Author

Justin G. Chitpin

University of OttawaORCID 0000-0002-8692-9616Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Research Focus:differential expression· 3 Assay:metabolomics· 3 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:ChIP-seq· 1

Frequent Co-Authors

Theodore J. PerkinsiD· 4 shared Angela M. CrawleyiD· 1 shared Anuradha SurendraiD· 1 shared Aseel AwdehiD· 1 shared Graeme P. Taylor· 1 shared Hongbin XuiD· 1 shared Irina AlecuiD· 1 shared Julianna J. TomlinsoniD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

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10 top collaborators · 37 mutual co-author links

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4 shared papersBioinformatics

Theodore J. PerkinsORCID

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4 papers

Recent·Most cited

Atomic elementary flux modes explain the steady state flow of metabolites in large-scale flux networks

Justin G. Chitpin, Theodore J. Perkins

The paper introduces atomic elementary flux modes (AEFMs) to efficiently analyze metabolic fluxes in large networks, showing they outperform traditional EFMs and reveal new insights into carbon and nitrogen metabolism in HepG2 cells.

Cell Reports Methods · May 22, 2026Peer-reviewedBioinformatics

A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks

Justin G. Chitpin, Theodore J. Perkins

The paper introduces a Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks, demonstrating its application in analyzing lipid metabolism in Alzheimer's disease.

Journal of Theoretical Biology · Oct 5, 2023Peer-reviewedBioinformatics

BATL: Bayesian annotations for targeted lipidomics

Justin G. Chitpin, Anuradha Surendra, Thao Nguyen-Tran, Graeme P. Taylor, Hongbin Xu, Irina Alecu, Roberto Ortega, Julianna J. Tomlinson, Angela M. Crawley, Michaeline McGuinty, Michael G. Schlossmacher, Rachel J. Saunders-Pullman, Miroslava Čuperlović‐Culf, Steffany A. L. Bennett, Theodore J. Perkins

BATL is a Bayesian classifier for targeted lipidomics that annotates LC-ESI-MS/MS peaks with high accuracy using eight features, achieving over 95% correct identification in SRM datasets.

Bioinformatics · Dec 21, 2021Peer-reviewedBioinformatics

RECAP reveals the true statistical significance of ChIP-seq peak calls

Justin G. Chitpin, Aseel Awdeh, Theodore J. Perkins

RECAP corrects statistical biases in ChIP-seq peak calling by recalibrating p-values to provide more accurate false discovery rate estimates.

Bioinformatics · Feb 27, 2019Peer-reviewedBioinformatics