Liquid chromatography with electrospray ionization mass spectrometry (LC-ESI-MS) is an established analytical technique for biomedical analyses. The analytes' ionization with an atmospheric pressure ion source, such as ESI, can result in substantial ionic species diversity. This thesis focuses on the development, implementation, and benchmarking of bioinformatics tools to enhance metabolite identification and quantification and improve our understanding of the complexity and variability of ESI-MS data. These tools include a graph-based annotation software called mzAdan, which allowed us to perform large-scale ESI-MS data analysis and discuss the recurrent issues observed with modern LC-MS data processing tools, as well as a NoSQL-based online/local data management platform for differential mobility data and metadata, which improved data flow in research groups.