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  • Published: 01 November 2013

So you want to be a computational biologist?

  • Nick Loman 2 &
  • Mick Watson 1  

Nature Biotechnology volume  31 ,  pages 996–998 ( 2013 ) Cite this article

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  • Computational biology and bioinformatics

Two computational biologists give advice when starting out on computational projects.

You have full access to this article via your institution.

The term 'computational biologist' can encompass several roles, including data analyst, data curator, database developer, statistician, mathematical modeler, bioinformatician, software developer, ontologist—and many more. What's clear is that computers are now essential components of modern biological research, and scientists are being asked to adopt new skills in computational biology and master new terminology ( Box 1 ). Whether you're a student, a professor or somewhere in between, if you increasingly find that computational analysis is important to your research, follow the advice below and start along the road towards becoming a computational biologist!

Understand your goals and choose appropriate methods

Key to good computational biology is the selection and use of appropriate software. Before you can usefully interpret the output of a piece of software, you must understand what the software is doing. You wouldn't go into the laboratory and perform a polymerase chain reaction without a basic understanding of the method. Why would you do the same with a computational analysis? Understanding the underlying methods and algorithms gives you the tools to interpret the results. That doesn't mean you need to read through each line of source code, but you should have a grasp of the concepts.

Software tools are often implementations of a particular algorithm that may be well-suited for particular types of data; for example, in de novo assembly, an Overlap-Layout-Consensus assembler is optimized for longer sequence reads, whereas de Bruijn graphs were designed with short reads in mind. Choosing software employing the most appropriate algorithm will save you a lot of time.

Set traps for your own scripts and other people's

Laboratory scientists wouldn't dream of running experiments without the necessary positive and negative controls... tests are the computational biology equivalent.

How do you know your script, software or pipeline is working? Computers will happily output results for the most bizarre of input data, and the absence of an error message is not an indication of success. Create tests, small datasets for which the answer is known, and check that the software or pipeline can reproduce that answer. Try and do that for every 'type' of answer you expect to find. Double-check the results of everything, to see if those results make sense. Laboratory scientists wouldn't dream of running experiments without the necessary positive and negative controls, and these tests are the computational biology equivalent.

You're a scientist, not a programmer

The perfect is the enemy of the good. Remember you are a scientist and the quality of your research is what is important, not how pretty your source code looks. Perfectly written, extensively documented, elegant code that gets the answer wrong is not as useful as a basic script that gets it right. Having said that, once you're sure your core algorithm works, spend time making it elegant and documenting how to use it. Use your biological knowledge as much as possible—that's what makes you a computational biologist.

Use version control software

Versioning will help you track changes to your code, maintain multiple versions and to work collaboratively with others. Using a standard tool, such as Git or Subversion, you will also be able to publish your code easily. Be nice to your future self. A few well-placed README files explaining the choices you made and why you made them will be a boon in months or years when you return to a project. Document your code and scripts so that you understand what they do. When you come to publish your work, try publishing the scripts and methods you used to generate your results so that others can reproduce them. Also consider keeping a digital laboratory notebook to document your analyses as you perform them. Repositories, such as Github, are ideal for this and also help you maintain copies of the repository to serve as off-site backups ( Table 1 ).

Pipelineitis is a nasty disease

A pipeline is a series of steps, or software tools, run in sequence according to a predefined plan. Pipelines are great for running exactly the same set of steps in a repetitive fashion, and for sharing protocols with others, but they force you into a rigid way of thinking and can decrease creativity.

Warning: don't pipeline too early. Get a method working before you turn it into a pipeline. And even then, does it need to be a pipeline? Have you saved time? Is your pipeline really of use to others? If those steps are only ever going to be run by you, then a simple script will suffice and any attempts at pipelining will simply waste time. Similarly, if those steps will only ever be run once, just run them once, document the fact you did so and move on.

An Obama frame of mind

Yes you can! As a computational biologist, you will need to be creative, from tweaking existing methods to developing entirely new ones. Be adventurous, be prepared to fail, but keep going. It's amazing what you can achieve by using Google, by asking other people in the field and by teaching yourself how to solve particular problems.

Attending training courses ( Table 2 ) can be useful, but these are only really the start of your learning, not the end. Continue by teaching yourself afterwards.

Be suspicious and trust nobody

The following experiment is often performed during statistics training. First, a large matrix of random numbers is created and each column is designated as 'case' or 'control'. A statistical test is then applied to each row to test for significant differences between the case data and the control data. You should not be surprised to learn that hundreds of rows come back with P values indicating statistical significance. Biological datasets, such as those generated by genomics experiments are just like this, large and full of noise. Your data analysis will produce both false positives and false negatives; and there may be systematic bias in the data, introduced either in the experiment or during the analysis.

Knowledge of biology is vital in the interpretation of computational results.

There is a temptation, even among biologists trained in statistical techniques, to throw caution to the wind when particular software or pipelines produce an interesting result. Instead, treat results with great suspicion, and carry out further tests to determine whether the results can be explained by experimental error or bias. If multiple approaches agree, then your confidence in those answers increases. But for many findings, validation and further work in the laboratory may be necessary. Knowledge of biology is vital in the interpretation of computational results. Setting traps, or tests, as mentioned above, is only part of this. Those tests are meant to ensure that your software or pipeline is working as you expect it to work; it doesn't necessarily mean that the answers produced are correct.

The right tool for the job

Become comfortable working from the UNIX/Linux command line. The command line is incredibly powerful, allowing you greater control over what software is doing and allowing you to run and control multiple jobs at once. Most bioinformatics software is designed to be run from the command line. Learn about compute clusters and how to run hundreds of jobs in parallel. You'll need to be able to code, but the choice of language is not as critical as you may be led to believe by computer scientists. Each language has strengths and weaknesses, and you may have to use more than one to get the job done.

Bear in mind that choosing a more popular language will let you benefit from a larger library of existing toolsets, for example the Bio* projects from the Open Bioinformatics Foundation ( http://www.open-bio.org/wiki/Main_Page ). Microsoft Excel is a spreadsheet program, and unless used very carefully, is not suitable for biological data ( http://www.biomedcentral.com/1471-2105/5/80/ ). Store your experimental data, in structured text files or in an SQL database. Employing basic database practice, such as normalization (i.e., ensuring a single place for each piece of data associated with your project), means there are fewer chances to make a mistake later. Make sure everything is backed up, regularly.

Be a detective

As a computational biologist, a lot of your time will be spent analyzing and interpreting data. The data are telling you something. They contain a story and it's your job to find out what that story is. Unless you're very lucky, it probably won't be obvious. Finding out will not be easy. You will have to think about how the experiment was performed; how the analysis was performed; and what the results are telling you. You will need to confidently disregard, or control for, what you think are errors and systematic biases in the data.

To do the above you may need to talk to other scientists involved in the work, or integrate and analyze additional data. You may need to propose follow-up experiments, designed to test any hypotheses you generate. Remember, the real story may not be in your data at all! If the biological system you're interested in hinges on phosphorylation of a protein, then you probably won't see this effect in your RNA-seq data. You are basically a detective. Work the data. Figure it out.

Someone has already done this. Find them!

No matter how gnarly a problem or how cutting-edge a method, there is a pretty good chance someone out there has tried to tackle it already. Two excellent resources for discussing problems with software are BioStars ( http://www.biostars.org/ ) and SEQanswers ( http://seqanswers.com/ ). Twitter is another place where you will be able to find advice and links to resources and papers. Hook up with other computational biologists in your department or institute. There is likely to be a local computational biology meeting or interest group in your area, so find it and join up; if there isn't, why not start your own like Nick did!

