Computational Lipidomics and Metabolomics

Close association of lipidome and metabolome with phenotype, combined with an increasing availability of highly advanced analytical methods for their high throughput measurement offers major power for linking these molecular measures with biological systems behaviour in health and disease. Complexity of this data requires utilization of appropriate computational tools as it defies manual exploration. COMPLIMET.CA provides collection of solutions that were specifically created for metabolomics and lipidomics data and are here presented through user friendly Web applications. Currently included are solutions ranging from data preprocessing, data analysis and mining as well as simulations with new applications added regularly. The objective of COMPLIMET.CA is to provide both computational and experimental biologists with appropriate methods for gathering knowledge from lipidomics and metabolomics measurements and data. Our aim is to grow COMPLIMET.CA into a reliable and comprehensive framework for bioinformatics and computational biology in metabolomics and lipidomics.

Our Tools

Targeted Lipidomic Identification and Quantification

Bayesian Annotations For Targeted Lipidomics

A Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to user-specified peak features such as retention, intensity, and/or shape.

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Quantification For Lipids

A tool for normalizing datasets using specified internal standard barcodes as the reference.

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Machine Learning Tools

METAbolomics data Balancing with Over-sampling Algorithms

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

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Network Analysis

Signed Distance Correlation

An application for calculating pairwise distance correlation coefficients between all columns of a dataset.

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Distance Correlation and Network Thresholding

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