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Thus, we also present here a new R-software package (PAPi) which implements the PAPi algorithm and facilitates its usage to quickly compare metabolic pathways activities between different experimental conditions. However, PAPi is time consuming to perform manually.
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PAPi was able to support the biological interpretations of the previously published observations and, in addition, generated new hypotheses in a straightforward manner. The applicability and potential of PAPi was demonstrated using a previously published data from the yeast Saccharomyces cerevisiae. Here, we present a new algorithm Pathway Activity Profiling (PAPi) with which we are able to compare metabolic pathway activities from metabolite profiles. Thus, this kind of analysis still depends on extremely laborious and time-consuming processes. Despite recent developments in bioinformatics, which have improved the quality of metabolomics data, there is still no straightforward method capable of correlating metabolite level to the activity of different metabolic pathways operating within the cells. However, metabolomics datasets are considered highly complex when used to relate metabolite levels to metabolic pathway activity. It has evolved very quickly during the last decade. Metabolomics is one of the most recent omics-technologies and uses robust analytical techniques to screen low molecular mass metabolites in biological samples.