In conclusion, there is a huge amount of support available online and through local user groups, if you want to practice computational biology. The most important starting point is to be brave enough to try and to learn from these resources. Install Linux on your PC, and start working through some learning materials online. You will be astonished what you will be able to achieve very quickly, and ultimately you will have a very rewarding experience!

Box 1: Glossary of useful computing terms

Command line interface. A means of interacting with a computer whereby the user issues commands in the form of successive lines of text. The term 'shell', or 'UNIX shell', refers to a command line interpreter for the UNIX/Linux operating system. Microsoft provides a command line interface to Windows, but this is not commonly used in bioinformatics.

Compute cluster. A collection of computers that work together, often to run many jobs at once through a job scheduling and resource management system.

Pipeline. In computer jargon, this is a series of steps, or software tools, run in a specified order, where the input to one tool may be the output of a previous tool. Can include automated logical decisions.

Source code (code). Refers to computer instructions written in a particular programming language.

Software. We don't really need to define this, do we? For completeness, let's just say this is a set of instructions that instructs a computer to carry out certain operations. Can be an executable file that is 'compiled' from source code or a collection of source code that is interpreted.

Script. Source code written in an interpreted language, often used in bioinformatics to perform particular tasks, for example, running other software in a specified order, such as in a pipeline.

Source control and version control. A system by which changes in source code are tracked and managed, and under which multiple versions of source code can be maintained.

UNIX/Linux. UNIX is a stable, multiuser, multitasking system for servers, desktops and laptops, with both a graphical and command-line interface. UNIX comes in many different versions. Linux refers to a number of different UNIX-like operating systems that are developed under an open-source model.

Author information

Authors and affiliations.

Mick Watson is at The Roslin Institute, University of Edinburgh, Edinburgh, UK, and is Head of Bioinformatics at Edinburgh Genomics, an academic genomics facility developing bioinformatics training in next-generation sequence analysis (http://genomics.ed.ac.uk). Follow him on Twitter, @BioMickWatson, and on his blog at http://biomickwatson.wordpress.com/.,

Mick Watson

Nick Loman works as an independent research fellow in the Institute for Microbiology and Infection at the University of Birmingham, Birmingham, UK, sponsored by a Medical Research Council Special Training Fellowship in Biomedical Informatics. Follow him on Twitter, @pathogenomenick, and on his blog at http://pathogenomics.bham.ac.uk/blog.,

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Loman, N., Watson, M. So you want to be a computational biologist?. Nat Biotechnol 31 , 996–998 (2013). https://doi.org/10.1038/nbt.2740

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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) Discuss any broader impacts that you have had on your community (e.g. family, educational institution, or broader community). 5) 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.)

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.

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Berkeley Berkeley Academic Guide: Academic Guide 2023-24

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.

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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. A complete list of graduate academic 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 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, personal background, 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 or Ethics course). 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, 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 a 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).

  • Computer Science and Engineering: A single course at the level of CS61A or higher will fulfill this requirement. Students can also take CS 88 (as an alternative to CS61A), though depending on their background, Data 8 may be necessary to complete this course. Students with a more advanced background are recommended to take a higher level CS course to fulfill the requirement.
  • Biostatistics, Mathematics and Statistics: A single course at the level of Stat 131A, 133, 134, or 135 or higher will fulfill this requirement. Students with a more advanced background are recommended to take one of either Stat 201A & 201B or a higher level course to fulfill the requirement. Statistics or probability courses from other departments may be able to fulfill this requirement with prior approval of the program.
  • Biology: please select an appropriate biology course from the list linked below (not up-to-date), or choose a course from current course listings.
  • Computational Biology: CMPBIO C293, Doctoral Seminar, offered Fall & Spring.

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 2023, Fall 2022, Fall 2021, Fall 2020 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 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: Spring 2024, Fall 2023, Fall 2022 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 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 predi ction. 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 2023, Spring 2023, Spring 2022 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 2023, Fall 2022, Fall 2021 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]

Executive Director, CCB

Phone: 510-666-3342

[email protected]

Graduate Program Manager

574 Stanley Hall, MC #3220

[email protected]

Head Graduate Advisor and Chair for the PhD & DE

John Huelsenbeck

[email protected]

CCB DE Advising

[email protected]

Print Options

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Best Universities for Bioinformatics and Computational biology in the World

Updated: July 18, 2023

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Below is a list of best universities in the World ranked based on their research performance in Bioinformatics and Computational biology. A graph of 1.8B citations received by 83.2M academic papers made by 3,014 universities in the World was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Harvard University

For Bioinformatics and Computational biology

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2. Stanford University

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3. University of California - San Francisco

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4. Massachusetts Institute of Technology

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5. University of Washington - Seattle

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6. University of California-San Diego

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7. Johns Hopkins University

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8. University of Oxford

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9. Yale University

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10. University of Michigan - Ann Arbor

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11. University of Pennsylvania

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12. University of Toronto

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13. University of Cambridge

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14. University of California - Los Angeles

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15. University of California - Berkeley

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16. Boston University

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17. Washington University in St Louis

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18. University of Wisconsin - Madison

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19. University of North Carolina at Chapel Hill

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20. University of Texas MD Anderson Cancer Center

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21. Cornell University

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22. University College London

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23. Columbia University

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24. Baylor College of Medicine

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25. Imperial College London

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26. University of Chicago

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27. Karolinska Institute

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28. University of Queensland

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29. University of Manchester

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30. University of Edinburgh

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31. University of Copenhagen

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32. Duke University

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33. Ohio State University

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34. University of California - Davis

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35. University of Pittsburgh

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36. University of Minnesota - Twin Cities

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37. University of Tokyo

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38. Kyoto University

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39. New York University

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40. University of Southern California

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41. Northwestern University

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42. University of British Columbia

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43. University of Texas Southwestern Medical Center

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44. Vanderbilt University

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45. University of Alberta

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46. University of Melbourne

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47. Icahn School of Medicine at Mount Sinai

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48. University of Illinois at Urbana - Champaign

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49. University of Florida

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50. Rockefeller University

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51. California Institute of Technology

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52. Pennsylvania State University

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53. Emory University

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54. University of Utah

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55. Weizmann Institute of Science

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56. Catholic University of Leuven

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57. McGill University

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58. Technical University of Munich

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59. National University of Singapore

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60. Uppsala University

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61. University of Sydney

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62. Swiss Federal Institute of Technology Zurich

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63. Princeton University

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64. University of Zurich

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65. Rutgers University - New Brunswick

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66. Case Western Reserve University

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67. University of Virginia

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68. King's College London

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69. University of Colorado Boulder

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70. Seoul National University

Seoul National University logo

71. Monash University

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72. Charite - Medical University of Berlin

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73. University of Glasgow

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74. University of Arizona

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75. Mayo Clinic College of Medicine and Science

Mayo Clinic College of Medicine and Science logo

76. University of Massachusetts Medical School Worcester

University of Massachusetts Medical School Worcester logo

77. University of Helsinki

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78. University of Texas at Austin

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79. Ghent University

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80. Shanghai Jiao Tong University

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81. Utrecht University

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82. University of Alabama at Birmingham

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83. University of California - Irvine

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84. Peking University

Peking University logo

85. McMaster University

McMaster University logo

86. Tel Aviv University

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87. Technical University of Denmark

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88. Seattle University

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89. University of Maryland - College Park

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90. University of Bristol

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91. Indiana University - Bloomington

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92. University of Birmingham

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93. Zhejiang University

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94. University of Barcelona

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95. University of Iowa

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96. Michigan State University

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97. University of Basel

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98. University of Geneva

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99. University of Amsterdam

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100. University of Lausanne

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Biology subfields in the World

computational biology phd reddit

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Information for prospective Ph.D. students in Computational Biology or Bioinformatics

The Ph.D. programs in Computational Biology at Johns Hopkins University span four Departments and a wide range of research topics. Our programs provide interdisciplinary training in computational and quantitative approaches to scientific problems that include questions in genomics, medicine, genome engineering, sequencing technology, molecular biology, genetics, and others.

