Dataset Information

A multi-layer inference approach to reconstruct condition-specific genes and their regulation.

ABSTRACT: An important topic in systems biology is the reverse engineering of regulatory mechanisms through reconstruction of context-dependent gene networks. A major challenge is to identify the genes and the regulations specific to a condition or phenotype, given that regulatory processes are highly connected such that a specific response is typically accompanied by numerous collateral effects. In this study, we design a multi-layer approach that is able to reconstruct condition-specific genes and their regulation through an integrative analysis of large-scale information of gene expression, protein interaction and transcriptional regulation (transcription factor-target gene relationships). We establish the accuracy of our methodology against synthetic datasets, as well as a yeast dataset. We then extend the framework to the application of higher eukaryotic systems, including human breast cancer and Arabidopsis thaliana cold acclimation. Our study identified TACSTD2 (TROP2) as a target gene for human breast cancer and discovered its regulation by transcription factors CREB, as well as NFkB. We also predict KIF2C is a target gene for ER-/HER2- breast cancer and is positively regulated by E2F1. The predictions were further confirmed through experimental studies.The implementation and detailed protocol of the layer approach is available at http://www.egr.msu.edu/changroup/Protocols/Three-layer%20approach%20 to % 20reconstruct%20condition.html.

SUBMITTER: Wu M

PROVIDER: S-EPMC3673221 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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A multi-layer inference approach to reconstruct condition-specific genes and their regulation.

Wu Ming M Liu Li L Hijazi Hussein H Chan Christina C

Bioinformatics (Oxford, England) 20130422 12

<h4>Unlabelled</h4>An important topic in systems biology is the reverse engineering of regulatory mechanisms through reconstruction of context-dependent gene networks. A major challenge is to identify the genes and the regulations specific to a condition or phenotype, given that regulatory processes are highly connected such that a specific response is typically accompanied by numerous collateral effects. In this study, we design a multi-layer approach that is able to reconstruct condition-speci ...[more]

PMID: 23610368

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Project description:The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.

Dataset Information

A multi-layer inference approach to reconstruct condition-specific genes and their regulation.

Publications

A multi-layer inference approach to reconstruct condition-specific genes and their regulation.

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