Wednesday, March 7, 2012

Monday, March 12, 2012

ISCB Education Committee Seeks Input on
Identifying a Consensus Bioinformatics Curriculum

International Society for Computational Biology
A Report of the Curriculum Task Force of the ISCB Education Committee

Draft approved by the Curriculum Task Force of the ISCB Education Committee on March 1, 2012

Open for comment from the ISCB membership
Comment period closes March 30, 2012

The International Society for Computational Biology (ISCB) is dedicated to advancing human knowledge at the intersection of computation and life sciences. The ISCB Education Committee (EduComm) promotes worldwide education and training in computational biology and bioinformatics and serves as a resource and advisor to organizations interested in developing educational programs.  We hereby present (draft) guidelines on the important issue of curriculum for computational biology and bioinformatics.

The topic of curricula for bioinformatics programs has long been of interest to ISCB and EduComm. Dr. Russ Altman, a founding board member of ISCB and past-president, has been associated with one of the first degree programs (at Stanford University) and wrote an article on the topic of curriculum. Dr. Shoba Ranganathan, as chair of EduComm a decade ago, began organizing a yearly Workshop on Education in Bioinformatics (WEB) at ISMB (Intelligent Systems for Molecular Biology) meetings that generated exchange of information and many productive discussions. Curriculum development was one aspect of bioinformatics education covered in these sessions.

 The field of bioinformatics has grown in the past decade. There are many such degree granting programs around the world at the bachelors’, masters’, and Ph.D. levels. This article provides a status report of the EduComm’s ongoing endeavor to provide a set of curricular guidelines for bioinformatics education at all levels.  The Curriculum Task Force of the EduComm conducted a survey in spring of 2011.  Analysis of the survey produced an initial set of recommendations in the form of a first draft of a curriculum.  The EduComm will continue to refine its results. Individuals who are interested in contributing to this initiative are encouraged to contact the Chairs of the ISCB EduComm.

The results of our survey were presented at the Third RECOMB Satellite Conference on Bioinformatics Education (RECOMB-BE), held in Vienna, Austria in July 2011.  The purposes of this article are to further disseminate the survey results and to solicit participation in the initiative.  The survey questions and responses are presented, the draft curriculum is defined, and the next steps of the EduComm Curriculum Task Force are outlined.


  1. We are looking forward to receiving your comments!

  2. We are particularly interested in hearing from you about:
    1) ideas for publicizing these efforts and involving a broader cross-section of the computational biology community
    2) the draft curriculum (a link to the document is provided on the blog page)
    3) the plans to refine the curriculum (see the final paragraph of document)


  3. Dear colleagues,

    the draft curriculum looks good and covers most of what you would expect in the first two years of a bioinformatics curriculum: a mix of foundations in maths/CS/statistics and life sciences/chemistry.
    What I believe is missing is an intro to physical chemistry that would be required for much of the research in structural bioinformatics.
    At the same time, I was a bit disappointed: the list is just the consensus that is - apologies for being blunt - totally obvious.
    My hopes for this initiative were a bit higher. What is missing in the curriculum is: bioinformatics.
    Bioinformatics is more than just a 50/50 mix of CS and biology, as I am sure we all agree.

    After the foundations have been laid in CS and life sciences (2nd - 3rd year), it would be good to have a
    core curriculum that does not cover all ISMB core areas, but that covers a reasonable cross section of these

    There are of course several ways to structure these core contents and many people will feel the tendency
    to integrate their favorite topics here.

    After many internal discussions, we decided to structure the core contents, i.e. the part of the bioinformatics
    lectures that are mandatory, into the four key topics:
    - sequence analysis (alignments, assembly, sequence searches)
    - evolution (mechanisms, phylogenies)
    - structures (RNA, proteins, force field methods, secondary and tertiary structure prediction)
    - systems (omics technologies, data integration, statistical methods for omics data analysis)

    Anything more specialized we teach (e.g., metagenomics, immunoinformatics, computational
    metabolomics, ...) can then build upon these foundations.
    Many of the ISMB topics will of course fit into these four major topics mentioned above.

    I would very much like to see 'proper' bioinformatics included as a third column of the suggested
    curriculum. And I would like to label it as such. Of course it is possible to teach Smith-Waterman
    in the context of an algorithms lecture or even as part of a genomics class. I would not recommend
    it, though.

    In my experience, many students entering a bioinformatics program start wondering: 'what the heck *is*
    this bioinformatics thing?' after taking classes in maths, CS, biology, physiology and what not.
    Showing them that bioinformatics is really bringing all this together and is a discipline on its own
    helps them identifying with the field.