Our students are actively involved in high-profile research, and have developed very widely-used bioinformatics software systems such as Bowtie , Tophat , and Cufflinks . and the more-recent systems HISAT and Stringtie (for RNA-seq alignment and assembly) and Kraken (for metagenomic sequence analysis). The work they do with Hopkins faculty prepares them to go on to postdoctoral and tenure track faculty positions at top-ranked universities including (in recent years) Harvard, the University of Washington, Carnegie Mellon, the University of Maryland, and Brown.

Students in computational biology at Hopkins can enroll in one of four different Ph.D. programs. These include Biomedical Engineering, ranked #1 in the nation; Biostatistics, also ranked #1 in the nation; Biology, ranked #6 in the nation; and the rapidly growing Computer Science Department, ranked #23 in the nation. Hopkins is also ranked #4 in the nation in Bioinformatics, a ranking that just started appearing in 2022.

CCB faculty have appointments in each of these programs, and some of us maintain appointments in multiple programs. To determine which program fits your interests and background, browse the course lists below. Each program has a separate application process; please apply specifically to the departments you're interested in. Applications to multiple programs are permitted, but if you're not certain, we encourage you to contact potential faculty advisors before you apply. Wherever you apply, make it clear that your interest is Computational Biology.

Sample Course Offerings for Ph.D. students in Computational Biology

Department of biomedical engineering, whiting school of engineering.

The Johns Hopkins Department of Biomedical Engineering (BME), widely regarded as the top program of its kind in the world and ranked #1 in the nation by U.S. News , is dedicated to solving important scientific problems at the intersection of multiple disciplines and that have the potential to make a significant impact on medicine and health. At the intersection of inquiry and discovery, the department integrates biology, medicine, and engineering and draws upon the considerable strengths and talents of the Johns Hopkins Schools of Engineering and Medicine. See the BME Ph.D. program website for many details.

Department of Computer Science, Whiting School of Engineering

The faculty represent a broad spectrum of disciplines encompassing core computer science and many cross-disciplinary areas including Computational Biology and Medicine, Information Security, Machine Learning, Data Intensive Computing, Computer-Integrated Surgery, and Natural Language Processing.

Ph.D. program

A total of 8 courses are required, and a typical load is 3 courses per semester. See the CS Department website for details. For a look at courses that might be included in Ph.D. training, see this page , though note that it is not a comprehensive list. For the Computer Science Ph.D., 2 out of the required 8 classes can be taken outside the Department. These may include any of the courses in the BME, Biostatistics, and Biology programs listed on this page.

Department of Biostatistics, Bloomberg School of Public Health

Johns Hopkins Biostatistics is the oldest department of its kind in the world and has long been considered as one of the best. In 2022, it was ranked #1 in the nation by U.S. News .

All students in the Biostatistics Ph.D. program have to complete the core requirements:

  • A two-year sequence on biostatistical methodology (140.751-756)
  • A two-year sequence on probability and the foundations and theory of statistical science (550.620-621, 140.673-674, 140.771-772);
  • Principles of Epidemiology (340.601)

In addition, students in computational biology might take:

  • 140.776.01 Statistical Computing (3 credits)
  • 140.638.01 Analysis of Biological Sequences (3 credits)
  • 140.644.01 Statistica machine learning: methods, theory, and applications (4 credits)
  • 140.688.01 Statistics for Genomics (3 credits)

Further courses might include 2-3 courses in Computer Science, BME, or Biology listed on this page.

Department of Biology, Krieger School of Arts and Sciences

The Hopkins Biology Graduate Program, founded in 1876, is the oldest Biology graduate school in the country. People like Thomas Morgan, E. B. Wilson, Edwin Conklin and Ross Harrison, were part of the initial graduate classes when the program was first founded. Hopkins is ranked #6 in the nation in Biological Sciences by U.S. News

Quantitative and computational biology are an integral part of the CMDB training program. During the first semester students attend Quantitative Biology Bootcamp, a one week intensive course in using computational tools and programming for biological data analysis. Two of our core courses - Graduate Biophysical Chemistry and Genomes and Development - each have an associated computational lab component.

Ph.D. in Cell, Molecular, Developmental Biology, and Biophysics (CMDB):

The CMDB core includes the following courses:

  • 020.607 Quantitative Biology Bootcamp
  • 020.674 Graduate Biophysical Chemistry
  • 020.686 Advanced Cell Biology
  • 020.637 Genomes and Development
  • 020.668 Advanced Molecular Biology
  • 020.606 Molecular Evolution
  • 020.620 Stem Cells
  • 020.630 Human Genetics
  • 020.640 Epigenetics & Chromosome Dynamics
  • 020.650 Eukaryotic Molecular Biology
  • 020.644 RNA

Students in computational biology can use their electives to take more computationally intensive courses. You have considerable flexibility to design a program of study with your Ph.D. advisor.

computational biology phd reddit

The Center for Computational Biology at Johns Hopkins University

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QUANTITATIVE AND

COMPUTATIONAL BIOLOGY

computational biology phd reddit

PhD PROGRAM

We prepare students to develop or innovatively apply novel, effective, and efficient computational methods capable of answering complex problems through a rigorous curriculum grounded in a focus on statistics, algorithms, and biology.

Why Choose Our P.h.D. Program?

Ph.D. QUICK LINKS

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A Ph.D. in Computational Biology and Bioinformatics from the Dornsife College of Letter, Arts and Sciences at USC offers its students a truly unique graduate experience: ​

A long history in Computational Biology training

A distinguished body of Alumni

A strong focus in methodological development

Close ties with experimental biology

A distinguished list of faculty

A strong research program

Our QCB Department is an academic unit, and not a virtual program

Our Philosophy: A Strong Focus in Method Development and Applications

We prepare students to be able to develop and innovatively apply novel, effective, and efficient computational methods to address the challenges arisen from emerging data.

We train our students to have a solid foundation of statistics, algorithms, and biology with a rigorous curriculum, and build their ability to analyze data and formulate problems by research projects.

This training prepares our students for a variety of careers

Join A Collaborative Working Environment

We are the PhD Program of USC's Department of Quantitative and Computational Biology, located in two modern research buildings shared with wet-lab experimental biologists, as well as chemists, physicists, and engineers.

We have close collaborations with faculty at the Dornsife College of Letters, Arts and Sciences, the Keck School of Medicine, the Viterbi School of Engineering, and other Schools across the university.

An Independent Academic Department, Not A Virtual Program

Most Computational Biology training programs in the world collect faculties from different departments, and build a virtual program. In contrast all faculty in our program belong to the same academic unit, physically located in the same two buildings (we also have several, affiliated faculty from other departments).

With an exceptionally strong research program, our graduate students over the past decade have published multiple significant research papers, including articles in Science, Cell, Nature, Nature Biotechnology, Nature Methods, PNAS, and Genome Research.

