Česká společnost pro hmotnostní spektrometrii

(Česká konference hmotnostní spektrometrie 2019 - PL-03)
Mass Spectrometry Data, Analyses and Tools in Context

Magnus Palmblad ^1 *

1. Leiden University Medical Center

Abstrakt

Mass spectrometry has been used across many omics domains, primarily proteomics and metabolomics with their specializations, such as glycomics or lipidomics, but also to targeted SNP analyses and epigenetics. Despite being a Jack of many trades, understanding complex biological problem requires additional information and context not provided by mass spectrometry measurements alone. Here I will give a brief and personal history of integrating mass spectrometry data with genome-wide SNP, gene expression and small-molecule data, across time and spatial dimensions [1].

Much information is also held in databases, repositories and the scientific literature. Though considerable text mining effort have been spent on building gene-disease and protein-protein interaction networks, we can also probe the literature for mass spectrometry measurements. This information in turn tells us something about bias in data, or the general applicability of separation, ionization, fragmentation and detection methods. In this talk, I will show how to combine text mining and machine learning to visualize patterns in the literature and databases in ways familiar to practitioners of mass spectrometry [2].

Just as one type of measurement cannot answer all questions, no one tool can execute all data analysis tasks. Complex questions require combination of software tools. For reproducible and scalable analyses, these tools need to be combined in automated, documented workflows, guiding the analysis from raw data all the way to final statistical analysis and visualization. I will report on recent efforts [3] to simplify finding tools fit-for-purpose and automatically assemble them into workflows, both with emphasis on mass spectrometry data analysis.

* Korespondující autor: n.m.palmblad@lumc.nl

Literatura

1. Travin D. et al.: J. Proteome Res. 17(1), 739-744 (2018).
2. Palmblad M.: Anal. Chem. in press (2019).
3. Palmblad M. et al.: Bioinformatics 35(4), 656-664 (2019).