Integrating microarray data and gene regulatory networks: Survey and critical considerations

Koumakis, Lefteris; Potamias, George; Zervakis, Michalis; Moustakis, Vassilis

doi:10.1109/iwbe.2011.6079034

Cited by 2 publications

(1 citation statement)

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“…Many studies in the literature that deal with expression data and GRNs concern the reconstruction of networks using expression data [4]. Apart from the GRN reconstruction, many methodologies take advantage of the GRNs to group genes or select genes [1], [5]. The most advanced methods that combine GRNs and expression data take advantage of the semantics in a GRN such as the interaction of the genes and identify core regulatory genes that are potential targets for therapeutic intervention [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

Atsalaki

Koumakis

Potamias

et al. 2020

Preprint

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High-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIPseq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological subpathways.

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