Grant Details
Grant Number: |
5U01CA235487-03 Interpret this number |
Primary Investigator: |
Karnovsky, Alla |
Organization: |
University Of Michigan At Ann Arbor |
Project Title: |
Methods and Tools for Integrative Functional Enrichment Analysis of Metabolomics Data |
Fiscal Year: |
2020 |
Abstract
Abstract
Modern analytical methods allow simultaneous detection of hundreds of metabolites, generating
increasingly large and complex data sets. Analysis of metabolomics data is a multi-step process that
requires variety of bioinformatics and statistical tools. One of the biggest challenges in metabolomics is
how alterations in metabolite levels can be linked to specific biological processes that are disrupted
contributing to the development of disease, or reflecting the disease state. To address this challenge, we
propose to develop methods and build computational tools to help researchers interrogate their
metabolomics data and integrate them with other molecular phenotypes to build testable hypotheses and
derive biological knowledge that could help addressing this challenge.
Our team has extensive collaborative experience working together in the Phase I Common Fund-supported
Regional Comprehensive Metabolomics Resource Core (MRC2) and in building computational methods
and tools for the analysis of multi-dimensional `omics data. We propose to build on our past efforts to
develop a novel functional enrichment testing (FET) approach that will not be limited to compounds found
in canonical metabolic pathways and will include both known and unknown metabolites in the analysis. We
will leverage our previously developed methodology for building partial correlation networks that allows
identifying commonalities and differences in network structures derived from different experimental
conditions.
Exploring relationships between key metabolic changes and alterations in transcript, proteins and other
molecular components can provide additional levels of information and help build biological insights from
experimental data. The overarching goal of this proposal is to develop FET methods that would enable
analysis of multi-condition, multi-layer `omics data sets. To that end, we propose a network-based data
integration strategy that will help uncover relationships both within and between different molecular layers,
identify subnetworks, containing metabolites, transcripts etc., and test their significance. We anticipate that
the application of our methods will lead to better insights into molecular networks affected by many
complex diseases.
!
Publications
CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data.
Authors: Iyer G.
, Brandenburg M.
, Patsalis C.
, Michailidis G.
, Karnovsky A.
.
Source: Journal of visualized experiments : JoVE, 2023-11-10; (201), .
EPub date: 2023-11-10.
PMID: 38009735
Related Citations
Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts.
Authors: Goutman S.A.
, Guo K.
, Savelieff M.G.
, Patterson A.
, Sakowski S.A.
, Habra H.
, Karnovsky A.
, Hur J.
, Feldman E.L.
.
Source: Brain : a journal of neurology, 2022-12-19; 145(12), p. 4425-4439.
PMID: 35088843
Related Citations
A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions.
Authors: Samanta S.
, Khare K.
, Michailidis G.
.
Source: Statistics and computing, 2022 Jun; 32(3), .
EPub date: 2022-06-03.
PMID: 36713060
Related Citations
Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease.
Authors: Baek J.
, He C.
, Afshinnia F.
, Michailidis G.
, Pennathur S.
.
Source: Nature reviews. Nephrology, 2022 Jan; 18(1), p. 38-55.
EPub date: 2021-10-06.
PMID: 34616096
Related Citations
Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations.
Authors: Iyer G.R.
, Wigginton J.
, Duren W.
, LaBarre J.L.
, Brandenburg M.
, Burant C.
, Michailidis G.
, Karnovsky A.
.
Source: Metabolites, 2020-11-24; 10(12), .
EPub date: 2020-11-24.
PMID: 33255384
Related Citations
Pathway Analysis for Targeted and Untargeted Metabolomics.
Authors: Karnovsky A.
, Li S.
.
Source: Methods in molecular biology (Clifton, N.J.), 2020; 2104, p. 387-400.
PMID: 31953827
Related Citations
Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease.
Authors: Ma J.
, Karnovsky A.
, Afshinnia F.
, Wigginton J.
, Rader D.J.
, Natarajan L.
, Sharma K.
, Porter A.C.
, Rahman M.
, He J.
, et al.
.
Source: Bioinformatics (Oxford, England), 2019-09-15; 35(18), p. 3441-3452.
PMID: 30887029
Related Citations
Untargeted Metabolomics Differentiates l-Carnitine Treated Septic Shock 1-Year Survivors and Nonsurvivors.
Authors: Evans C.R.
, Karnovsky A.
, Puskarich M.A.
, Michailidis G.
, Jones A.E.
, Stringer K.A.
.
Source: Journal of proteome research, 2019-05-03; 18(5), p. 2004-2011.
EPub date: 2019-04-01.
PMID: 30895797
Related Citations