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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.

The Center for Computational Biology at Johns Hopkins University

PhD in Biomedical Informatics

The PhD program in Biomedical Informatics is part of the   Coordinated Doctoral Programs in Biomedical Sciences . Students are trained to employ a scientific approach to information in health care and biomedicine. Students may only enroll full-time, as required by the Graduate School of Arts and Sciences (GSAS). The first two years are generally devoted to coursework and research. Subsequent years focus on independent research that culminates in a dissertation. 

Our PhD students come from top universities in the country and around the world. The group is dynamic and engaged, breaking new ground in informatics research as evidenced by their strong publication records. Our students are highly collaborative, frequently assisting on each other’s projects, sharing ideas, and supporting each other.

The program consists of core courses that are required of every student and provide a foundation in general biomedical informatics methods, techniques and theories, while electives enable students to apply these methods to one or more areas of specialization in bioinformatics, translational, clinical informatics, clinical research informatics, or public health informatics. In addition, students conduct research, assist in teaching (if PhD or postdoctoral trainees), and attend colloquia.

Degree Requirements

​ Courses : A minimum of 60 points of Columbia University graduate (4000 level or above) coursework, 6 residence units, consisting of:

• Research each term (BINF G6001, BINF G9001)
• 5 core classes
• 2 domain (specialization) courses
• 3 educational objectives courses
• 1 ethics course (spring term of first year)
• serving as a TA for 2 classes (or 1 class for MD-PhD students)
• 1 research seminar each term

Students must complete a minimum of 60 points of Columbia University instruction at the 4000 level or higher, address any admission deficiencies, and complete DBMI degree requirements. In years three and above, research is the primary focus of the student’s degree program, and the number of hours spent on research increases with each year in the program. Students enroll in BINF G6001 fall and spring terms as follows: a) 6 points each term year one, 9 points each term year two, 12 points each term years three and above. Students enroll in BINF G9001 in lieu of BINF G6001 the term following successful completion of the Oral II/Depth Exam. In their final term of enrollment, students will also register for BINF G9999 Doctoral Dissertation for 0 points. Students should pursue five goals when conducting research, and the grade earned in the required research classes (BINF G6001, BINF G9001) will reflect how well the student has achieved these goals: 1) understand the nature of informatics research 2) master intellectual and technical skills necessary for research 3) read and apply the scientific literature, 4) develop skill in scientific writing 5) demonstrate a responsible working attitude.

Ethics:  PhD students are required to enroll in CMBS G4010 Responsible Conduct of Research and Related Policy Issues in their second term in the program.

Teaching Assistantship: Students are required to serve as teaching assistants (TAs) for two courses in the department. In order to earn credit for TA responsibilities, students need to register for two points of BINF G8010 MPhil Teaching Experience each semester in which they serve as a TA. Students and faculty are solicited in spring term for their top 3 preferences. The Training Committee assigns TAs based on faculty and student preferences and departmental needs. The assignments will be communicated to students and faculty by the Graduate Program Manager. PhD students are required to TA two courses. Two-year postdoctoral research fellows TA one course; three-year postdoctoral research fellows TA two courses. MD-PhD students TA one course.

Seminar: PhD students are required to enroll in the weekly DBMI seminar. PhD students in the bio track are required to enroll in the DBMI seminar in their first year in the program, and may substitute the Systems Biology seminar in year 2 and beyond.

Residence Units: PhD students accrue 6 residence units for the degree. They are enrolled in the appropriate residence unit category by the GSAS Office of Graduate Affairs every fall and spring term.

Milestones: There are four milestones for PhD students:

• Breadth Exam
• Dissertation Proposal
• Dissertation Defense

Academic progress is tracked each semester by the students and their academic advisors (see Forms page for semester forms)

Research Rotations With the exception of MD-PhD students whose research rotation occurs between years 1 and 2 of medical school, all PhD students rotate in two different research labs their first year. Research rotations begin by the end of the change of program (add/drop) period of each term. The second research rotation begins the first day of classes of spring term. Projects should be completed prior to the start of the subsequent term. The permanent research advisor is chosen by May 15 of the first year. The Training Committee grants final approval of research rotations and permanent research advisor selections. Work with the permanent research advisor commences the next business day following the last day of final exams. A third summer rotation is possible with the Committee’s permission.

