Abstract
Mass spectrometry with gas chromatography is one of the emerging high-resolution instruments. This technology can be used to discover the composition of the chemical compounds. It is used for targeted detection or for untargeted screening. As such, this technology is providing a large volume of measurements. These data are also in high precision. There are emerging need to efficiently process these data and be able to identify and extract all possible information. There are numerous tools to do that, using common steps. One of the steps is peak picking, usually carried by signal processing methods. We are proposing a two-dimensional approach to identify the peaks and extract their features for further analysis. This method can be easily adaptable to fit the current pipelines and to perform the computation efficiently. We are proposing a method to preprocess the data onto a grid of required precision. After that, we are applying an image processing method watershed, to extract the region of interest and the peaks.
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Acknowledgment
Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures”.
This work was carried out with the support of the RECETOX (LM2018121) research infrastructures funded by the Ministry of Education, Youth and Sports of the Czech Republic.
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Bartoň, V., Nykrýnová, M., Škutková, H. (2020). Watershed Segmentation for Peak Picking in Mass Spectrometry Data. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_44
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