    Just my 2 cents,

    1. Dear Oliver,

      You are correct that this is to some degree an obvious consensus and that is part of why we are posting it as is. Our purpose is not to tell the field what should be in a bioinformatics curriculum but to start a discussion of best practices that will end with some really useful practical advice for those running programs in these areas. I think any of us who run a training program do quite a bit beyond what ended up in the consensus of that initial survey. We do not necessarily do the same things, though, and part of our goal is to be able to compare what different programs are doing and hopefully learn from one another.

      For example, you consider physical chemistry and structural bioinformatics important parts of a training program. Some existing training programs agree, but others omit computational structural biology entirely. We could observe the same heterogeneity across training programs for many areas of study in computational biology (e.g., does a computational biologist need to understand biochemical simulation methods?) as well as foundational areas of computer science (e.g., what kind of foundation do you need in discrete algorithms or machine learning?) or biology (e.g., how strong does a computational biologist need to be in cell biology or classical genetics?). We hope for responses like yours to tell us what your experience says is the core to be expected in a training program, and hopefully for programs with different expectations to defend their views. And we would like to know how these expectations match up to job opportunities for graduates at all levels.

      There is work in progress on our three subcommittees to go beyond the consensus of the initial survey, to assess the range of current educational practices and to see how well those match to job opportunities in the field. These efforts need more community input, however, and that is really what we are trying to accomplish by putting out this first summary, which hopefully illustrates just how far we are from having any sort of consensus on what a program needs, either at the foundational level or at the level of advanced specifically interdisciplinary material.


  4. There is a related effort going on at the American Medical Informatics Association ( ). There is a document being circulated by Ted Shortliffe called "Biomedical Informatics Core Competencies" which I think dovetails nicely with much of the work you have done. It would be really good for the field if their work and yours were coordinated as much as possible.

    Also, while I think a curriculum is valuable, I also am interested in higher-level statements of educational goals. Since the content of our field changes so rapidly, we have to update our curriculum pretty much every time we teach our core courses. However, our educational mission statement ( ) has not changed much over the years, and constantly informs how we update our curriculum.


  5. Table 1 is misleading in at least two ways. First, the two columns indicate no integration. For example, the key concept in cellular and molecular biology is the cell as a system with dynamic behaviour determined by the interaction among cellular entities (among proteins, between protein and DNA/RNA, between proteins and ligangs, etc.). In particular, how the system achieve its robustness against the perturbations and how the three essential biological processes (DNA replication, transcription and translation) are acomplished with high efficiency and high accuracy. Associated with these are stochastic processes, operations research, and graph theory in statistics/math, data structure and algorithms associated with tree/graph traversal and decision-searching in computer science, etc. Of course, stochastic processes and tree/graph traversal are also essential for phylogenetics (in evolutionary biology).

    Statistical estimation (e.g., maximum likelihood method and the associated EM algorithm) is needed in every area in biology nowadays.

    The second misleading aspect in Table 1 is that a reader not in the area may think that the two entries in each row are somewhat associated.

    We should break biology into may categories as column headings, computer science into many categories as row headings, and math/stats into many categories as "depth" headings. In other words, we need to have these three essential dimensions to help us find what high-throughput biology needs computation and math/stats.


  6. Dear Larry -

    Thank you for the pointer to the related initiative of the AMIA. The ISCB Education Committee would like to join forces with this initiative, as much as makes sense. Can you help us to open a dialogue with the appropriate AMIA people?

    You make some good points about higher level goals. Thanks for providing an example of such goals; we will include this in our survey of existing curricula. Similar things are also falling out of our surveys of (1) career opportunities and (2) directors of bioinformatics core facilities.


  7. Dear Xuhua -

    Thank you for your comments. I appreciate your points about fleshing out the details in each of the areas. As we move forward with refinement of the initial ideas, this will certainly be one of the goals. Of course, we need to balance the tension between specificity relevant to the technology at one point in time and generality that is achieved by higher-level goals and categories (e.g., see Larry's post). We welcome your involvement in the process as we proceed.

  8. The curriculum does appear to be a bit of a shopping list. There seems to be lacking a consistent pedagogy of bioinformatics.

    There is also a risk of siloisation - this is the biology module, that is the programming module, over there is the stats module.

    As we are progressing through our wholescale revision of our undergraduate life sciences curriculum, I have been focussing on three facets of knowledge. These can be described as Knowledge, Ability, and Understanding. The three are interlinked.