For first year students rotating with different research advisors, the Fall term dates for the first research rotation of BINF G6001 are the second week of September through the MLK, Jr. Holiday. For Spring term, the dates are the day following the MLK, Jr. holiday (the first day of classes) until the last date of final examinations ( see the online University academic calendar ) .  Work with the permanent research advisor commences the next business day following the last day of final exams.

Rotation Research Advisor Prior to the start of the Fall and Spring semesters, first-year PhD students should contact the faculty with whom they are considering doing a rotation to request an appointment. Selection of a research rotation advisor must be official by the end of the drop/add period of each semester. Students should discuss expectations for the rotation as well as a finite project to be completed by the end of the term of the rotation with the research advisor. This prevents projects continuing into the next semester which impacts the output of the new research rotation. The project should not depend on applying for a new IRB as this will delay the research into the subsequent semester, which is ill-advised. The Training Committee grants final approval of research advisors.

Register for research credit and a letter grade (6 points in fall and spring of first year, 9 points in fall and spring of second year, 12 points in fall and spring all subsequent years).

Publications PhD students and postdoctoral fellows are expected to make submissions to publications and conferences each year. The frequency and appropriateness of these submissions are decided by the research advisor. No student or fellow may submit work to any publication or conference without the expressed prior approval of their research advisor. Prior to submission, the research advisor must review final versions of all papers and abstracts submitted to journals, conferences, books or other publications. This policy applies to all publications, regardless of authorship, that deal with work that has been done at DBMI, Columbia University, or any affiliated institution(s).

Funding More information about funding sources and fellowships is available in the Student Funding page .​

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PhD Program

Prospective students who have completed a bachelor’s degree may apply for admission to the PhD program. The PhD requires a total of 64 credits, consisting of lecture, laboratory and seminar courses and research credits. While there is a set of required core courses, the precise course of study will be determined in consultation with the student’s academic advisor, and will reflect the student’s background and interests.

In order to be admitted to PhD candidacy, students must demonstrate mastery of the core subject matter (no lower than a “B” in core courses) and successfully complete the oral qualifying examination by the end of the second year.

Required Core Courses

ENG BE 562: Computational Biology: Genomes, Networks, Evolution (4 cr.)

GRS MA 681: Accelerated Intro. to Statistical Methods for Quantitative Research (4 cr.)

ENG BF 690: Bioinformatics Challenge Project (2 cr./2 cr.)

ENG BF 752: Legal & Ethical Issues of Science and Technology  (4 cr.)

ENG BF 810 Laboratory Rotation System (1 cr each, 3 total)

ENG BF 820: Research Opportunities in Bioinformatics (1 cr.)

ENG BF 821: Bioinformatics Graduate Seminar (2 cr./2 cr.)

Choice of one Comp/Math core course :

CAS CS 542 Machine Learning (4 cr)

GRS MA 770 Mathematical and Statistical Methods of Bioinformatics (4 cr)

ENG BF 571: Dynamics and Evolution of Biological Networks (4 cr.)

ENG BF 768: Biological Database Analysis (4 cr.)

Choice of one Biology core course :

CAS BI 565 Functional Genomics (4 cr)

ENG BF 751 Molecular Biology and Biochemistry: Molecules and Processes (4 cr)

One non-research elective course (4 cr)

Lab Rotation Requirement

Three lab rotations are required during a Ph.D. student’s first year, each lasting approximately 9 weeks. One rotation must be experimental, one computational, and the third can be either. Lab rotations must take place at Boston University, on either the Charles River campus or the Medical School campus.  Only rotations done in laboratories located on-campus fulfill the rotation requirement.

Annual Report

All students are required to submit an annual report each fall. The report includes a list of courses completed, research projects and committee updates, journal publications, conference presentations or posters, teaching, Bioinformatics Community Service, financial support, report of oral examination, as well as a brief program evaluation.

Teaching Requirement

There is a one semester teaching requirement for all PhD students in the Bioinformatics Program.

Qualifying Examination

The goal of the oral qualifying exam is for the student to demonstrate his or her general proficiency in bioinformatics, as well as command of the area(s) in which he or she intends to conduct research. Each student in the Bioinformatics Program will select a Qualifying Committee (QC) of 4 faculty members in the program during the first semester of their second year.  The Qualifying Committee must include faculty members with biological/experimental expertise, as well as members with computational expertise.

