursework) and any additional electives, and to consult with the Head Graduate Advisor before the Spring semester of their first year. The course plan will take into account the student's undergraduate training areas and goals for PhD research areas.12
Responsible Conduct in Research1
Complete an experimental training component in one of three ways: 1) complete a laboratory course at Berkeley (or equivalent) with a minimum grade of B, 2) complete a rotation in an experimental lab (w/ an experimental project), with a positive evaluation from the PI, 3) demonstrate proof of previous experience, such as: a biological sciences undergraduate major with at least two upper division laboratory-based courses, a semester or equivalent of supervised undergraduate experimental laboratory-based research at a university, or previous paid or volunteer/internship work in an industry-based experimental laboratory. Students will provide a brief summary of this experience to the Head Graduate Advisor for approval before taking the QE.

Lab Rotations

Students conduct three 10-week laboratory rotations in the first year. The thesis lab, where dissertation research will take place, is chosen at the end of the third rotation in late April/early May.

Qualifying Examination

The qualifying examination will evaluate a student’s depth of knowledge in his or her research area, breadth of knowledge in fundamentals of computational biology, ability to formulate a research plan, and critical thinking. The QE prospectus will include a description of the specific research problem that will serve as a framework for the QE committee members to probe the student’s foundational knowledge in the field and area of research. Proposals will be written in the manner of an NIH-style grant proposal. The prospectus must be completed and submitted to the chair no fewer than four weeks prior to the oral qualifying examination. Students are expected to pass the qualifying examination by the end of the fourth semester in the program.

Time in Candidacy

Advancement.

After passing the qualifying exam by the end of the second year, students have until the beginning of the fifth semester to select a thesis committee and submit the Advancement to Candidacy paperwork to the Graduate Division.

Dissertation

Primary dissertation research is conducted in years 3-5/5.5. Requirements for the dissertation are decided in consultation with the thesis advisor and thesis committee members. To this end, students are required to have yearly thesis committee meetings with the committee after advancing to candidacy.

Dissertation Presentation/Finishing Talk

There is no formal defense of the completed dissertation; however, students are expected to publicly present an Exit Talk about their dissertation research in their final year.

Required Professional Development

Presentations.

All computational biology students are expected to attend the annual retreat, and will regularly present research talks there. They are also encouraged to attend national and international conferences to present research.

Computational biology students are required to teach for one or two semesters (either one semester at 50% (20hrs/wk) or two semesters at 25% (10hrs/wk)) and may teach more. The requirement can be modified if the student has funding that does not allow teaching.

Designated Emphasis Requirements

Curriculum/coursework.

Please refer to the DE page on the CCB website for the most up-to-date requirements and information.

The DE curriculum consists of one semester of the Doctoral Seminar in computational biology (CMPBIO 293, offered Fall & Spring) taken before the qualifying exam, plus three courses, one each from the three broad areas listed below, which may be independent from or an integral part of a student’s Associated Program. The three courses should be taken in different departments, only one of which may be the student’s home program. These requirements must be fulfilled with coursework taken with a grade of B or better while the student is enrolled as a graduate student at UC Berkeley. S/U graded courses do not count . See below for recommended coursework.

Students do not need to complete all of the course requirements prior to the application or the qualifying exam. The Doctoral Seminar does not need to be taken in order, ie either Fall or Spring are ok, but should be prior to or in the same semester as the Qualifying Exam. The DE will be rescinded if coursework has not been completed upon graduation (students should report their progress each year to the DE advisor, especially if they wish to change one of the courses they listed for the requirement).

More information, including a link to pre-approved courses, can be found on the CCB website .

Qualifying Examination and Dissertation

The qualifying examination and dissertation committees must include at least one (more is fine) Core faculty members from the Computational Biology Graduate Group. The faculty member(s) may serve any role on the committee from Chair to ASR. The Qualifying Examination must include examination of knowledge within the area of Computational and Genomic Biology. The Comp Bio Doctoral Seminar must be completed before the QE, as it will be important preparation for the exam.

Seminars & Retreat

Students must attend the annual Computational Biology Retreat (generally held in November) as well as regular CCB Seminar Series , or equivalent, as designated by the Curriculum Committee. Students are also strongly encouraged to attend or volunteer with program events during Orientation, Recruitment, Symposia, etc. Available travel funds will be dependent upon participation.

