Computational Lipidomics and Metabolomics

The objective of CompLiMet is to provide experimental and computational biologists methods for data processing, analyte annotation, statistical analysis, machine learning analysis, and network investigation. These tools were developed primarily for lipidomic and metabolomic data.

Our Tools

Lipidomic Annotation

Bayesian Annotations For Targeted Lipidomics

A Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight user-specified peak features related to retention time, intensity, and peak shape5.

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Multivariate Data Optimization and Analysis Solutions

METAbolomics data Balancing with Over-sampling Algorithms

A software solution for handling sample imbalance, primarily for metabolomics and lipidomics datasets.

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Imputation for Lipidomics and Metabolomics

A user-friendly solution for dataset appropriate imputation of missing data.

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Correlation Network Derivation

Signed Distance Correlation

An application for calculation of distance correlation in omics data, measuring linear and non-linear dependencies between variables. SiDCo further provides a novel “signed distance correlation” that includes overall directionality as well as magnitude of the correlation.

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Lipid Drawing Tools


A tool that searches a computationally generated database of glycerophospholipids by m/z with option to draw all possible lipid structures and/or download static visualizations.

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