Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems

Le Shu; Yuqi Zhao; Zeyneb Kurt; Sean Geoffrey Byars; Taru Tukiainen; Johannes Kettunen; Samuli Ripatti; Bin Zhang; Michael Inouye; Ville-Petteri Makinen; Xia Yang

doi:10.1101/036012

Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems

Le Shu, Yuqi Zhao, Zeyneb Kurt, Sean Geoffrey Byars, Taru Tukiainen, Johannes Kettunen, Samuli Ripatti, Bin Zhang, Michael Inouye, Ville-Petteri Makinen, Xia Yang

Research output: Other contribution › peer-review

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Abstract

Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

Original language	English
Type	Biorxiv
Media of output	Pre-print
DOIs	https://doi.org/10.1101/036012
Publication status	Published - 7 Jan 2016
Externally published	Yes

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036012v1.fullSubmitted manuscript, 1.04 MBLicence: CC BY-NC

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title = "Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems",

abstract = "Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.",

author = "Le Shu and Yuqi Zhao and Zeyneb Kurt and Byars, {Sean Geoffrey} and Taru Tukiainen and Johannes Kettunen and Samuli Ripatti and Bin Zhang and Michael Inouye and Ville-Petteri Makinen and Xia Yang",

note = "Now published in BMC Genomics doi: 10.1186/s12864-016-3198-9",

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doi = "10.1101/036012",

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AU - Shu, Le

AU - Zhao, Yuqi

AU - Kurt, Zeyneb

AU - Byars, Sean Geoffrey

AU - Tukiainen, Taru

AU - Kettunen, Johannes

AU - Ripatti, Samuli

AU - Zhang, Bin

AU - Inouye, Michael

AU - Makinen, Ville-Petteri

AU - Yang, Xia

N1 - Now published in BMC Genomics doi: 10.1186/s12864-016-3198-9

PY - 2016/1/7

Y1 - 2016/1/7

N2 - Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

AB - Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

U2 - 10.1101/036012

DO - 10.1101/036012

M3 - Other contribution

ER -

Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems

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