Ph.D. Dissertation

All Ph.D. students are expected to defend the significance, originality and methodologies employed in their thesis research. This defense consists of two parts. The first is the public seminar open to the University community and based on the work by the student. The second is an oral defense of the work, which usually follows the public seminar, and is done privately before the student’s Thesis Committee. The committee members ensure that the research is complete and understood by the candidate. At this time they can voice any concerns over the data or the preparation of the dissertation document. Depending on how well the thesis experiments are designed, performed, and defended, and how well the thesis is prepared, the committee will vote whether or not the thesis is complete and satisfactory.

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Biomedical Informatics (PhD and MD/PhD)

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Graduate Committee Dr. Jenenne Geske (Interim Chair and Program Director), Dr. James Campbell, Dr. Scott Campbell, Dr. Martina Clarke, Dr. Jane Meza (or designee),  Dr. Ann Fruhling (University of Nebraska - Omaha representative), and Dr. Dele Davies (Senior Vice Chancellor for Academic Affairs).  

The mission of the Biomedical Informatics Graduate Program is to develop the next generation of biomedical informaticians who will advance research and practice in contemporary information and knowledge management using innovative evidence-based approaches to improve human health.  The Biomedical Informatics Graduate Program was formally approved by the Regents of the University and the State of Nebraska in the Spring of 2013.  This program brings together experts and resources from multiple campuses including the University of Nebraska Medical Center ( UNMC ), the University of Nebraska - Omaha ( UNO ) and the University of Nebraska Lincoln (UNL).

This joint program involving UNMC & UNO leverages expertise across campuses to provide an educational and research program with strengths in biologic, health care and technological aspects of biomedical informatics. It is a multidisciplinary, interprofessional effort integrating the theory and practice of information technology management, computer science, decision support systems, and applied computing with clinical science, biological science, bio-imaging, and public health. 

General Requirements for PhD 

• Completion of Coursework.
• Completion of the Comprehensive Examination.
• Completion of a research project consistent with a PhD level of achievement.
• Completion and successful defense of a doctoral dissertation.
• Concurrence of the mentor and the student's Supervisory Committee. 

PhD Coursework

Students pursuing the PhD degree in Biomedical Informatics must complete the courses listed below.  PhD students with prior education can place out of core courses.

MD/PhD Coursework

MD/PhD students in Biomedical Informatics must complete 18 credits of graded (not Pass/Fail) graduate-level courses. Students with prior education can place out of core courses, but will need to take an elective in that same core.  

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MD-PhD Program

Integrate Science and Medicine.

Train for a career as a translational scientist..

We help you integrate your clinical and basic-science talents early, establishing a firm foundation for your career as a physician-scientist. You'll learn interdisciplinary skills and acquire the comprehensive perspective translational research demands.

Experience Immersive Clinical Training

Your intensive training will prepare you to join teams that are advancing health care nationally and internationally.

Explore Broad Research Possibilities

Diverse research opportunities form the heart of our program. Whether your calling is in the basic sciences, public health, engineering, or cancer sciences, we're sure you'll find your fit here.

Connect Medicine to Research

You will train in both tracks concurrently throughout your MD-PhD program, cross-applying your insights into research and patient care.

Clayton Brady MD-PhD student

Elliot Kramer MD-PhD student

How It Works

Our program's structured curriculum and one-on-one advising give you the resources to succeed on your path to becoming a clinical translational scientist with an average program duration of seven to eight years.

First Two Years: MS 1 & 2

• Clinical and didactic coursework alongside medical students
• Laboratory Rotations
• Bi-weekly MD-PhD Seminar

PhD Training: Avg 3-5 years

• Advanced basic science coursework
• PhD thesis research
• Longitudinal clinical mentoring program

Final Two Years: MS 3 & 4

• Clinical rotations and electives
• Prepare for Residency or Post-Doc

By the Numbers

• Average Years to Completion: 7.8 
• Current Students in Program: 30
• Current F30 Awards: 8
• Students from Out of State: 14

Current numbers as of Fall 2022.

Residency Placements

• Internal Medicine: 11
• Neurology: 3
• Psychiatry: 3
• Radiology: 3

Classes of 2014-2021

PhD Program Options

Jacobs school of medicine and biomedical sciences.

