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Pathway enrichment analysis of -omics data

Jüri Reimand, Ruth Isserlin, Veronique Voisin, Mike Kucera, Christian Tannus-Lopes, Asha Rostamianfar, Lina Wadi, Mona Meyer, Jeff Wong, Changjiang Xu, Daniele Merico, Gary D. Bader
doi: https://doi.org/10.1101/232835
Jüri Reimand
1Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
2Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
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Ruth Isserlin
3The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
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Veronique Voisin
3The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
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Mike Kucera
3The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
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Christian Tannus-Lopes
3The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
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Asha Rostamianfar
3The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
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Lina Wadi
1Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
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Mona Meyer
1Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
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Jeff Wong
3The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
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Changjiang Xu
3The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
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Daniele Merico
4Deep Genomics Inc., Toronto, Ontario, Canada
5The Centre for Applied Genomics (TCAG), The Hospital for Sick Children, Toronto, Ontario, Canada
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Gary D. Bader
3The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
6Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
7Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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  • For correspondence: gary.bader@utoronto.ca
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ABSTRACT

Pathway enrichment analysis helps gain mechanistic insight into large gene lists typically resulting from genome scale (–omics) experiments. It identifies biological pathways that are enriched in the gene list more than expected by chance. We explain pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome sequencing experiments. The protocol comprises three major steps: define a gene list from genome scale data, determine statistically enriched pathways, and visualize and interpret the results. We focus on differentially expressed genes and mutated cancer genes, however the described principles can be applied to diverse –omics data. The protocol is designed for biologists with no prior bioinformatics training and uses freely available software including g:Profiler, GSEA, Cytoscape and Enrichment Map.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 12, 2017.
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Pathway enrichment analysis of -omics data
Jüri Reimand, Ruth Isserlin, Veronique Voisin, Mike Kucera, Christian Tannus-Lopes, Asha Rostamianfar, Lina Wadi, Mona Meyer, Jeff Wong, Changjiang Xu, Daniele Merico, Gary D. Bader
bioRxiv 232835; doi: https://doi.org/10.1101/232835
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Pathway enrichment analysis of -omics data
Jüri Reimand, Ruth Isserlin, Veronique Voisin, Mike Kucera, Christian Tannus-Lopes, Asha Rostamianfar, Lina Wadi, Mona Meyer, Jeff Wong, Changjiang Xu, Daniele Merico, Gary D. Bader
bioRxiv 232835; doi: https://doi.org/10.1101/232835

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