CMPBIO 201 Classics in Computational Biology 3 Units

Terms offered: Fall 2015, Fall 2014, Fall 2013 Research project and approaches in computational biology. An introducton to the diverse ways biological problems are investigated computationally through critical evaluation of the classics and recent peer-reviewed literature. This is the core course required of all Computational Biology graduate students. Classics in Computational Biology: Read More [+]

Rules & Requirements

Prerequisites: Acceptance in the Computational Biology Phd program; consent of instructor

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of lecture and 2 hours of discussion per week

Additional Format: One hour of Lecture and Two hours of Discussion per week for 15 weeks.

Additional Details

Subject/Course Level: Computational Biology/Graduate

Grading: Letter grade.

Classics in Computational Biology: Read Less [-]

CMPBIO C210 Introduction to Quantitative Methods In Biology 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course provides a fast-paced introduction to a variety of quantitative methods used in biology and their mathematical underpinnings. While no topic will be covered in depth, the course will provide an overview of several different topics commonly encountered in modern biological research including differential equations and systems of differential equations, a review of basic concepts in linear algebra, an introduction to probability theory, Markov chains, maximum likelihood and Bayesian estimation, measures of statistical confidence, hypothesis testing and model choice, permutation and simulation, and several topics in statistics and machine learning including regression analyses, clustering, and principal component analyses. Introduction to Quantitative Methods In Biology: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Ability to calculate means and variances for a sample and relate it to expectations and variances of a random variable. Ability to calculate probabilities of discrete events using simple counting techniques, addition of probabilities of mutually exclusive events, multiplication of probabilities of independent events, the definition of conditional probability, the law of total probability, and Bayes’ formula, and familiarity with the use of such calculations to understand biological relationships. Ability to carry out various procedures for data visualization in R. Ability to classify states in discrete time Markov chains, and to calculate transition probabilities and stationary distributions for simple discrete time, finite state-space Markov chains, and an understanding of the modeling of evolutionary processes as Markov chains. Ability to define likelihood functions for simple examples based on standard random variables. Ability to implement simple statistical models in R and to use simple permutation procedures to quantify uncertainty. Ability to implement standard and logistic regression models with multiple covariates in R. Ability to manipulate matrices using multiplication and addition. Ability to model simple relationships between biological variables using differential equations. Ability to work in a Unix environment and manipulating files in Unix. An understanding of basic probability theory including some of the standard univariate random variables, such as the binomial, geometric, exponential, and normal distribution, and how these variables can be used to model biological systems. An understanding of powers of matrices and the inverse of a matrix. An understanding of sampling and sampling variance. An understanding of the principles used for point estimation, hypothesis testing, and the formation of confidence intervals and credible intervals. Familiarity with ANOVA and ability to implementation it in R. Familiarity with PCA, other methods of clustering, and their implementation in R. Familiarity with basic differential equations and their solutions. Familiarity with covariance, correlation, ordinary least squares, and interpretations of slopes and intercepts of a regression line. Familiarity with functional programming in R and/or Python and ability to define new functions. Familiarity with one or more methods used in machine learning/statistics such as hidden Markov models, CART, neural networks, and/or graphical models. Familiarity with python allowing students to understand simple python scripts. Familiarity with random effects models and ability to implement them in R. Familiarity with the assumptions of regression and methods for investigating the assumptions using R. Familiarity with the use of matrices to model transitions in a biological system with discrete categories.

Prerequisites: Introductory calculus and introductory undergraduate statistics recommended

Credit Restrictions: Students will receive no credit for INTEGBI C201 after completing INTEGBI 201. A deficient grade in INTEGBI C201 may be removed by taking INTEGBI 201, or INTEGBI 201.

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Format: Three hours of lecture and three hours of laboratory per week.

Formerly known as: Integrative Biology 201

Also listed as: INTEGBI C201

Introduction to Quantitative Methods In Biology: Read Less [-]

CMPBIO C231 Introduction to Computational Molecular and Cell Biology 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022, Fall 2021 This class teaches basic bioinformatics and computational biology, with an emphasis on alignment, phylogeny, and ontologies. Supporting foundational topics are also reviewed with an emphasis on bioinformatics topics, including basic molecular biology, probability theory, and information theory. Introduction to Computational Molecular and Cell Biology: Read More [+]

Prerequisites: BIO ENG 11 or BIOLOGY 1A (may be taken concurrently); and a programming course ( ENGIN 7 or COMPSCI 61A )

Credit Restrictions: Students will receive no credit for BIO ENG C231 after completing BIO ENG 231 . A deficient grade in BIO ENG C231 may be removed by taking BIO ENG 231 , or BIO ENG 231 .