• Biochemistry
• Biomedical Informatics
• Genetics, Genomics & Bioinformatics
• Microbiology & Immunology
• Neuroscience
• Oral Biology
• Pathology & Anatomical Sciences
• Pharmacology & Toxicology
• Physiology & Biophysics
• Structural Biology

School of Public Health and Health Professions

• Epidemiology
• Nutrition & Exercise Science

School of Engineering and Applied Sciences

• Biomedical Engineering
• Chemical & Biological Engineering
• Mechanical & Aerospace Engineering

Roswell Park Comprehensive Cancer Center

• Cancer Prevention
• Cancer Genetics, Genomics, and Development
• Tumor Immunology
• Cell Stress and Biophysical Oncology
• Experimental Therapeutics

Research Areas

We’re sure you’ll discover your next home with us.

MD-PhD News

• 6/3/24 PhD White Coat Ceremony Honors Student Advancement
• 5/29/24 UB Awards 320 Biomedical Science Degrees; 35 Earn PhDs
• 5/8/24 Jacobs School Students Feted for Academic Excellence
• 3/13/24 Cancer and Nutrition Talk Wins Prize at 3MT Event

Yuhao (Tom) Shi MD-PhD student

Ready to Start your journey?

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Focus: Educating Yourself in Bioinformatics

Perspectives on an education in computational biology and medicine.

The mainstream application of massively parallel, high-throughput assays in biomedical research has created a demand for scientists educated in Computational Biology and Bioinformatics (CBB). In response, formalized graduate programs have rapidly evolved over the past decade. Concurrently, there is increasing need for clinicians trained to oversee the responsible translation of CBB research into clinical tools. Physician-scientists with dedicated CBB training can facilitate such translation, positioning themselves at the intersection between computational biomedical research and medicine. This perspective explores key elements of the educational path to such a position, specifically addressing: 1) evolving perceptions of the role of the computational biologist and the impact on training and career opportunities; 2) challenges in and strategies for obtaining the core skill set required of a biomedical researcher in a computational world; and 3) how the combination of CBB with medical training provides a logical foundation for a career in academic medicine and/or biomedical research.

Introduction

Over the past few decades, high-throughput assays producing large-scale datasets have become mainstream research tools [ 1 - 4 ]. In response to the need for scientists with facility in this multi-dimensional data space, the fields of Computational Biology and Bioinformatics (CBB) have emerged as major players in modern biomedical research [ 5 , 6 ]. Several years have passed since formalized programs in CBB graduated their first PhD students [ 7 - 10 ]. With these scientists finding roles as academic faculty running independent research labs, as principal PIs on major grants, and as key players within the industrial hierarchy, it is fair to say that the field is firmly established as a legitimate arm of biomedical research.

Despite widespread recognition of the importance of dedicated training in CBB, challenges remain in establishing the specific content of such training in a rapidly evolving field [ 11 , 12 ]. Difficult as this can be for program directors responsible for defining prerequisite and core curricular courses for graduate programs, students also face a degree of uncertainty about how best to ensure their preparedness for the careers that await them. In one sense, this uncertainty reflects the natural and healthy insecurity of the early graduate student who is learning to navigate a new environment, particularly in such a diverse field. Yet, given the youth of the field and the resultant scarcity of program alumni working within any particular CBB specialty, it is understandable that many students would like to identify a core skill set that we feel comprises computational biologists’ most basic common armamentarium. Below, I will describe my version of such a skill set and attempt to pinpoint where in my education I had the greatest opportunity to acquire these skills.

With the decreasing cost and increasing availability of high-dimensional molecular interrogation techniques, the pressure to begin realizing progress not only in the basic sciences but also in the clinical realm is ever growing [ 13 ]. The relative paucity of scientists trained to responsibly implement these research techniques and the veritable dearth of active clinicians with experience in this realm presents a very real problem as we increasingly integrate computational biological approaches in basic science, translational research, and clinical applications. Rather than allowing the haphazard implementation of bioinformatics-driven tools in the clinical setting (or, worse yet, allowing for-profit industry to drive the effort), it behooves us to make conscious decisions about the process through which we develop, test, and adopt the use of such tools. In particular, how do we facilitate productive collaboration between CBB researchers and their clinical counterparts, who should be driving these efforts, and what training do such people need?

