Through shared resource facilities, service cores, and grant-funded collaboration, we provide a range of bioinformatics and systems biology services.We develop many of the tools that we use and can provide customized analyses, tailored to the specific experimental design, analytical technique, and scientific question.
We provide a range of interaction network analyses utilizing custom-built interaction networks using molecular interaction data derived from public repositories.
Description: We have built a comprehensive Protein-Protein Interaction (PPI) network for human by aggregating and integrating PPI and other interaction data from several publicly available resources. We are able to filter this custom network based on a variety of criteria including gene expression, mutational patterns, phenotype, disease, and functional annotation, enabling a wide range of interaction network analyses. Moreover, we can overlay a variety of annotations onto the network. We can use these analyses to generate functional hypotheses for genes with limited functional information and to derive a pathway/module for a group of related genes-products involved in a common process.
Omics Data Analysis
We have analyzed datasets derived from all major omics technologies including epigenomics, genomics, transcriptomics, proteomics, and especially metabolomics.
Differential Abundance Analysis
We perform differential abundance analyses on various types of omics datasets, especially transcriptomics and metabolomics datasets.
In transcriptomics, these analyses are referred to as differential expression analysis.
These analyses are quite popular, especially when combined with annotation enrichment analysis.
We can perform a variety of biomarker analyses, including (but not limited to):
Classification using machine learning methods, especially Random Forest.
Metabolomics Data Analyses
We have extensive expertise in analyzing both NMR and mass spectrometry metabolomics datasets.
Small Molecule Isotope Resolved Formula Enumerator (SMIRFE)
This is a unique isotopically-resolved metabolite assignment methodology that we are patenting (US patent application 15/642,143).
Description: We have developed and refined a novel algorithm SMIRFE that detects small biomolecules less than 2000 daltons in mass at a desired statistical confidence and determines their specific elemental molecular formula (EMF) using detected cliques of related isotopologue peaks with compatible isotope resolved molecular formulae (IMFs). The methodology works on both mass spectra derived from non-stable isotope tracing experiments, but especially on mass spectra from stable isotope tracing experiments. The current implementation efficiently searches a roughly 200 quintillion (2x1020) IMF space for each peak’s m/z, based on molecular masses <=2000 daltons, but larger IMF spaces are searchable.
Natural Abundance Correction
We have implemented a unique deisotoping method that corrects for isotopic labeling derived from natural abundance in multi-labeling experiments.