A World Class Faculty

Our Department of Quantitative and Computational Biology currently has 15 tenured and tenure track, 2 teaching and 2 Emeritus core faculty members. We also have 20 joint faculty members from the Dornsife College of Letter, Arts and Sciences, the Viterbi School of Engineering and the Keck School of Medicine, and other USC Schools.

Our faculty members have won many prestigious awards: The QCB faculty include a Nobel Laureate, four members of the US National Academy of Sciences, one member of the US National Academy of Engineering, two fellows of the Royal Society, one member of the French Academy of Sciences, one member of the Chinese Academy of Sciences, and five AAAS Fellows. 

A Distinguished Body of Alumni

Our program in Computational Biology and Bioinformatics is the world’s oldest training program in Computational Biology. In 2022 we have celebrated 40 years of Computational Biology at USC with the USC Computational Biology Symposium 2022. (link conference website here) At this conference, the we also celebrated the new QCB Department and Professor Michael Waterman’s 80th birthday. 

Over the past 40 years, many prominent and leading computational biologists have been trained at USC. A list of recent graduates of our program is available HERE . Become part of our QCB family!

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Bioinformatics and Computational Biology

Background BICB was established in 2007 as a result of legislative funding and support driven by the recommendations of the Governor of Minnesota's appointed Rochester Higher Education Development Committee (RHEDC).  The committee recommended the collaborative development of an institution that focuses on health science, bioscience, engineering, and technology. This institution is the University of Minnesota Rochester.

Vision The vision of the Bioinformatics and Computational Biology (BICB) program to establish world-class academic and research programs at the University of Minnesota Rochester by leveraging the University of Minnesota’s academic and research capabilities in partnership with Mayo Clinic, Hormel Institute, IBM, National Marrow Donor Program (NMDP), the Brain Sciences Center and other industry leaders. The goal is to advance informatics and computation that provide applications to economic activities via innovation, translational research, and clinical experiences to support a strong life science industry in Minnesota.

Mission The mission of the Bioinformatics and Computational Biology (BICB) graduate program is to provide interdisciplinary education in the area of biomedical informatics and computational biology at the interface of quantitative sciences, medicine, and biology. The graduate program trains graduate students in the development and applications of computational methods and to work in interdisciplinary teams of life scientists and computational scientists. The program offers industrial and clinical internships and training in business leadership, technology management, and ethics to prepare students for the workplace. Faculty provide education through formal coursework, research seminars, and one-on-one advising. In addition, the program provides a mentoring program for students and junior faculty that will serve as a model for interdisciplinary graduate education.​​​​​

Seminar and Events

BICB Colloquium Faculty Nomination Talks : Join us in person on the UMR campus in room 322, on the Twin Cities campus in MCB 2-122 or  virtually  at 5 p.m. on the dates listed below:

9/14  Nuri Ince  - Professor of Biomedical Engineering and Neurosurgery Mayo Clinic

9/28  Damien Fair  - Professor, Department of Pediatrics, University of Minnesota Medical School

10/12  Hamid Tizhoosh  - Professor of Biomedical Informatics Mayo Clinic 

10/26  Nichole Klatt  - Professor and the Director of the Division of Surgical Outcomes and Precision Medicine Research, University of Minnesota

11/9  Yogatheesan Varatharajah  - Assistant Professor, Department of Computer Science & Engineering

11/30  Amy Kinsley  - Assistant Professor, Department of Veterinary Population Medicine (VPM)

1/25 -  Kyungsoo Yoo  - Professor, Department of Soil, Water and Climate at the University of Minnesota

2/22 -  Shijia Zhu  - Assistant Professor, Department of Laboratory Medicine and Pathology, UMN Medical School

3/28  Wei Zhang  - Associate Professor in the School of Dentistry at the University of Minnesota

4/11 -  Zhiyv "Neal" Niu  - Assistant Professor of Laboratory Medicine and Pathology at Mayo Clinic

Current News

Congratulations to our BICB graduate student, Aya Aqeel (Adviser:  Dr. Meghan Driscoll ). She has been awarded the Interdisciplinary Doctoral Fellowship for the 2024-25 academic year by the graduate school.

News Archive

Bioinformatics and Computational Biology in the News

March 2, 2023 - Congratulations to Quincy Gu (Adviser:  Steven Hart ). He has recently been featured for work in artificial intelligence to diagnose melanoma at  M Global , a University platform for global engagement.

February 1, 2023 - Congratulations to our BICB graduate student, Myana Anderson (Adviser:  Dr. Tinen Iles ). She has been awarded the Interdisciplinary Doctoral Fellowship for the 2023-24 academic year by the graduate school.

Jan 12, 2023 - The 15th  Annual BICB Research Symposium  was held at the University of Minnesota Rochester campus. The theme for this year's symposium was "Data Analytics in Genomics and Imaging."

PhD student Rachel Moss will be presenting a talk at the Department of Pediatrics seminar series IMPRESS. If you would like to learn more and/or attend the talk please visit the  IMPRESS  website.

August 25, 2022 - Congratulations to Daniel Chang (Adviser: Zohar Sachs, Co-adviser: Chad Myers). He has been awarded the 2022-23 Translational Research and Career Training (TRACT) fellowship sponsored by the NIH TL1 program at the Clinical and Translational Science Institute. 

July 7, 2022 - Congratulations to Rachel Moss (Adviser: Logan Spector). She has been awarded the 2022-23 Translational Research and Career Training (TRACT) fellowship sponsored by the NIH TL1 program at the Clinical and Translational Science Institute.

June 2, 2022 -The 9th Annual Bioinformatics and Computational Biology (BICB) Industry Symposium will be held on Thursday, August 25, 2022 on the Twin Cities campus of the University of Minnesota. The program will include faculty and student presentations, as well as a poster session covering wide-ranging topics in bioinformatics and computational biology. Transportation between the Minneapolis and Rochester Campuses will be provided. Agenda and registration information coming soon.

Mar 9, 2022: Congratulations from the BICB program to Prof.  Noelle Noyes . She has been named as the  2022-24 McKnight Land-Grant Professor . The award is given  junior faculty members with potential to make significant contributions to their departments and to their scholarly fields.

March 6, 2022 - Congratulations to our Michelle Cox (Adviser: Saad Kendarian). She was the recipient of the Best Abstract Award for outstanding basic/translational science research at the recently held Transplantation & Cellular Therapy Meeting in Orlando.

March 1, 2022 - Congratulations to our second year BICB graduate student, Alisa Nelson (Adviser:  Peter Crawford, MD, PhD ). She has been awarded the Interdisciplinary Doctoral Fellowship for the 2020-21 academic year by the graduate school.

April 28, 2022 - Congratulations to Alisa Nelson (Adviser:  Peter Crawford ),  Yao Gong (Adviser:  Yue Chen ) and Quincy Gu (Adviser:  Steven Hart ). All have been awarded the  Doctoral Dissertation Fellowship  for the 2022-23 academic year by the graduate school.

April 22, 2022 - Congratulations to Alisa Nelson (Adviser:  Peter Crawford ) and Quincy Gu (Adviser:  Steven Hart ). Both have been awarded the  Doctoral Dissertation Fellowship  for the 2022-23 academic year by the graduate school.

April 15, 2022 -  BICB fist post-covid social event was held at the CHS Field between the St Paul Saints and the Indianapoils Indians minor league baseball teams.