Instructor: Holmes

Also listed as: BIO ENG C231

Introduction to Computational Molecular and Cell Biology: Read Less [-]

CMPBIO C249 Computational Functional Genomics 4 Units

Terms offered: Fall 2024, Fall 2023 This course provides a survey of the computational analysis of genomic data, introducing the material through lectures on biological concepts and computational methods, presentations of primary literature, and practical bioinformatics exercises. The emphasis is on measuring the output of the genome and its regulation. Topics include modern computational and statistical methods for analyzing data from genomics experiments: high-throughput RNA sequencing data , single-cell data, and other genome-scale measurements of biological processes. Students will perform original analyses with Python and command-line tools. Computational Functional Genomics: Read More [+]

Course Objectives: This course aims to equip students with practical proficiency in bioinformatics analysis of genomic data, as well as understanding of the biological, statistical, and computational underpinnings of this field.

Student Learning Outcomes: Students completing this course should have stronger programming skills, practical proficiency with essential bioinformatics methods that are applicable to genomics research, understanding of the statistics underlying these methods, and awareness of key aspects of genome function and challenges in the field of genomics.

Prerequisites: Math 54 or EECS 16A /B; CS 61A or another course in python; BioE 11 or Bio 1a; and BioE 131. Introductory statistics or data science is recommended

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Lareau

Also listed as: BIO ENG C249

Computational Functional Genomics: Read Less [-]

CMPBIO C256 Human Genome, Environment and Public Health 4 Units

Terms offered: Spring 2024, Spring 2023, Fall 2020 This introductory course will cover basic principles of human/population genetics and molecular biology relevant to molecular and genetic epidemiology. The latest methods for genome-wide association studies and other approaches to identify genetic variants and environmental risk factors important to disease and health will be presented. The application of biomarkers to define exposures and outcomes will be explored. Recent developments in genomics , epigenomics and other ‘omics’ will be included. Computer and wet laboratory work will provide hands-on experience. Human Genome, Environment and Public Health: Read More [+]

Prerequisites: Introductory level biology/genetics course, or consent of instructor. Introductory biostatistics and epidemiology courses strongly recommended

Credit Restrictions: Students will receive no credit for PB HLTH C256 after completing CMPBIO 156 . A deficient grade in PB HLTH C256 may be removed by taking CMPBIO 156 .

Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of laboratory per week

Additional Format: Two hours of lecture and two hours of laboratory per week.

Instructors: Barcellos, Holland

Also listed as: PB HLTH C256

Human Genome, Environment and Public Health: Read Less [-]

CMPBIO C256A Human Genome, Environment and Human Health 3 Units

Terms offered: Spring 2017 This introductory course will cover basic principles of human/population genetics and molecular biology relevant to understanding how data from the human genome are being used to study disease and other health outcomes. The latest designs and methods for genome-wide association studies and other approaches to identify genetic variants, environmental risk factors and the combined effects of gene and environment important to disease and health will be presented. The application of biomarkers to define exposures and outcomes will be explored. The course will cover recent developments in genomics, epigenomics and other ‘omics’, including applications of the latest sequencing technology and characterization of the human microbiome. Human Genome, Environment and Human Health: Read More [+]

Prerequisites: Introductory level biology course. Completion of introductory biostatistics and epidemiology courses strongly recommended and may be taken concurrently

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Also listed as: PB HLTH C256A

Human Genome, Environment and Human Health: Read Less [-]

CMPBIO C256B Genetic Analysis Method 3 Units

Terms offered: Prior to 2007 This introductory course will provide hands-on experience with modern wet laboratory techniques and computer analysis tools for studies in molecular and genetic epidemiology and other areas of genomics in human health. Students will also participate in critical review of journal articles. Students are expected to understand basic principles of human/population genetics and molecular biology, latest designs and methods for genome-wide association studies and other approaches to identify genetic variants, environmental risk factors and the combined effects of gene and environment important to human health. Students will learn how to perform DNA extraction, polymerase chain reaction and methods for genotyping, sequencing, and cytogenetics. Genetic Analysis Method: Read More [+]

Prerequisites: Introductory level biology course. Completion of introductory biostatistics and epidemiology courses strongly recommended and may be taken concurrently with permission. PH256A is a requirement for PH256B; they can be taken concurrently

Fall and/or spring: 15 weeks - 2-2 hours of lecture and 1-3 hours of laboratory per week

Additional Format: Two hours of lecture and one to three hours of laboratory per week.

