6.047 | Fall 2015 | Undergraduate

Computational Biology




The final project is divided into several milestones due over the course of the semester. The first two milestones will get you thinking about problems you are interested in and classmates you may want to collaborate with. The first milestone is included in the problem sets; however, the subsequent milestones are separate assignments. This overview lists some resources for finding project topics and outlines the criteria that will be used to evaluate your project.


Project Profile

Describe your previous research, areas of interest in computational biology, type of project that best fits your interests. Post in a profile that lets your classmates know you and find potential partners.

Lecture 6

Literature Search and Pre-Proposal

Identify previous project proposals, recent papers, and potential partners that match your areas of interest. List initial project ideas and partners.

Lecture 8

Formal Proposal

Form teams of two, specify project goals, division of work, milestones, datasets, challenges. Prepare slide presentation for the class and the mentors.

Recitation 6

Peer Review

Evaluate / discuss three peer proposals, NIH review format.

Recitation 7

Review Response

Address peer evaluations, revise aims, scope, list of final deliverables / goals.

Lecture 17

Midterm Progress Report

Continue making substantial progress on proposed milestones. Write outline of final report.

Quiz date

Final Report

Complete your milestones, finalize results, figures, write-up in conference publication format. As part of report, comment on your overall project experience.

Recitation 11

Project Presentations

Conference format slide presentation

Final presentation dates

Proposal Guidelines

Write an National Institutes of Health (NIH) research proposal for your project describing your specific aims, research strategy, and resources. Follow the latest guidelines (available at the URLs below), making sure to adhere to the page limits.

Restructured Application Forms and Instructions for Submissions for Funding

Details of Application Changes for Research Grants and Cooperative Agreements (PDF)

In the resources section, be sure to discuss what datasets you are going to use (and how you are going to gain access to them if they are not public), what computational resources you will use (especially for computationally intensive projects), and who you plan to ask for advice (mentors from the the instructor’s lab, other faculty, etc.). Also be sure to identify relevant course lectures to help us (and your peers) evaluate the relevance of your project.

Groups must include a collaboration plan specifying the roles of individual investigators and how they will coordinate their activities.

Your proposal must also include milestones projecting when you will complete each of your specific aims (including what will be done by the mid-course report), how you will measure progress, and the expected results for the final report.

Selecting a Final Project

The first part of any research project is coming up with a good, innovative, concrete, and feasible idea. There is no single recipe for getting a good idea for a project, and our best ideas frequently come in unexpected ways. While you are brainstorming, there are several resources available: During lectures, we will discuss several research directions that can be pursued as final projects. Going through your notes can give you more ideas. The second half of the semester will be guest lectures on current research. Guest lecture notes from last year can give you more details on most of these and can help you build your own research projects. The problem sets will provide possible starting points for projects that extend the algorithms and programs you have already written in new research directions. And of course, browsing recent publications in Nature, Science, PLoS Biology, Genome Research, Nucleic Acids Research, PNAS, PLoS Computational Biology, the Journal of Computational Biology, PubMed, and Google Scholar is a great way to get ideas of recent research ideas, datasets, and results that you can expand upon for your project.

Peer Review Guidelines

Peer reviews are designed to provide two benefits: first, feedback from others can improve the innovation or feasibility of your project. Second, thinking critically about other proposals exposes you to new areas of computational biology and enhances your research and project-planning ability.

Each project proposal will be anonymously reviewed by multiple reviewers who will score on both NIH grant criteria and the course project criteria. We expect reviewers to score proposals impartially even if they recognize proposals based on the topic or background of the investigators. Review panels will discuss the proposals they have received, after which review panels will collaboratively write reviews. We will assign primary and secondary reviewers based on background, but each student will be a primary reviewer at most once.

_Review Panels_

Panel discussions will be led by the primary reviewer assigned, who will summarize the proposal and its strengths and weaknesses. All reviewers will share their comments and scores and explain their rationales. Although we anticipate reviewers changing their scores after the discussion, reviewers must explicitly say if the score they felt was appropriate is outside the range of scores shared by the panel and justify it.