Feb 8, 2022: Congratulations to Sana Khan (Adviser: Lynn Eberly) and Weijie Zhang (Adviser: Stephanie Huang). Both have been awarded the 2022-23  Interdisciplinary Doctoral Fellowship  by the graduate school.

Sep 2, 2021: Congratulations from the BICB program to Prof.  Walter Low . He was selected by the American Society of Neural Therapy and Repair ( ASNTR ) as the recipient of the 2021 Bernard Sanberg Memorial Award, the highest award given for research on restoring neurological function.

Aug 26, 2021:The Annual BICB orientations was held virtually via Zoom and in-person to welcome our new graduate students and faculty from 4-5:30pm.

Aug 19, 2021: The 8th Annual BICB Industry symposium was held virtually on August 19, 2021 with external keynote speakers from IBM, Boeringher Ingelheim and Octant. More information of the symposium can be found  here .

Orientations will be held virtually via Zoom to welcome our new graduate students and faculty from 3-5pm. All members and affiliates of the BICB program are welcome. Click  here  or follow the information below to join remotely.

May 5, 2021: Congratulations to Mattea Allert (Adviser:  Ran Blekhman . She is one of the recipients of the  2021 President's Student Leadership and Service Award .

April 19, 2021 - Congratulations to Henry Ward (Adviser:  Chad Myers ) and Stephen Heinsch (Adviser:  Michael Smanski ). Both have been awarded the  Doctoral Dissertation Fellowship  for the 2021-22 academic year by the graduate school.

Apr 6, 2021:  Congratulations to Bhupinder Juneja (Adviser:  Kingshuk Sinha ) as a member of the student team that won the  U of M Interdisciplinary Health Data Competition  on Health Disparities & Community Impact of COVID-19.

Jan 14, 2021 - The 13th  Annual BICB Research Symposium  was held virtually. The theme for this year's symposium was "Data Analytics in Genomics and Imaging."

Jan 8, 2021 - Congratulations from the BICB program to Prof.  Wei Pan . He has been elected to the  Academy for Excellence in Health Research . He is the second BICB faculty elected to the academy since it was first established in 2003.

Dec 29, 2020 - Congratulations to BICB MD/PhD student Kevin Lin (Advisers:  Chad Myers ,  Anja Bielinsky ), who has been awarded an NCI/NIH F30 Fellows

Dec 10, 2020 - Congratulations to Megin Nguyen (Adviser:  Yuk Sham ). She has been awarded the 2021 UMII - MnDRIVE Graduate Assistantship.

September 1, 2020 - Annual BICB orientations will be held virtually via Zoom to welcome our new graduate students and faculty from 3-5pm. All members and affiliates of the BICB program are welcome. Click  here  or follow the information below to join remotely.

Aug 15, 2020 - Congratulations to Alisa Nelson (Adviser:  Peter Crawford ). She has been awarded the Interdisciplinary Doctoral Fellowship for the 2020-21 academic year by the graduate school.

July 22, 2020 - The 7th Annual BICB Industry Symposium will be held virtually on Aug 20,2020. The theme is " Combating the COVID-19 Pandemic ".  Registration  is now open. 

April 28, 2020 - Congratulations to  Serina Robinson  (Adviser:  Larry Wackett ). She is the co-recipient of the  2019 PNAS Cozzareli Prize  in biomedical science. As a Fullbright scholar, Serina spent a year with Dr. Mette Svenning in Norway studying methane consuming bacteria and their contribution to global warming.

April 20, 2020 - Congratulations to Tatiana Lenskaia (Adviser:  Dan Boley ). She has been awarded the  Doctoral Dissertation Fellowship  for the 2020-21 academic year by the graduate school.

April 8, 2020 - Congratulations to Breanna Shi, our incoming doctoral student this Fall. She has been awarded the  Diversity of Views and Experience (DOVE) Fellowship  for the 2020-2021 academic year by the graduate school. She will be advised by  Dr. Noelle Noyes .

January 14, 2020 - 12th Annual BICB Research Symposium was held at the University of Minnesota Rochester campus. 

  • October 25, 2019 - BICB open house was held at the Cancer & Cardiovascular Research Building (CCRB)  
  • August 16, 2019 - The 6th Annual BICB Industry Symposium was held at the University of Minnesota Twin Cities.  
  • August 27, 2019 - Mayo News Network has highlighted a new study, authored by our BICB PhD student Zachi Attia. The study, "Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs" was published in the scientific journal Circulation: Arrhythmia and Electrophysiology.  
  • August 27 and August 28, 2018 - Annual BICB orientations were held in both the Twin Cities and Rochester campuses this week to welcome our new graduate students and faculty.   
  • April 27 - Congratulation to Sambhawa Priya (Adviser: Ran Blekman). She has been awarded the  Doctoral Dissertation Fellowship  for the 2019-20 academic year by the graduate school.  
  • April 5 - We are now on  Linkedin . Come join us in promoting and tacking the bioinformatics and computational biology challenges in Minnesota!  
  • April 1, 2019 - New BICB initiative to provide research training to underrepresented undergraduate or post-bac students who are interested in pursuing advanced degree(s) in the BICB program. Awardees will be selected based on personal statement, diversity statement, and academic record. The deadline for application is April 12, 2019. For more information, click  here . To apply click  here .

February 11, 2019 - BICB PhD student, Nishitha Paidimukkala (Adviser: Rebecca Morris), was the highlight on a recent SciTechsperience article entitled " The Joy of Data: An Internship with StemoniX ". It is  a reflection on her industrial internship experience with a small startup company specializes in stem cell solution for drug development and discovery.

February 9 - Congratulation to Christopher Dean (Adviser: Noelle Noyes). He has been selected for the highly competitive IBM summer internship program. He will be working with some of IBM's newest bioinformatics databases and pipelines at the IBM's Almaden Research Laboratory in San Jose, California, this summer.

February 1- Congratulation to Tatiana Lenskaia (Adviser: Daniel Boley). She has been awarded the Interdisciplinary Doctoral Fellowship for the 2019-20 academic year by the graduate school.

January 22, 2019 - BICB PhD student, Richard Abdill, and faculty, Ran Blekman, were highlighted in recent Nature News entitled "What bioRxiv’s first 30,000 preprints reveal about biologists".

January 15, 2019 - The 11th Annual BICB Research Symposium was held at the University of Minnesota Rochester

January 7, 2019 - Mayo News Network has highlighted a new study, authored by our BICB PhD student Zachi Attia. The study, " Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram " was published in the scientific journal Nature Medicine.

November 1, 2018 - The University of Minnesota Research News highlighted a new study, authored by our BICB PhD student Pajau Vangay and BICB faculty Dan Knights. The study, "US Immigration Westernizes the Human Gut Microbiome" was recently published in the scientific journal Cell .

August 28 and August 29, 2018- Annual BICB orientations were held in both the Twin Cities and Rochester campuses this week to welcome our new graduate students and faculty.

August 23, 2018 - Congratulations to BICB doctoral student, Erik Gaasedelen, for been selected as one of the top 25 candidates in the Lyft and Udacity Partner for Self-Driving Hiring Challenge .

August 17, 2018 - BICB Rochester students visit the Twin Cities Campus for the 5th Annual BICB Industry Symposium .

August 6, 2018 - BICB PhD student, Michelle Cox, and faculty, Krishna Kalari, were highlighted in the recently held Computational Genomics Course at Mayo Clinic.

September 30, 2016 - Two BICB PhD students, Gabriel Al-Ghalith and Pajau Vangay, received MnDRIVE UMII Graduate Fellowships .

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2023-24 edition, mathematical, computational, and systems biology, ph.d..

