The idea of the project is to integrate retention times from liquid chromatography into the SIRIUS/CSI:FingerID identification pipeline. Literally hundreds of papers have been published on the topic of retention time prediction, but all of them fail to provide predictions that are transferable across chromatography conditions and compound classes; see Héberger’s review (Journal of Chromatography A, 2007) where he speaks rather frankly about the malpractices of publishing such RT-prediction methods. On the other hand, retention times can indeed be used to further boost CSI:FingerID’s identification performance. Also, transferable retention prediction is not impossible, as we have shown here. The trick is not to try to predict retention time (which is extremely dependent on instrument parameters etc) but rather retention order.
We are searching for a qualified and motivated PhD student who wants to accept this challenge. (S)he should be knowledgeable in machine learning and preferably also bioinformatics in general; biochemistry knowledge is clearly also a plus. We believe that this can be the next big thing to further push CSI:FingerID’s performance. Please contact Sebastian or Kathrin in case you are interested and qualified.