Corna
Corna performs natural abundance correction (NAC) on metabolite intensity outputs from mass spectrometry to enable accurate quantification in stable isotope-based metabolomics.
Key Features:
- Natural Abundance Correction (NAC): Applies NAC to metabolite intensity outputs derived from mass spectrometry experiments, including stable isotope tracer studies.
- Multiple Tracer Support: Supports experiments with multiple tracer elements and includes correction capability for two tracer elements.
- Algorithm Validation: Implements algorithms that have been validated against published tools where applicable.
- Integration with Peak Integration Software: Integrates with an open-source peak integration software to process MS outputs and reduce overall processing time for stable isotope experiments.
- Resolution and MS Mode Support: Accommodates different instrument resolutions and tandem MS setups.
Scientific Applications:
- Metabolite quantification: Enables more accurate quantification of metabolites from mass spectrometry data in stable isotope experiments.
- Metabolite identification: Improves correction of isotopic patterns to support metabolite identification from MS data.
- Metabolic pathway discovery: Supports discovery of metabolic pathways by providing corrected isotopologue distributions for downstream analysis.
- Intracellular flux measurement: Facilitates measurement of intracellular fluxes using stable isotope labeling and mass spectrometry.
Methodology:
Applies validated computational algorithms for natural abundance correction to MS-derived metabolite intensities, supporting multiple tracer elements (including two tracers), different instrument resolutions, tandem MS, and integration with open-source peak integration software.
Topics
Details
- Tool Type:
- library
- Programming Languages:
- Python
- Added:
- 1/18/2021
- Last Updated:
- 2/17/2021
Operations
Publications
Raaisa R, Lathwal S, Chubukov V, Kibbey RG, Jha AK. >Corna - An Open Source Python Tool For Natural Abundance Correction In Isotope Tracer Experiments. Unknown Journal. 2020. doi:10.1101/2020.09.19.304741.