Feitosa, L. G. P. | bioRxiv Science

bioRxiv2024

Optimizing the detection of biological signals through a semi-automated feature selection tool

Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches. Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled to mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package, RegFilter. RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biological relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the users interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization of biological signals, previously absent in the DDA mode. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

bioinformatics↗

bioRxiv2022

Improving annotation propagation on molecular networks through random walks: Introducing ChemWalker

Annotation of the mass signals is still the biggest bottleneck for the untargeted mass spectrometry analysis of complex mixtures. Molecular networks are being increasingly adopted by the mass spectrometry community as a tool to annotate large scale experiments. We have previously shown that the process of propagating annotations from spectral library matches on molecular networks can be automated using Network Annotation Propagation (NAP). One of the limitations of NAP is that the information for the spectral matches is only propagated locally, to the first neighbor of a spectral match. Here we show that annotation propagation can be expanded to nodes not directly connected to spectral matches using random walks on graphs, introducing the ChemWalker python library. Similarly to NAP, ChemWalker relies on combinatorial in silico fragmentation results, performed by MetFrag, searching biologically relevant databases. Departing from the combination of a spectral network and the structural similarity among candidate structures, we have used MetFusion Scoring function to create a weight function, producing a weighted graph. This graph was subsequently used by the random walk to calculate the probability of walking through a set of candidates, departing from seed nodes (represented by spectral library matches). This approach allowed the information propagation to nodes not directly connected to the spectral library match. Compared to NAP, ChemWalker has a series of improvements, on running time, scalability and maintainability and is available as a stand alone python package. ChemWalker is freely available at https://github.com/computational-chemical-biology/ChemWalker.

bioinformatics↗