Glycans are molecules made from simple sugars that form complex tree structures. Glycans constitute one of the most important protein modifications, and identification of glycans remains a pressing problem in biology. Unfortunately, the structure of glycans is hard to predict from the genome sequence of an organism. We consider the problem of deriving the topology of a glycan solely from tandem mass spectrometry data. We want to generate glycan tree candidates that sufficiently match the sample mass spectrum. Unfortunately, the resulting problem is known to be computationally hard. We present an efficient exact algorithm for this problem based on fixedparameter algorithmics, that can process a spectrum in a matter of seconds. We also report some preliminary results of our method on experimental data. We show that our approach is fast enough in applications, and that we can reach very good de novo identification results. Finally, we show how to compute the number of glycan topologie...