The graduate program in Mathematical, Computational, and Systems Biology (MCSB) is designed to meet the interdisciplinary training challenges of modern biology and function in concert with existing departmental programs (Departmental option) or as an individually tailored program (stand-alone option) leading to a Ph.D. degree.

The degree program provides students with both opportunity for rigorous training toward research careers in areas related to systems biology and flexibility through individualized faculty counseling on curricular needs, and access to a diverse group of affiliated faculty and research projects from member departments. Current member departments include Biomedical Engineering, Biological Chemistry, Computer Science, Developmental and Cell Biology, Ecology and Evolutionary Biology, Mathematics, Microbiology and Molecular Genetics, Molecular Biology and Biochemistry, Chemistry, and Physics.

If you have any questions or would like to learn more about the MCSB Program, please email [email protected] .

Students interested in the MCSB Program apply to the Office of Graduate Studies (OGS). Applicants must specify that they wish to pursue the M.S. or Ph.D. Upon completion of the M.S., students who may wish to pursue a Ph.D. may request to be evaluated together with the pool of prospective Ph.D. candidates for admission to the Ph.D. program.

Applicants are expected to hold a Bachelor’s degree in one of the Science, Technology, Engineering, and Mathematics (STEM) fields. Applicants are evaluated on the basis of their prior academic record and their potential for creative research and teaching, as demonstrated in submitted application materials (official university transcripts, letters of recommendation, GRE scores, and statement of purpose).

Required Core Courses

Enrolled students participate in a common first-year “gateway” program and must complete the seven required core courses (listed above). Students are assigned an MCSB Advisory Committee consisting of two participating faculty members to oversee course and laboratory work. Subsequently, students select a thesis advisor and choose between the Departmental or Interdisciplinary (Stand-Alone) options for the remainder of their Ph.D. training.

Departmental Option

For students who select the Departmental option, a faculty member in a participating department must agree to serve as the student’s thesis advisor. Completion of the Ph.D. is subject to the degree requirements of the departmental Ph.D. program in which the student enrolls. Participating departments accept both the course work and research conducted during the “gateway” year in partial fulfillment of such requirements. Students are encouraged to consult with the department of choice for specific information on additional requirements. All department student advisory committees are established according to the rules of the participating department. In addition, the student’s MCSB Advisory Committee meets annually to follow progress and provide additional guidance. The normative time to degree for students in the Departmental option is five years.

To complete the coursework requirements for the Departmental option, students must:

  • Attend first-year bootcamp
  • Perform at least two laboratory rotations; one in an experimental (wet) lab and one in a computational (dry) lab
  • Complete the seven required core courses, in addition to any departmental requirements.

Interdisciplinary (Stand-Alone) Option

For students who select the stand-alone option, the student’s thesis advisor assumes the role of the Committee Chair when a participating MCSB faculty member agrees to accept that role. Adjustments to the MCSB Advisory Committee may be made based on the area of the student’s research, or by request of the student, thesis advisor, or committee members. The student meets biannually with the Advisory Committee until an Advancement to Candidacy Committee has formed, which then assumes the duties until the M.S. or Ph.D. defense. The normative time to degree for students in the Stand-Alone option is five years.

To complete the coursework requirements for the Stand-Alone option, students must:

  • Complete the seven required core courses, plus five elective courses selected from Breadth Categories I and II.

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2023-2024 Catalogue

A PDF of the entire 2023-2024 catalogue.

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  • Mission Statement
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Admissions Requirements for Bioinformatics Ph.D. Program

Thank you for your interest in the Bioinformatics & Systems Biology Graduate Program at UC San Diego. This is a full-time PhD program. Students are admitted as full-time PhD students. We do not admit "Masters-only" students.

Admission is in accordance with the general requirements of the graduate division. Candidates should have a quantitative or computational track record and a passion for working on challenging research questions in interdisciplinary areas across biology, medicine, computational sciences and engineering. The most competitive applicants have an undergraduate degree majoring in any of the disciplines in the biological sciences, the physical sciences, computer science, or mathematics, and a strong background in the complementary disciplines.

Applicants must apply online at https://gradapply.ucsd.edu and must submit a completed UC San Diego Application for Graduate Admission (use major code BF76). Applicants indicate their priority interest in the Bioinformatics and Systems Biology Track or the Biomedical Informatics Track; please see this page for further information on each track.

Fall 2024 Application Deadline: Wednesday November 29, 2023. Applications with fee waiver requests are due a week earlier.

Please expand the sections below for more information.

UCSD offers an online application for the Bioinformatics & Systems Biology Graduate Program. Hard copy applications are not available. Apply online at https://gradapply.ucsd.edu (use major code BF76 ). The online application system opens mid-September.

Students are only admitted during the fall quarter. The Fall 2024 Application Deadline is Wednesday November 29, 2023. Applications with fee waiver requests are due a week earlier.

Please send test scores to

  • Institution Code 4836 (UC San Diego)
  • GRE Department Code 0224 (Bioinformatics)
  • TOEFL Department Code 69 (Engineering, other)

For further admission information, students should see the Admissions FAQ or contact the Bioinformatics and Systems Biology graduate coordinator via e-mail at [email protected] or at (858) 822-0831.

To check the status of application materials that you have submitted, please email the Graduate Coordinator with your Name, Date of Birth, and Email Address used on your application. An email containing the status of your application will ONLY provide information verifying receipt of supplementary materials (transcripts, letters, etc.). Official notification of admission is distributed directly from the campus-wide Graduate Education office (GEPA).

Admission review will be on a competitive basis based on the combined elements of the application, which include:

  • Undergraduate / graduate transcripts (unofficial transcripts suffice for the application; English translation must accompany transcripts written in other languages)
  • Graduate Record Examination (GRE) General Test scores (optional for Fall 2024 admissions cycle; see notes below)
  • TOEFL scores (required ONLY for international applicants whose native language is not English and whose undergraduate education was conducted in a language other than English)
  • Statement of Purpose
  • 3 Letters of Reference from individuals who can attest to the academic competence and to the depth of the candidate’s interest in pursuing graduate study
  • Curriculum Vitae
  • Short answers to questions
  • Additional Educational Experience ( optional ; categories include: Community Involvement, Leadership, Overcoming Adversity, Personal or Professional Ethics, Research, Social Justice Experience, Other).  These responses will also allow you to be considered for Graduate Division Fellowships.

All applications will be screened and evaluated by the Admissions Committee with input from program faculty. Important factors in the holistic review of the application include:

  • Nature and quality of the undergraduate program
  • Undergraduate track record and other scholastic achievements
  • Preparation in quantitative and biomedical subject areas (see for example)
  • Proficiency with computation
  • Previous research experience, if any
  • Publications, if any
  • Evidence of qualities needed for success in graduate programs such as motivation, initiative, independence, commitment, and career plans
  • Interest in the program faculty
  • Additional educational experience

Strong applications will demonstrate aptitude for critical thinking, quantitative reasoning, computational and/or research experience, community engagement, motivation, initiative and perseverance.

  • Due to the impact of COVID-19, GRE General Test scores are optional for applications for Fall 2024 admissions. We encourage applicants to use GRE scores to the best effect for their applications. For our program, GRE General Test scores are considered as evidence of quantitative and analytical reasoning abilities. We encourage reporting of scores that provide support for this. Further context can be provided in the Statement of Purpose.
  • The application form for Fall 2024 admissions includes an optional COVID-19 Personal Statement section to address any impacts due to COVID-19.
  • For applicants who are required to take the TOEFL iBT or IELTS, we have added additional options for applications for Fall 2024 admissions: the TOEFL iBT Special Home Edition; and the IELTS Indicator. See more info here.

