How to present a paper in BioI 7713

What is the problem the paper is trying to address?

You should both define the problem and explain its broader significance. In addressing this question, you want to consider things like: What is biological nature of the problem? Is it reconstructing evolutionary history, identifying genes relevant to the prognosis or treatment of a disease? Why is that important? What is the contribution of the paper to furthering our understanding of the biology? Then you may want to talk about the computational nature of the problem. How was the biological problem reformulated into a computational problem? Is that the contribution (it often is). Are there aspects of the computational problem that are particularly interesting? Is a previous (or obvious) computational formulation too slow or not accurate enough? If so, why what kind of improvement in the computational approach would be important, and why? Or is this a comparison of alternative approaches? If so, why were those approaches selected and not others? How are they to be compared?

What were the methods used in the paper?

Often, this is where you have to spend the most time in your presentation, since new methods are the essence of most bioinformatics publications. You want to carefully explain exactly what was done. It may require a very close reading of the paper to figure this out; often important facts are buried in seeming asides. When you are working on this part of your presentation, imagine you were trying to replicate the work. What would you need to know?

What were the results reported?

Ideally, it would be straightforward to compare the results presented with the problem statement, but it is not always that easy. Discuss the evaluation method(s) as well as the results. It is often interesting to consider how the authors chose to evaluate there contribution: was it fair? was it indicative of "real world" performance?

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.

For example, a paper might claim that there are conserved upstream cis regulatory elements between mouse and human that are not conserved in more distantly related species, and present evidence in the form of statistics about sequence elements. One clue that a paper is of this nature is the use of statistical p values in the results section. Recall that a p value is a measure of the probability of a hypothesis (claim) being true by chance, given some set of data. This clue is not a substitute for thinking about what the content of the paper is, however. There are other ways to support a claim (e.g. a proof, or even a set of compelling examples), so a statistical test is not required in a basic science paper. Also note that engineering-style papers might use some statistical test as part of a method....