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

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.

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Personalized analysis of breast cancer using sample-specific networks

PeerJ ◽

10.7717/peerj.9161 ◽

2020 ◽

Vol 8 ◽

pp. e9161

Author(s):

Ke Zhu ◽

Cong Pian ◽

Qiong Xiang ◽

Xin Liu ◽

Yuanyuan Chen

Keyword(s):

Breast Cancer ◽

Expression Profiles ◽

Single Gene ◽

Gene Interaction ◽

Interaction Networks ◽

Cancer Prognosis ◽

Specific Gene ◽

Validation Data ◽

Data Set ◽

Gene Interaction Networks

Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.

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Meta-analysis of expression signatures of muscle atrophy: gene interaction networks in early and late stages

BMC Genomics ◽

10.1186/1471-2164-9-630 ◽

2008 ◽

Vol 9 (1) ◽

pp. 630 ◽

Cited By ~ 40

Author(s):

Enrica Calura ◽

Stefano Cagnin ◽

Anna Raffaello ◽

Paolo Laveder ◽

Gerolamo Lanfranchi ◽

...

Keyword(s):

Muscle Atrophy ◽

Meta Analysis ◽

Gene Interaction ◽

Interaction Networks ◽

Gene Interaction Networks ◽

Late Stages

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Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

10.1101/2021.12.08.471133 ◽

2021 ◽

Author(s):

Elisabetta Sciacca ◽

Anna E.A. Surace ◽

Salvatore Alaimo ◽

Alfredo Pulvirenti ◽

Felice Rivellese ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Rna Sequencing ◽

Gene Networks ◽

Prediction Models ◽

Gene Interaction ◽

Interaction Networks ◽

Gene Interactions ◽

Specific Gene ◽

Rna Seq ◽

Gene Interaction Networks

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

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