A Computational What Now? Changing Perceptions and Awareness of CBB

In the roughly 10 years since the beginning of my graduate education, there has been a significant shift in the perception of the role of the computational biologist. When I first started, the fields of Computational Biology and Bioinformatics had only recently been recognized as formal disciplines for which organized and directed educational programs should exist. Happily, major research universities across the country had committed to training scientists in CBB, and there were multiple nascent PhD programs from which to choose. Yet, there seemed to remain a degree of skepticism among some traditional basic science researchers regarding the necessity of establishing the field as its own entity. As I toured the major academic centers interviewing for MD/PhD programs, I had the sense that a few of the scientists I encountered regarded the bioinformatician as a member of the support staff whose skill set was needed in the functioning of a modern lab, but who shouldn't aspire to stand alone as an independent researcher.

Although the skepticism I met with on the interview trail did not accurately represent the governing attitude at my chosen institution, the young age of our graduate program could be seen reflected in the confused expression of many who asked about my research interests. CBB as a field of study had not yet been universally recognized. While early bioinformatics research tools such as microarrays were already in widespread use at the time, because they were relatively new and fairly expensive, they remained the domain of a minority group of specialists. Still, while there weren't an endless number, there were ample labs in which to do research rotations and from which to choose a PhD mentor.

My first jolt of recognition that computational biology had hit prime time came a few years after I started the MD/PhD program. I was sitting in a Department of Surgery Grand Round when a heat map flashed on the screen, and I learned that the presenting surgeon ran a lab that was using microarray data to search for biomarkers for the risk of abdominal aortic aneurysm. Shortly thereafter, while shadowing a general surgeon in the office setting, I learned about Oncotype Dx, a tool for predicting the likelihood of recurrence and the potential benefit of chemotherapy in certain types of breast cancer [ 14 , 15 ]. This was the first time I had seen the clinical implementation of a tool that derived directly from bioinformatics-driven research.

In the past few years, large-scale assays have become such standard tools in biomedical research that I am no longer surprised when presented with high-dimensional data in forums not traditionally associated with CBB. With the increasing availability and decreasing cost of these assays, many scientists are including investigations of this type in their research. As someone invested in CBB as a field, I am nervous that too many scientists are generating mountains of data without first defining clear hypotheses and research questions. At the same time, I am excited by the explosion in opportunities for collaboration. It would seem that those of us with specific training in computational biology are currently in high demand. Having recently completed the residency interview trail, I was struck by the contrasting attitudes with which I was met as a computational biologist now as opposed to during my MD/PhD interviews eight years ago. While some misperceptions still exist about what my research entails, it was clear that most of my interviewers possessed at least a basic understanding of the field and were aware of the value of graduate training in CBB.

What Does a Computational Biologist Need to Know?

The computational biologist can play a key role in capitalizing on the power of available technologies and in ensuring their responsible application. However, defining the core set of required skills and organizing a curriculum that will prepare scientists for this role is a daunting task. It is imperative that these scientists are properly trained to responsibly design large-scale experiments, analyze immense datasets, and draw biologically and/or clinically meaningful conclusions. Equally important is the ability to ask meaningful questions and communicate effectively with more traditional bench scientists and, perhaps, clinicians.

In my mind, the key elements of my education can be grouped into three broad categories. The most easily identifiable of these is the one that students seem most preoccupied with in the early years. That is, what are the concrete skills I need first to qualify for training in this field and second to function as a computational biologist at the end of my training? The question of prerequisites seems to have become less frequent in recent years with the increasing availability of CBB courses and programs at the undergraduate level, but it is still a difficult one to answer. In the early years of the program, I remember my program director expressing the opinion that it is slightly easier to teach a computer scientist about biology than vice versa, but that a strong background in either was sufficient at least for consideration for our program. These days, students are expected to arrive with a solid foundation in the basics sciences as well as significant programming experience. While these are reasonable requirements for today's climate, based on my own experience, I would encourage those who aren't sure if they qualify to speak with prospective program directors before they write themselves off.

All too often I hear good students, with genuine interest in the field, dismissing CBB as an option because they lack confidence in their level of computational preparedness. For those who fall into this category, I will share that I was an Architecture major in college. I did complete a master’s degree in Computer Science, but to my chagrin, the few years I spent pursuing that degree had not transformed me into an expert of the type who had been building computers in her parents’ basement since age 9. Still, I gained the qualifications I needed: I learned to program. Although my relatively limited background did create more work for me at the beginning of training, my skills quickly improved and did so in accord with the specific needs of my research. As for my basic science background, the intensive post-baccalaureate pre-medical program I completed just before beginning graduate school provided a strong foundation for my future studies.