Are you an undergraduate who (i) is interested in learning about research as a career, or (ii) already has a passion for research and wants to learn more about the PhD path? If so, students of the UCSD Bioinformatics and Systems Biology PhD program are putting on a student-led info session aiming to:

  • Encourage undergraduate students to consider PhD programs as one of the fastest paths to leadership positions in academia or industry. We go over common requirements for (i) most STEM PhD programs, (ii) typical requirements of Bioinformatics PhD programs, and (iii) the specific requirements of the UCSD Bioinformatics PhD program.  
  • Raise awareness of the benefits of the PhD path. We want students to know that (i) you are paid a living wage as a PhD student, (ii) you typically don’t pay tuition for most Bioinformatics PhD programs, (iii) what the day-to-day life of a PhD researcher is like.  
  • Provide mentorship (limited availability) to undergraduates who are interested in applying to the UCSD Bioinformatics PhD program. This includes either a one-on-one meeting with a current UCSD Bioinformatics and Systems Biology PhD student and/or a review of application materials. If the student doesn't have the experiences or the classwork yet, we will advise them how to gain those experiences so they can have a competitive application next year.

How do I sign up?

  • The live info session will be held on Wednesday, November 1, 2023 at 3pm PDT.
  • One-on-one appointments and application advising will be available through mid-November 2023.
  • sign up for the info session;
  • sign up for a mentoring session;
  • request an application review;
  • or submit a question about the application process to current students.

Past info sessions

  • Nov 2023: [video] [slides]
  • Oct 2022: [video] [slides]
  • Nov 2020: [video] [tips & resources from students]

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Computational Biology, offered jointly between the Departments of Biological Sciences (Dietrich School of Arts & Sciences) and Computer Science ( School of Computing and Information ), will prepare students to understand core principles, models, and theories in the fields of biology and computer science and use them strategically to solve key problems in Computational Biology. Graduates will have the skills and knowledge to pursue graduate study or careers in industry.

Computational Biology Major Progress Tracker:

This major is designed to be completed in 4 academic years. Specific course sequencing cannot be changed. If you are looking for a major that can be completed in fewer than 4 academic years, we offer a BS in Biological Sciences which is much more flexible in course sequencing. Occasionally a student bringing in transfer credit may have a different timeline; please see a Bio Advisor to discuss.

It is the student's responsibility to track their progress through the major and general education requirements.  All Computational Biology majors are required to bring an updated progress tracker (and Old Gen Ed supplement, if applicable) to their pre-enrollment appointment in order to have their hold lifted. 

  • Computational Biology Major Progress Tracker  (with Gen Eds for students who started Fall 2018 and later)
  • If you started before Fall 2018, please use  this supplement  to track your General Education Requirements. 

Download a Computational Biology Major Sample Plan below:

  • Computational Biology Major Sample 4YR Plan

Access the Dietrich School of Arts and Sciences Major Sheet here: ​  Dietrich School Computational Biology Major Sheet

Use the following to help you compare choosing between Dietrich School and SCI:

CALS

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The Department of Computational Biology consists of faculty members with expertise in computer science, genomics, systems biology, population genetics and modeling.  They apply these skills to a wide range of exciting problems in the life sciences.

The department administers the Computational Biology undergraduate concentration within the  Bachelor of Science degree in Biology .  Additionally, the faculty in the department are members of the  Computational Biology graduate field , as well as several other graduate fields offering  M.Sc. and Ph.D. degrees .

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A new study testing the accuracy of existing methods used to predict the genetic variation that cause infertility found that relying on computational or in vitro experiments alone is insufficient.

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Five Johns Hopkins scientists named Sloan Research Fellows

Stephen fried, benjamin grimmer, justus kebschull, jonathan lynch, and yahui zhang recognized for their potential to become leaders in their respective fields.

By Aleyna Rentz

Five Johns Hopkins faculty members have been named 2024 Sloan Research Fellows , a prestigious award celebrating rising stars in academia. In all, 126 early-career scholars were recognized this year.

Awarded annually since 1955 by the Alfred P. Sloan Foundation , the fellowship honors exceptional U.S. and Canadian researchers whose creativity, innovation, and research accomplishments make them stand out as the next generation of leaders. Open to scholars in seven fields—chemistry, computer science, Earth system science, economics, mathematics, neuroscience, and physics—the Sloan Research Fellowships are awarded in close coordination with the scientific community. To date, fellows have gone on to win 57 Nobel Prizes and 71 National Medals of Science.

Image caption: The 2024 Sloan Research Fellows from Johns Hopkins are (clockwise from top left) Stephen Fried, Benjamin Grimmer, Justus Kebschull, Jonathan Lynch, and Yahui Zhang

Candidates must be nominated by their fellow scientists and winners are selected by independent panels of senior scholars based on a candidate's research accomplishments, creativity, and potential to become a leader in their field. More than 1,000 researchers are nominated each year. Winners receive a two-year, $75,000 fellowship which can be used flexibly to advance the fellow's research.

Including this year's winners, 87 faculty from Johns Hopkins University have received a Sloan Research Fellowship.

The five newest Sloan recipients from Johns Hopkins University are:

Stephen Fried

Assistant professor, departments of Chemistry and Biophysics

Artificial intelligence is surprisingly good at folding proteins into their correct 3-D structures, and yet proteins themselves are surprisingly not good at this task—oftentimes they are prone to "misfold" or stick together, which causes them to lose their functions and make cells sick. These processes are at the root of most neurodegenerative diseases such as Alzheimer's and may also be a factor in aging more broadly. Stephen Fried 's research pioneered the use of mass spectrometry proteomics to interrogate protein folding on the scale of entire proteomes. These studies have provided an array of insights on questions as diverse as the molecular basis of aging, the origins of life, and the function of disorder in the yeast proteome.

Benjamin Grimmer

Assistant professor, Department of Applied Mathematics and Statistics

Benjamin Grimmer has recently become fascinated with computer-aided optimization of the algorithms used to solve big real-world problems. A new wave of results in his field (optimization) has made computers provably good at this. Many of our now strongest algorithmic guarantees have only been made possible thanks to computer-assistance. Grimmer's research also recently had breakthrough results, covered by Quanta Magazine , showing that a new computer-aided analysis approach can beat the well-established textbook theory for gradient descent.

Justus M. Kebschull

Assistant professor, Department of Biomedical Engineering

Justus Kebschull 's research aims to understand the structure and function of the brain. To do so, he takes a comparative approach and engineers molecular, viral, and sequencing technologies to measure neuronal connectivity networks and gene expression at scale in disease models and a wide range of vertebrates. He developed the first barcode sequencing-based approaches to map neuronal connectivity, increasing throughput of single-neuron mapping by orders of magnitude and opening the door to single-cell comparative connectomics. He complements these barcoding approaches by in situ sequencing of barcodes and genes. Leveraging these technologies, his team asks questions including: How do new brain regions and connections evolve to support new computations? What are the organizing principles and fundamental circuit motifs of the vertebrate brain? And how do drugs of abuse and neurodevelopmental disorders break these principles? His work is highly interdisciplinary, residing at the interface of molecular engineering, neuroscience, synthetic and evolutionary biology, genomics, virology, and computational biology.