    For example: In the central dogma at first year we teach DNA structure. The students KNOW that DNA is made up of 4 bases and can code for proteins in triplet codons, they UNDERSTAND the base pairing principle and how to translate a nucleotide to a protein, they are ABLE to calculate a base pair composition and do a simple database sequence search. There is an element of training (How To) but also a strong emphasis on education (Why To)

    We need to decide what the overall curriculum is *as a coherent whole* so that we teach the knowledge, the understanding and develop the relevant abilities to allow the student to explore and develop their understanding in the subject. The difference between a biology course and a bioinformatics course is that we use keyboards instead of pipettes, and we develop a concept of modelling data and understanding how that can develop insights.

    The implications of this are that the modelling is taught alongside the biology. There will be a need for specialist modules that cover the modelling techniques background, but teaching biology *and computationally manipulating it at the same time* are what is required.

    So what are core techniques? I would suggest that a competent[1] level of programming and understanding of the tools is equivalent to the introductory wet lab skills course - software engineering is an advanced module. Basic stats (significance testingetc), data access and searching tools, literature searching.
    What is core knowledge? Central dogma. Common experimental techniques and approaches (Genomics, function prediction, phylogeny, proteomics, expression analysis) Basic cell biology and communication, the systems approach to biology.
    What is core understanding? How this all fits together. That experiments in silico are analagous to experiments in vitro and have to be critically assessed in the same way.

  9. Sorry, forgot to define [1]
    Able to construct, test and document a program that achieves a defined analytical task in a 'useful' language without requiring constant supervision/correction.

  10. I am posting this on behalf of Cynthia Gibas at UNC Charlotte, who is having trouble logging in:

    I think the topics listed are pretty uncontroversial, but it's a big step from a list like that to building a curriculum and teaching skills effectively.

    I find it interesting that so few of the survey respondents expressed that it was desirable to teach students to use a core of existing bioinformatics tools. It's great to focus on programming and algorithm design for advanced students that will be developing new methods, but for practical purposes our graduates frequently need to understand, evaluate, and apply existing methods in their work.

    We teach at both graduate and undergraduate level and have just revised the curriculum for our Professional Science Masters students. Because they have to get what they need in 2 years and 40 credit hours, and part of that needs to be professional preparation (PLUS courses in PSM jargon), we are somewhat limited in what we can consider "core". The six courses that all students are required to take are: Programming, Statistics, Databases, Sequence Analysis, Genomics, and a Biophysics and Biochemistry intensive course, which align pretty closely with the proposed categories but don't cover them completely. I think it's key that all of these courses are designed by us, taught by us, and the emphasis is on teaching with examples from current, relevant research on genome-scale problems in the life sciences, rather than, for instance, sending our students over to CS to take a generic database or programming course that's focused on business and financial case studies.

    Once they've done the core they have three electives remaining to specialize a bit -- they can take more structural bio and modeling, they can get more heavily into lab genomics, or they can emphasize advanced programming methods. But ideally they all have core competence in genome-scale analysis and can understand and handle genomics data from all perspectives -- programming challenges, data management, sequence processing, statistical inference, the physical-molecular perspective, and the genomic bio perspective.

    We updated and integrated the core in response to hearing back from graduates and from employers that the most beneficial thing we were doing for students was making them struggle through large-scale data analysis problems in the context of their coursework. We're ramping that kind of content up even further -- it helps the PSM students be prepared for internships and jobs and it gets the Ph.D. students (who share some of the same courses) up to speed for research.

    1. Cynthia,

      Including a practical component in your curriculum sounds like a great idea. When hiring into a Bioinformatics Core Facility, it is very hard to find new Masters or Ph.D. grads who know how to use a wide range of standard bioinformatics tools to analyze new experimental data.

  11. I think that this is a good start. I particularly agree with Larry's comment about a guiding philosophy and with Oliver's comments about structuring the core curriculum and adding a third column for bioinformatics proper.

    I would add that an appropriate curriculum depends on the student's focus. Somebody who is aimed at developing bioinformatics tools needs common language with biologists but more background in programming. Somebody else who is working primarily at the bench needs fewer programming and mathematical skills. Somebody working in a bioinformatics core facility may need more math.

    In Israel, for example, the universities do not give undergraduate degrees in bioinformatics, at the insistence of the Council for Higher Education (full disclosure: I was on the CHE committee that decided this). Instead, they give degrees in, e.g., Computer Science with a focus on Bioinformatics or Life Sciences with a focus on Bioinformatics. Specialization on pure bioinformatics is left for later degrees, and those also can focus more on one side or the other.