Also listed as: PB HLTH C256B

Genetic Analysis Method: Read Less [-]

CMPBIO 275 Computational Biology Seminar/Journal Club 1 Unit

Terms offered: Fall 2024, Spring 2024, Fall 2023 This seminar course will cover a wide range of topics in the field of computational biology. The main goals of the course are to expose students to cutting edge research in the field and to prepare students for engaging in academic discourse with seminar speakers - who are often leaders in their fields. A selected number of class meetings will be devoted to the review of scientific papers published by upcoming seminar speakers and the other class meetings will be devoted to discussing other related articles in the field. The seminar will expose students to both the breadth and highest standards of current computational biology research. Computational Biology Seminar/Journal Club: Read More [+]

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 1 hour of seminar per week

Additional Format: One hour of seminar per week.

Grading: Offered for satisfactory/unsatisfactory grade only.

Computational Biology Seminar/Journal Club: Read Less [-]

CMPBIO 276 Algorithms for Computational Biology 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will provide familiarity with algorithms and probabilistic models that arise in various computational biology applications, such as suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, hidden Markov models, gene finding, motif finding, linear/logistic regression, random forests, convolutional neural networks, genome-wide association studies, pathogenicity prediction, and sequence-to-epigenome prediction. Algorithms for Computational Biology: Read More [+]

Prerequisites: CompSci 70 AND CompSci 170, MATH 54 OR EECS 16A OR an equivalent linear algebra course

Repeat rules: Course may be repeated for credit with instructor consent.

Instructors: Song, Ioannidis

Algorithms for Computational Biology: Read Less [-]

CMPBIO 290 Special Topics - Computational Biology 1 - 4 Units

Terms offered: Fall 2022, Fall 2021, Spring 2018 This graduate-level course will cover various special topics in computational biology and the theme will vary from semester to semester. The course will focus on computational methodology, but also cover relevant biological applications. This course will be offered according to student demand and faculty availability. Special Topics - Computational Biology: Read More [+]

Prerequisites: Graduate standing in EECS, MCB, Computational Biology or related fields; or consent of the instructor

Fall and/or spring: 15 weeks - 1-3 hours of lecture per week

Additional Format: One to three hours of lecture per week for standard offering. In some instances, condensed special topics classes running from 2-10 weeks may also be offered usually to accommodate guest instructors. Total works hours will remain the same but more work in a given week will be required.

Special Topics - Computational Biology: Read Less [-]

CMPBIO 293 Doctoral Seminar in Computational Biology 2 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 This interactive seminar builds skills, knowledge and community in computational biology for first year PhD and second year Designated Emphasis students. Topics covered include concepts in human genetics/genomics, microbiome data analysis, laboratory methodologies and data sources for computational biology, workshops/instruction on use of various bioinformatics tools, critical review of current research studies and computational methods, preparation for success in the PhD program and career development. Faculty members of the graduate program in computational biology and scientists from other institutions will participate. Topics will vary each semester. Doctoral Seminar in Computational Biology: Read More [+]

Fall and/or spring: 15 weeks - 2 hours of seminar per week

Additional Format: Two hours of seminar per week.

Doctoral Seminar in Computational Biology: Read Less [-]

CMPBIO C293 Doctoral Seminar in Computational Biology 2 Units

Terms offered: Spring 2024, Fall 2022, Fall 2021 This interactive seminar builds skills, knowledge and community in computational biology for first year PhD and second year Designated Emphasis students. Topics covered include concepts in human genetics/genomics, microbiome data analysis, laboratory methodologies and data sources for computational biology, workshops/instruction on use of various bioinformatics tools, critical review of current research studies and computational methods, preparation for success in the PhD program and career development. Faculty members of the graduate program in computational biology and scientists from other institutions will participate. Topics will vary each semester. Doctoral Seminar in Computational Biology: Read More [+]

Instructors: Moorjani, Rokhsar

Also listed as: MCELLBI C296

CMPBIO 294A Introduction to Research in Computational Biology 2 - 12 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Closely supervised experimental or computational work under the direction of an individual faculty member; an introduction to methods and research approaches in particular areas of computational biology. Introduction to Research in Computational Biology: Read More [+]

Prerequisites: Standing as a Computational Biology graduate student

Fall and/or spring: 15 weeks - 2-20 hours of laboratory per week

Additional Format: Two to Twenty hours of Laboratory per week for 15 weeks.

