An algorithmic approach for the effect of transcription factor binding sites over functional gene regulatory networks

Koumakis, Lefteris; Potamias, George; Marias, Kostas; Tsiknakis, Manolis

doi:10.1109/bibe.2015.7367662

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Combining NGS (ChIP-seq) and GRNs can help the biologists to discover gene pathways associated with expression of genes in different phenotypes and help them understand complex biological processes such as cell cycle, cell differentiation, cell apoptosis, diseases. As a result, through the combination of a pathway analysis and the TFs that may disrupt key pathways for the specific disease, researchers can discover new treatments [16] while ChIP-seq can provide information related to the regulation of specific genes [17].…”

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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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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MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

et al. 2016

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Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

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An algorithmic approach for the effect of transcription factor binding sites over functional gene regulatory networks

Cited by 2 publications

References 24 publications

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

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

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

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