Jonathan Lynch

Assistant professor, Biochemistry, Cellular, and Molecular Biology Graduate Program

Animals, including humans, have stable relationships with communities of microorganisms collectively referred to as the microbiota. These communities profoundly influence the biology of their hosts, impacting host features such as immune function, metabolism, and even so-called "higher" traits such as cognition and social behavior. Due to the wide range of microbiota-associated effects on host biology, understanding host-microbe relationships is not only important for understanding the normal physiology of the host, but may also allow us to use the microbiota to intentionally shape host health. Jonathan Lynch focuses on several areas of host-microbe symbiosis, ranging from the fundamental features that govern these relationships to the translational prospects of using the microbiota to improve human health. This includes roles of intestinal bacteria in shaping host lipid and cholesterol metabolism, interactions between the microbiota and neurotransmitters, and the biophysical drivers of microbial colonization. He employs diverse techniques from molecular biology, biochemistry, and a variety of -omics platforms to explore our interactions with our microbial partners.

Yahui Zhang

Assistant professor, Department of Physics & Astronomy

Yahui Zhang works on theoretical condensed matter physics, which studies quantum materials with novel emergent properties due to the collective motion of many electrons. The current focus is in the following two directions: exploring new platforms for high temperature superconductor, for example, in bilayer nickelate material and in multilayer optical lattice; and engineering exotic fractional phases of matter in moire superlattices formed by twisting two sheets of two dimensional materials such as graphene.

Posted in Science+Technology , University News

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computational biology phd reddit

Professor Ji-Xin Cheng receives SPIE Biophotonics Technology Innovator Award

by Lea Rivel

Professor Ji-Xin Cheng is the recipient of the 2024 SPIE Biophotonics Technology Innovator Award . He earned this prestigious award for the “invention and commercialization of mid-infrared photothermal microscopy that allows highly sensitive dye-free bond-selective imaging of living cells and organisms.” The mIRage system, based on Cheng’s IP on mid-infrared photothermal microscopy and produced by Photothermal Spectroscopy Corp at Santa Barbara, has been delivered to over 100 research labs in 15 countries worldwide.

SPIE, one of the most prominent professional societies in optics and photonics, presents the Biophotonics Technology Innovator Award for “extraordinary achievements in biophotonics technology development that show strong promise or potential impact in biology, medicine, and biomedical optics.” According to their website, the award focuses on achievements that span across multiple disciplines and may include elements of basic research, technology development, or clinical translation. Awardees receive an honorarium of $2,000 for the achievement.

Ji-Xin Cheng

Related posts:

  • Ji-Xin Cheng Named 2022 BU Innovator of the Year
  • Ji-Xin Cheng Receives 2020 Pittsburg Spectroscopy Award
  • Professor Ji-Xin Cheng and Coworkers Published in Nature Communications
  • Professor Cheng Awarded $2.4 Million Grant by NIH

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  6. Algorithms for Computational Biology

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COMMENTS

  1. Am I competitive for Computational Biology/Bioinformatics PhD ...

    I believe my biology, computational (took a couple programming courses in Python), and research background is pretty sound. Much of my skepticism of my competitiveness stems from having a weak (?) quantitative background. I've only taken Calc 1 and biostatistics. I will be taking linear algebra soon.

  2. Computational Biology/Bioinformatics in PhD Portion of MD/PhD

    Jun 21, 2016 #1 Members don't see this ad. Hello. I've been strongly considering getting an MD/PhD. My current interests are in computational biology/bioinformatics, and image analysis/illness representations. I consider Radiology and Immunology to be particularly strong specializations to augment my research, which would mostly be computational.

  3. So you want to be a computational biologist?

    The term 'computational biologist' can encompass several roles, including data analyst, data curator, database developer, statistician, mathematical modeler, bioinformatician, software developer,...

  4. Computational Biology

    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.

  5. Computational Biology: Graduate School

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

  6. 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.

  7. Computational Biology < University of California, Berkeley

    Teaching. 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.

  8. World's best Bioinformatics and Computational biology universities

    1. Harvard University United States | Massachusetts For Bioinformatics and Computational biology # 1 in North America # 1 in the United States Acceptance Rate 4% Average SAT 1530 Average ACT 35 Net Price $13,910 Statistics Rankings 2. Stanford University United States | California For Bioinformatics and Computational biology # 2 in North America

  9. Ph.D. programs in Computational Biology at JHU

    Students in computational biology at Hopkins can enroll in one of four different Ph.D. programs. These include Biomedical Engineering, ranked #1 in the nation; Biostatistics, also ranked #1 in the nation; Biology, ranked #6 in the nation; and the rapidly growing Computer Science Department, ranked #23 in the nation.

  10. USC QCB

    The USC Quantitative and Computational Biology (QCB) PhD program trains students to apply cutting-edge computational and quantitative methods to solve complex biological problems. The program offers interdisciplinary courses, research opportunities, and mentorship across various fields of biology, computer science, mathematics, and engineering. Learn more about the QCB curriculum, faculty, and ...

  11. Bioinformatics and Computational Biology

    The vision of the Bioinformatics and Computational Biology (BICB) program to establish world-class academic and research programs at the University of Minnesota Rochester by leveraging the University of Minnesota's academic and research capabilities in partnership with Mayo Clinic, Hormel Institute, IBM, National Marrow Donor Program (NMDP), the...

  12. Mathematical, Computational, and Systems Biology, Ph.D

    The graduate program in Mathematical, Computational, and Systems Biology (MCSB) is designed to meet the interdisciplinary training challenges of modern biology and function in concert with existing departmental programs (Departmental option) or as an individually tailored program (stand-alone option) leading to a Ph.D. degree.

  13. Admissions Requirements for Bioinformatics Ph.D. Program

    Admission is in accordance with the general requirements of the graduate division. Candidates should have a quantitative or computational track record and a passion for working on challenging research questions in interdisciplinary areas across biology, medicine, computational sciences and engineering. The most competitive applicants have an ...

  14. Computational Biology (CB)

    The area exam in computational biology is an oral exam based on the student's specific course sequence. The student is examined by a panel of at least three faculty and must answer questions from those courses the student has covered in each of these three key areas: fundamentals of applied mathematics; fundamentals of biology and/or ...

  15. Computational Biology

    Computational Biology, offered jointly between the Departments of Biological Sciences (Dietrich School of Arts & Sciences) and Computer Science (School of Computing and Information), will prepare students to understand core principles, models, and theories in the fields of biology and computer science and use them strategically to solve key prob...

  16. Computational Biology

    Computational Biology The Department of Computational Biology consists of faculty members with expertise in computer science, genomics, systems biology, population genetics and modeling. They apply these skills to a wide range of exciting problems in the life sciences.

  17. Five Johns Hopkins scientists named Sloan Research Fellows

    Five Johns Hopkins faculty members have been named 2024 Sloan Research Fellows, a prestigious award celebrating rising stars in academia.In all, 126 early-career scholars were recognized this year. Awarded annually since 1955 by the Alfred P. Sloan Foundation, the fellowship honors exceptional U.S. and Canadian researchers whose creativity, innovation, and research accomplishments make them ...

  18. Professor Ji-Xin Cheng receives SPIE Biophotonics Technology Innovator

    Professor Ji-Xin Cheng receives SPIE Biophotonics Technology Innovator Award. by Lea Rivel. Professor Ji-Xin Cheng is the recipient of the 2024 SPIE Biophotonics Technology Innovator Award.He earned this prestigious award for the "invention and commercialization of mid-infrared photothermal microscopy that allows highly sensitive dye-free bond-selective imaging of living cells and organisms."