Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism.

Annamalai Muthiah,Jae K Lee,Susanna R Keller

doi:10.1155/2017/8514071

Annamalai Muthiah, Jae K Lee + Show 1 more

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https://doi.org/10.1155/2017/8514071

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Abstract

Different computational approaches have been examined and compared for inferring network relationships from time-series genomic data on human disease mechanisms under the recent Dialogue on Reverse Engineering Assessment and Methods (DREAM) challenge. Many of these approaches infer all possible relationships among all candidate genes, often resulting in extremely crowded candidate network relationships with many more False Positives than True Positives. To overcome this limitation, we introduce a novel approach, Module Anchored Network Inference (MANI), that constructs networks by analyzing sequentially small adjacent building blocks (modules). Using MANI, we inferred a 7-gene adipogenesis network based on time-series gene expression data during adipocyte differentiation. MANI was also applied to infer two 10-gene networks based on time-course perturbation datasets from DREAM3 and DREAM4 challenges. MANI well inferred and distinguished serial, parallel, and time-dependent gene interactions and network cascades in these applications showing a superior performance to other in silico network inference techniques for discovering and reconstructing gene network relationships.

Highlights

Many established algorithms and approaches are available for inferring gene regulatory networks from large time-course molecular data [1, 2]
Some of the widely used network inference approaches are ANOVerence (which detects gene relationships using nonlinear correlation coefficient derived from an analysis of variance (ANOVA) [4]), correlation, CLR (Context Likelihood of Relatedness, which estimates gene relationships using the concept of mutual information between genes [5]), GENIE3, Inferelator
We developed our Module Anchored Network Inference (MANI) technique, which identifies gene interactions and regulatory relationships within each local module and gradually expands the network by adding new network interactions from adjacent connected modules

Summary

Introduction

Many established algorithms and approaches are available for inferring gene regulatory networks from large time-course molecular data [1, 2]. International Journal of Genomics techniques for network inference: time-lagged CLR (tlCLR), an extension of CLR described above, and linear ODE model constrained by LASSO [7]), and TIGRESS (Trustful Inference of Gene Regulation using Stability Selection, a LASSObased regression approach for inferring gene regulations [8]) Most of these network inference approaches adopt a “global” approach to network inference and construct a network using all genes simultaneously. Similar to the dynamic algorithm in sequence alignment, a localized approach anchoring network inference around building blocks (modules) [1] or subunits of a large network can dramatically enhance computational network reconstruction Based on this principle, we developed our Module Anchored Network Inference (MANI) technique, which identifies gene interactions and regulatory relationships within each local module and gradually expands the network by adding new network interactions from adjacent connected modules. This systematic and local approach to network inference constructs a less complex network and identifies dynamic relationships between network genes

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