Introduction to Research in Computational Biology: Read Less [-]

CMPBIO 294B Introduction to Research in Computational Biology 2 - 12 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Closely supervised experimental or computational work under the direction of an individual faculty member; an introduction to methods and research approaches in particular areas of computational biology. Introduction to Research in Computational Biology: Read More [+]

CMPBIO 295 Individual Research for Doctoral Students 1 - 12 Units

Terms offered: Summer 2024 10 Week Session, Summer 2023 10 Week Session, Summer 2022 10 Week Session Laboratory research, conferences. Individual research under the supervision of a faculty member. Individual Research for Doctoral Students: Read More [+]

Prerequisites: Acceptance in the Computational Biology PhD program; consent of instructor

Fall and/or spring: 15 weeks - 1-20 hours of laboratory per week

Summer: 10 weeks - 1.5-30 hours of laboratory per week

Additional Format: One to twenty hours of laboratory per week. One and one-half to thirty hours of laboratory per week for 10 weeks.

Individual Research for Doctoral Students: Read Less [-]

CMPBIO 477 Introduction to Programming for Bioinformatics Bootcamp 1.5 Unit

Terms offered: Prior to 2007 The goals of this course are to introduce students to Python, a simple and powerful programming language that is used for many applications, and to expose them to the practical bioinformatic utility of Python and programming in general. The course will allow students to apply programming to the problems that they face in the lab and to leave this course with a sufficiently generalized knowledge of programming (and the confidence to read the manuals) that they will be able to apply their skills to whatever projects they happen to be working on. Introduction to Programming for Bioinformatics Bootcamp: Read More [+]

Prerequisites: This is a graduate course and upper level undergraduate students can only enroll with the consent of the instructor

Summer: 3 weeks - 40-40 hours of workshop per week

Additional Format: Organized as a bootcamp, the ten-day course will include two sessions daily, each consisting of roughly two hours of lecture and up to three hours of hands on exercises.

Subject/Course Level: Computational Biology/Other professional

Introduction to Programming for Bioinformatics Bootcamp: Read Less [-]

Contact Information

Computational biology graduate group.

574 Stanley Hall

Phone: 510-642-0379

Fax: 510-666-3399

[email protected]

Director, CCB

Elizabeth Purdom

[email protected]

Graduate Program Manager

574 Stanley Hall, MC #3220

[email protected]

Head Graduate Advisor for the PhD & DE

John Huelsenbeck

[email protected]

CCB DE Advising

CCB DE email

[email protected]

Executive Director, CCB

Phone: 510-666-3342

[email protected]

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COMMENTS

  1. 2022 Computational Biology PhD Programs - Reddit

    I am at UW which has strong courses and research in computational biology although you will have to major in something adjacent like Bioengineering or Computer Science or Genome Sciences and then focus your research on computational biology topics.

  2. Bioinformatics/Computational Biology PhD Programs

    Chemical Biology and Data Science double major with a Data Science concentration of Computational Biology Methods. GPA: 3.4 cumulative, 3.5 in Data Science coursework. GRE (unofficial scores): 167Q and 165V. I'll get my official scores and AW in about 2 weeks.

  3. Am I a competitive candidate for computational biology ...

    Do you want to do work on computational algorithm/program to work on genomics data? Then bioinformatics/computational biology is definitely better fit for you. Are you looking to analyze genetic data using computational tools?

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    The PhD program in Computational Biology and Bioinformatics participates in the Combined Program in the Biological and Biomedical Sciences (BBS), and applicants interested in pursuing a degree in cell biology should apply to the Computational Biology and Biomedical Informatics Track within BBS.

  5. Computational Biology | Graduate Programs - Brown University

    Computational Biology. The Center for Computational Molecular Biology (CCMB) offers Ph.D. degrees in Computational Biology to train the next generation of scientists to perform cutting edge research in the multidisciplinary field of Computational Biology.

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    The main objective of the Computational Biology PhD is to train the next generation of scientists who are both passionate about exploring the interface of computation and biology, and committed to functioning at a high level in both computational and biological fields.

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    The Program in Quantitative and Computational Biology (QCB) is intended to facilitate graduate education at Princeton at the interface of biology and the more quantitative sciences and computation.

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    The Computational Biology curriculum is designed to help students learn how to leverage mathematical and computational approaches to understand biological and chemical processes.

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    The Computational Biology Ph.D. program is training the next generation of Computational Scientists to tackle research using the big genomic, image, remote sensing, clinical, and real world data that are transforming the biological sciences.

  10. Computational Biology | Berkeley Academic Guide

    Under the auspices of the Center for Computational Biology, the Computational Biology Graduate Group offers the PhD in Computational Biology as well as the Designated Emphasis in Computational and Genomic Biology, a specialization for doctoral students in associated programs.