Overall, in thinking about the variety of backgrounds among students in my program, it is clear that there is no one set of prerequisites that constitutes the best preparation. In fact, I consider the varied perspectives arising from our diverse backgrounds to be a great strength of graduate training in CBB. In contrast to this flexibility in specific prerequisites, there are a few concrete computational skills that all students should possess before finishing training. In my mind, the most fundamental toolbox of the computational biologist should include the following minimum competencies:

• Expertise in a scripting language (ex. Perl, Python)
• Expertise in a statistical environment (ex. R, Matlab)
• Facility with database design, management, and use (ex. SQL)
• Facility with biostatistics
• Experience in a compiled programming language (ex. C++, Java)

As for how best to attain these skills, it seems dedicated coursework is a necessary first step for most of us. However, in terms of gaining a reasonable comfort level in any of these areas, for me there is no greater motivation than to be confronted with the specific needs that arise in the course of research.

Complementing these hands-on abilities is the second broad category of educational content, perhaps best described as the knowledge base. Examples of this type of core content include sequence alignment approaches, macromolecular simulation, biomedical data modeling, and standardized biomedical ontologies, to name a few. This category gives rise to a language and culture that is specific to CBB. It is the esoteric content, the common ground that allows two computational biologists to speak in shorthand with one another. This is a more difficult concept to define than the specific, nameable skills listed above, but here again the beginnings of this knowledge base can be found in the core CBB coursework.

The coursework should be augmented at first with broad reading of the literature, to be narrowed accordingly as particular research interests arise. Beyond this, I have found it is beneficial to pay deliberate attention to the ways in which researchers communicate with each other, be it in desk-side chats, in lab meetings, or in organizing and delivering talks. During medical school, listening to doctors discuss clinical cases with other doctors was among the most educational ways I could spend my time. It is through such observation, and gradually escalated levels of participation, that I learned to appreciate the clinical nuance and varying priorities that drive patient care for different specialists. I believe this same opportunity exists in graduate education for those who are astute observers.

Finally, there is the educational content I think of as the hidden curriculum. It is both the most abstract and the least specific to any particular field. It includes such abilities as critical thinking, organization, creativity, and scientific discipline. Beyond this list of desirable attributes, here I include self-awareness, communication skills, diplomacy, and academic political savvy. This third category is by far the least tangible, but arguably the most important element of an education. These are exceedingly difficult skills to teach programmatically, but instead seem largely perpetuated by mentorship, role modeling, and institutional culture. In deciding which graduate program to matriculate in, and particularly which lab to join, it is therefore worthwhile to go beyond considerations of specific research strengths and attempt to get a real sense of the governing culture. In choosing a lab, one might want to look for evidence that students are encouraged to think creatively and rewarded for taking the initiative to pursue their ideas. Part of graduate education is learning to take your place at the table. That is, students should gradually find their voices and understand that they can make valuable contributions to the discussion. The culture of the environment in which we train and the leadership styles to which we are subjected could either promote or delay this process and will inevitably influence the way we carry ourselves when we ascend to the supervisory role. It is therefore critical that we are cognizant of the type of mentorship we are receiving and that we aren’t afraid to seek guidance from those we most respect and wish to emulate.

Is There a Doctor of Bioinformatics in the House? Integrating CBB and Medicine

At the beginning of my graduate education, it was explained to me that the nature of MD training is very different from that of PhD training. The medical student is expected to memorize details of pathologies, learn to recognize constellations of signs and symptoms, and apply management paradigms that may or may not be based on a complete understanding of a condition’s pathophysiology. In contrast, the PhD student is encouraged to be more questioning from the outset, read each journal article with a hyper-critical eye, and always attempt to attain an intimate understanding of the underlying mechanisms of the system in study. While there are certainly plenty of times when graduate students accept facts at face value and medical students think critically and creatively about patient care, ultimately the differing priorities of the researcher and the clinician cause each to emphasize the acquisition of different skill sets. It is the goal of MD/PhD training to produce physician-scientists, whose dual educations position them at the boundary between the research and the clinical.

