Identifying the Unknowns: Fragmentation Trees and Molecular Fingerprints

• Applicant: Professor Dr. Sebastian Böcker
• Subject Area: Bioinformatics and Theoretical Biology
• Term: since 2016
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 324792648

Rapid identification of small compounds from small amounts of substance is of interest in many areas of biology and medicine such as metabolomics, natural products research, biomarker discovery, environmental research and diagnostics. Today, mass spectrometry (MS) is a key technology for the identification of small molecules. Over the last years, the amount and complexity of MS data has been growing rapidly. Computational analysis of such data is presumed to be one of the major technological hurdles in metabolomics and small molecule research today.About a decade ago, my group proposed fragmentation trees as a model for the fragmentation reactions in the measurement, and developed SIRIUS to compute such fragmentation trees. About five years ago, we developed CSI:FingerID for searching in molecular structure databases using tandem MS data, integrating fragmentation trees to boost search performance. As part of the previous funding period, we developed CANOPUS of the comprehensive assignment of compound classes without the need for structural elucidation, and ZODIAC for molecular formula assignments in complete datasets. We released version 4 of SIRIUS, which was named "method to watch" by Nature Methods, and demonstrated the power of our methods in biological case studies.In the second funding period, we will perform four diverse biological case studies, to demonstrate the power and versatility of our computational tools; we will continue our integration of LC-MS/MS processing into SIRIUS; we will develop Epimetheus for the validation of CSI:FingerID search results; we will perform in-depth evaluations of SIRIUS and CSI:FingerID; we will complete our work on separating chimeric spectra from the previous funding period; we will make ZODIAC faster and better; and finally, we will develop a method for network-based correction of molecular fingerprints, to further boost CSI:FingerID's performance.

• DFG Programme: Research Grants