Related Experiment Video
Updated: Jun 8, 2026
05:01
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
Published on: July 1, 2020
Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity
Raphael B M Aggio1, Katya Ruggiero, Silas Granato Villas-Bôas
1School of Biological Sciences, The University of Auckland, 3A Symonds Street, Private Bag 92019, Auckland 1142, New Zealand.
Bioinformatics (Oxford, England)
|October 9, 2010
Summary
This study introduces Pathway Activity Profiling (PAPi), a new algorithm and R-software package that simplifies the comparison of metabolic pathway activities from metabolomics data. PAPi aids in interpreting biological observations and generating new hypotheses more efficiently.
Area of Science:
- Metabolomics
- Bioinformatics
- Systems Biology
Background:
- Metabolomics data analysis is complex for correlating metabolite levels with metabolic pathway activity.
- Existing bioinformatics tools lack straightforward methods for this correlation, making analysis laborious.
- Accurate pathway activity assessment is crucial for understanding cellular metabolism.
Purpose of the Study:
- To develop a novel algorithm, Pathway Activity Profiling (PAPi), for comparing metabolic pathway activities from metabolomics data.
- To create an R-software package for PAPi to facilitate rapid comparison of pathway activities across experimental conditions.
- To support biological interpretation and hypothesis generation from metabolomics datasets.
Main Methods:
- Development of the Pathway Activity Profiling (PAPi) algorithm.
- Implementation of PAPi into a user-friendly R-software package.
- Utilizing metabolite profiles (identified metabolites and abundances) as input.
- Calculation of pathway Activity Scores.
- Statistical analysis including principal components analysis (PCA) and ANOVA/t-tests.
- Generation of comparative graphs for pathway activity visualization.
Main Results:
- PAPi successfully compared metabolic pathway activities using yeast (Saccharomyces cerevisiae) data.
- The algorithm supported existing biological interpretations and generated novel hypotheses.
- The R-software package enables quick and efficient comparison of pathway activities between conditions.
- PAPi provides statistical analysis and graphical outputs for differential pathway activity.
Conclusions:
- PAPi offers a streamlined approach to analyze and compare metabolic pathway activities from metabolomics data.
- The PAPi R-software package enhances the accessibility and efficiency of pathway activity profiling.
- This tool facilitates deeper biological insights and hypothesis generation in metabolomics research.
Related Concept Videos
01:29Amino Acid Biosynthetic Pathways
Amino acid biosynthesis is essential for cell growth, protein synthesis, and metabolic regulation. Cells generate essential and non-essential amino acids from metabolic intermediates to sustain vital biological functions. These intermediates originate from key metabolic pathways: glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway. Important precursors include α-ketoglutarate, pyruvate, oxaloacetate, phosphoenolpyruvate, and erythrose-4-phosphate, which provide...
01:17Drug Metabolism: Phase I Reactions
A phase I reaction is a biochemical process that introduces a functionally reactive polar group to a substance. This transformation predominantly occurs in the liver, facilitated by the cytochrome P450 system of hemoproteins situated in the lipophilic endoplasmic reticulum of cells. The metabolite generated through this process can have varying polarities. If it is sufficiently polar, it can be easily excreted in the urine due to its water compatibility. However, if the metabolite is nonpolar,...