This concept of physician-scientist as the bridge between bedside and bench is far from a new one, but I would argue that there is currently an under-utilized niche for the computational biologist in this dual role. The doctor’s clinical experience provides understanding of the physical manifestation of disease processes and their impact on the patient, as well as insight into the ways our treatments work or don’t work. For those who are paying attention, the clinical arena is a veritable breeding ground for important and interesting research questions, the answers to which can change the way we practice medicine. For the computational biologist, the goal is to devise and implement methods that capitalize on the recent drastically increased availability of massive amounts of data and impressive processing power to address these questions.

One major obstacle computational biologists face in achieving translational progress is the historical lack of standardization in the way data, particularly clinical data, is collected and stored. In the current system, more often than not the clinical data with which we work is entirely divorced from the molecular. While this is partly due to issues of informed consent and patient confidentiality, the missed opportunity we incur by not going through the proper channels to ensure compliance with these important considerations is too great. Beyond issues of confidentiality, perhaps the main barrier here is the relatively slow adoption of the electronic medical record (EMR) into standard practice and the low priority of research functionality in many of the early systems. Maximizing the clinical utility of the EMR while still providing quality research utility is far from a trivial task and is itself an example of an area where computational biologist doctors might make a real impact.

The past decade’s increased adoption of high-throughput technologies has defined an important role for computational biologists in translational research. A few characteristics of CBB make it a very amenable field for dividing time between research and clinical responsibilities. Already accustomed to navigating between the different research cultures of the computationalists, statisticians, and basic scientists, the computational biologist’s experience in bridging communication gaps can be a major asset in facilitating productive collaborations between researchers and clinicians. From a logistical standpoint, the computational biologist has great flexibility in the planning and execution of experiments. The availability of remote access to computational resources means there are fewer constraints in determining exactly when and where the work is carried out. This simple reality greatly eases the burden of balancing time in the lab with the more rigid time constraints imposed by one’s clinical obligations, an important consideration that is easy to overlook while still a student.

Conclusions and Outlook

Since its formal beginnings as a field not much more than a decade ago, computational biology has become established as a central discipline in biomedical research [ 5 , 6 ]. While the detailed content of graduate CBB training continues to evolve with the technology, more than a decade of development as an academic field has created an essential common skill set and knowledge base shared by experts in the field. For those wishing to apply their CBB expertise in translational research, it is important to recognize that the climates of MD and PhD training are very different and often emphasize opposing skills. These differing priorities, perceived in the way doctors and researchers think and communicate, create a need for physician-scientists with combined MD/PhD training; while this is true in all areas of biomedical research, recent advances in our technological capability have created a particular need for computational biologists in this role.

There is growing pressure from the health care establishment and also the general public to realize the clinical potential of affordable, large-scale molecular interrogation [ 13 , 16 , 17 ]. However, the very availability of these high-throughput technologies has perhaps created a false belief that we are further along the path toward individualized medical care than we actually are. I believe the next decade will witness large advances in our molecular-level understanding of complex diseases, which will in turn provide opportunity for significant improvements in our current standards of care. However, there is danger that research efforts driven by those without sufficient understanding of large-scale data and CBB methodology could lead to the development of sub-optimal or even flawed clinical tools. Given the significant pressure from health care institutions, funding agencies, and industry to speed clinical advances with new technologies, there is not only an opportunity but a responsibility for those completing dual training in CBB and medicine to guide the translation of the knowledge gained from high-dimensional assays into clinically informative tools. Existing MD/PhD programs have a strong tradition of training physician-scientists to guide translational research efforts; with the coming of age of graduate education in CBB, I believe we will soon see a steep increase in the number of MD/PhD students choosing this field of research to complement their medical educations and, ultimately, their careers in academic medicine.

Abbreviations

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• Computational Biology & Biomedical Informatics (MS Program)

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

• Programs of Study
• MS - Master of Science
• Yale Computational Biology and Bioinformatics
• Computational Biology and Bioinformatics

Cynthia Brandt

Director of Graduate Studies

• [email protected]

Robin Einbinder

Departmental Registrar

• [email protected]

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GRE is not accepted.

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

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

Admission Information

The terminal Computational Biology and Bioinformatics Master’s program is intended for students interested in biomedical informatics training offered through a new academic unit at Yale the called the Section of Biomedical Informatics and Data Science. Those with questions about the Master’s degree may contact the Master's program coordinator ( [email protected] ) or the CBB registrar ( [email protected] ).

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