Projects should be done in small groups (of 2 - 3 members). Groups combining people from different backgrounds are particularly encouraged. Feel free to use the class email list (firstname.lastname@example.org) to brainstorm project ideas or to find partners. You have two choices for the project:
I'd suggest that each group send me a paragraph or drop by to tell me who's in the group, describe your topic, the initial papers, and the test data (if applicable). Maybe I can give you some pointers.
Before the beginning of the final week, each group will need to hand in a paper (approximately 5-10 pages) describing the project. Your paper must also clearly describe the contribution of each personnel in the group.
Each group will also need to give a 10 minute presentation, scheduled on the final exam day: 12/17. You can choose to turn in your project and do the presenation before the final week if you wish.
Timeine (all submissions via blackboard):
You are encouraged to choose your own topics and impress me with your creative project ideas. Here are a few of mine to get you started.
To search for papers
To browse for papers from bioinformatics-related conferences
To browse for papers from bioinformatics-related journals
Advices on how to read and present a paper (Adapted from this web page)
When you present a paper in this course (or elsewhere), your goal is to get your audience to appreciate the contribution that the paper makes to scientific knowledge. Generally, you need to explain the following three things about the paper to do that. It often makes sense to present each point in order, but it is more important to focus on the essence of the contribution than it is to follow any particular format.
Try to identify where the main contribution of the paper is. For example, some papers define interesting new problems, but apply relatively straightforward methods to addressing them. For a paper like that, focus on work on related problems, and how the new problem statement differs from them. Are there better approaches developed for related problems that can be applied to the new problems? Some papers present a new approach to a well studied problem. For those papers, carefully compare the new method to other approaches people have taken to the problem. Also, in that situation, the choice of the evaluation method (used to compare the new approach to existing methods) is an important place to focus.
Look for unstated assumptions made in the paper, and try to make them explicit. For example, does a paper on finding cis-regulatory elements from sequence and gene expression data assume that the elements are independent of each other? That the position of the element with respect to the start of transcription is unimportant? Reading alternative approaches to the same problem will make it easier for you to identify these assumptions.
After you have communicated these facts about the paper, you can discuss the aspects you thought were most important or interesting. Is this a method that belongs in your "bioinformatics toolkit"? Can it be applied to related problems straightforwardly, or is it highly specialized? Was there something particularly impressive about the method, the evaluation, the translation of the problem into computational terms, etc.?
In general, bioinformatics papers have an "engineering" flavor that fits well into this problem / method / results paradigm. However, some papers have more of a "basic science" flavor, where a particular claim is being made, and evidence is presented to support that claim. Providing evidence for a claim is closely related to testing a particular hypothesis. If you feel that this better fits the paper you are presenting, then rather than using the problem / method / results paradigm, you can explain it in terms of claims and evidence.