_Written Reviews_

In a written review, give a score (1–5) for each of the five NIH core review criteria (reproduced below) and each of the five course project criteria (listed below). Justify each score by identifying the strengths and weaknesses of the proposal in that category and give constructive comments to improve the proposal. Note that “1” should stand for completely unsatisfactory, “3” should stand for acceptable, and “5” should stand for exemplary.

Submit a single zip file containing one PDF document for each proposal you reviewed as a primary reviewer named proposal#review.pdf (for example, proposal1review.pdf). Do not include your name in your reviews.

NIH Core Review Criteria

  • Significance: Does the project address an important problem or a critical barrier to progress in the field? If the aims of the project are achieved, how will scientific knowledge, technical capability, and / or clinical practice be improved? How will successful completion of the aims change the concepts, methods, technologies, treatments, services, or preventative interventions that drive this field?
  • Investigator(s): Are the PD / PIs, collaborators, and other researchers well suited to the project? If Early Stage Investigators or New Investigators, do they have appropriate experience and training? If established, have they demonstrated an ongoing record of accomplishments that have advanced their field(s)? If the project is collaborative or multi-PD / PI, do the investigators have complementary and integrated expertise; are their leadership approach, governance and organizational structure appropriate for the project?
  • Innovation: Does the application challenge and seek to shift current research or clinical practice paradigms by utilizing novel theoretical concepts, approaches or methodologies, instrumentation, or interventions? Are the concepts, approaches or methodologies, instrumentation, or interventions novel to one field of research or novel in a broad sense? Is a refinement, improvement, or new application of theoretical concepts, approaches or methodologies, instrumentation, or interventions proposed?
  • Approach: Are the overall strategy, methodology, and analyses well-reasoned and appropriate to accomplish the specific aims of the project? Are potential problems, alternative strategies, and benchmarks for success presented? If the project is in the early stages of development, will the strategy establish feasibility and will particularly risky aspects be managed? If the project involves clinical research, are the plans for
  1. protection of human subjects from research risks, and
  2. inclusion of minorities and members of both sexes / genders, as well as the inclusion of children, justified in terms of the scientific goals and research strategy proposed?
  • Environment: Will the scientific environment in which the work will be done contribute to the probability of success? Are the institutional support, equipment and other physical resources available to the investigators adequate for the project proposed? Will the project benefit from unique features of the scientific environment, subject populations, or collaborative arrangements?

Grading Criteria

  • Originality: How original is the idea? Note that we don’t expect every project to solve a previously unsolved problem. But we do expect projects to introduce some new computational idea to whatever problem they tackle (as illustrated in the lectures).
  • Challenge: How challenging was the project? Although we don’t expect every project to develop a brand-new algorithm, we do expect projects to do more than just apply off-the-shelf bioinformatics tools.
  • Relevance: Is the problem relevant to the course? Are you using ideas discussed in the course? Is this something that we could have used as an example in one of the lectures? Or is this only a vaguely justifiable tangential connection of something you were working on already?
  • Achievement: What did you actually accomplish in your project? What is your contribution to the field? Note that what you accomplished will be weighed against what you proposed to do and how challenging it was going to be, so be sure to pick a small enough, concrete problem which is still interesting.
  • Presentation: Did you effectively convey your research problem and your key ideas? In your written report, remember that you are writing for an audience of computational biologists. Be sure to make explicit what your contributions were not just in terms of figures and tables, but more importantly in terms of the ideas (biological or computational) which generated them. In your oral presentation, remember you are speaking to an audience of your peers and that you have stringent time limits. You must get your key ideas across, so do not feel obligated to show all of your results. Instead, prioritize the ones which demonstrate the intuition behind your approach.
Learning Resource Types
Problem Sets
Online Textbook