  12. Hershel -

    You provide some valuable perspectives.

    At Ohio University, we have followed a path that is similar to the one taken in Israel. Our reasoning was that (1) it was easier to put a training process in place by leveraging existing courses and degree programs and (2) it provides students with broad undergraduate training that maximizes career opportunities. We are currently evaluating our offerings to determine whether to add specific degree programs in bioinformatics and/or computational biology; considerations include depth, marketing, and formal recognition of skills acquired.

    What was the rationale of the CHE for its policy?


  13. Thanks for putting together this document, and for getting in touch with us for feedback on this. It's obvious, I know, but it's clear that getting in touch with us and eliciting feedback is a key and important step in the process of publicizing and refining these efforts.

    While reading the document, and thinking about the curriculum, one of my strongest feelings was that I know basically nothing about best practices in curriculum development. I'm aware that there's a literature on this e.g. Designing and Assessing Courses and Curricula: A Practical Guide (Diamond), but it's one I've never engaged with (as I've not ever had to define a curriculum).

    Perhaps it would be interesting, at the ISCB curriculum development BOF (unfortunately for me, I won't be at Long Beach this year, so I can't join this BOF), one of the members of the committees/subcommittees working on this could give a summary of this literature?

    And I think it would also be interesting to consider contacting directly members of the learning research community to consult with them about best practices on this; it could even be worth spending money bringing in a consultant/expert from that field to help us with this.

    In my ignorance, I began asking myself: should all curricula be the same? Should we be trying to make them the same? No, I think that diversity of programs is desirable, as this allows potential students to choose ones that fit better to their aims and experience. But making all curricula the same isn't the aim here. Rather, the aim is to focus on core topics that should at least be addressed/taught in all programs i.e. program diversity comes in terms of how, and in how much depth, the different core topics are addressed, and in which additional non-core topics are included in a given curriculum.

    Perhaps an additional way of thinking about the problem would be to consider the tasks we would hope **all** bioinformaticians to be able to successfully address at the end of their studies. A comparison of this set of tasks, and the knowledge/understanding needed to address them, with an analysis of the state of knowledge/understanding of "typical" potential bioinformatics students, could provide an interesting set of knowledge and skills that could provide a different perspective on what could/should make up the curriculum.

    However, this highlights the fact that (particularly if we're attempting to consider a basis for a world-wide curriculum) the diversity of the understanding and knowledge of potential bioinformatics students, before they enter a bioinformatics education, is likely to be extreme (based on my experience of teaching these topics on different continents), so perhaps to be able to put together a solid "core curriculum" we need to incorporate research into the diversity of background knowledge and skills of students who apply for these programs?

    This would need, I feel, to be done carefully i.e. to sample such students geographically widely to avoid excluding large numbers of potential students from consideration of a recommended curriculum, as this could introduce an unpleasant cultural and geographic bias to this discussion.

    I hope this is of some use for you, even though I haven't directly addressed the points you were particularly interested in getting feedback on.

  14. Aidan -

    Thank you for the thoughtful comments. You make some good points and you have identified some of the primary goals and challenges of this international endeavor. Your points re. consulting the best practices of curriculum design are well taken. I will be sure to include this in the BOF discussion at ISMB. Sorry that you won't be able to join us in Long Beach, but I look forward to more inputs from you as we move forward.


  15. Dear All,
    We have developed a curriculum for a cluster of master programs that includes bioinformatics and systems biology. It is an intensive curriculum of 18 courses with up to 70 hours/semester.
    The list of courses is at:
    For each course we have developed a comprehensive curriculum (available on demand and soon to be posted online).
    I would welcome your comments,
    George Popescu

    1. George - thank you for sharing this! It is a helpful reference for others who are developing / maintaining programs. Looking forward to seeing more details as they become available.

  16. Hi Lonnie,

    Our primary motivation was similar to your #2. We felt that students at the bachelor's level should be grounded in a discipline, and that a focus on bioinformatics could wait until an advanced degree. Part of the reason for this was to ensure reasonable job opportunities for graduates, and this turned out to be a good choice. We compared the situation to that of computer science in the 1960s -- it has since developed into a discipline of its own, but at the time, students studied it as part of another discipline.


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  27. There is a related effort going on at the American Medical Informatics Association ( ). There is a document being circulated by Ted Shortliffe called "Biomedical Informatics Core Competencies" which I think dovetails nicely with much of the work you have done. It would be really good for the field if their work and yours were coordinated as much as possible. Jogjadb