Making sense of the biological complexity through the platform-driven unification of the analytical and visualization tasks

Koutsandreas, Theodoras; Pilalis, Eleftherios; Vlachavas, Efstathios; Koczan, Dirk; Klippel, Sven; Dimitrakopoulou-Strauß, Antonia; Valavanis, Ioannis; Chatziioannou, Aristotelis

doi:10.1109/bibe.2015.7367724

Cited by 4 publications

(5 citation statements)

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“…(43). GO term analysis was conducted using the Bioinfominer integrative analysis platform (69) which integrates established algorithms for prioritized pathway and regulatory gene analysis through the exploitation of statistical resampling and semantic similarity measures applied to the Gene Ontology Tree network (unpublished data). Enrichment column represents the number of genes identified out of the total number of genes in that GO term.…”

Section: Examples Of Dna Methylation Changes In Response To Environmementioning

confidence: 99%

Epigenetic memory in response to environmental stressors

et al. 2017

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eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. 1 Draft August 3rd 2016Paolo Vineis (1), Aristotelis Chatziioannou (2), Vincent Cunliffe (3) Conflicts of interest: None declared 2 AbstractExposure to environmental stressors, toxicants and nutrient deficiencies can affect DNA in a number of ways. While some exposures cause damage and alter the structure of DNA, there is increasing evidence that the same or other environmental exposures, including those experienced during fetal development in utero, can cause epigenetic effects which modulate DNA function and gene expression. Some epigenetic changes to DNA that affect gene transcription are at least partially reversible (i.e. they can be enzymatically reversed after cessation of exposure to environmental agents), but some epigenetic modifications seem to persist even for decades. To explain the effects of early life experiences (such as famine and other exposures to stressors) on the long-term persistence of specific patterns of epigenetic modifications, such as DNA methylation, we propose an analogy with immune memory. We propose that an epigenetic memory can be established and maintained in self-renewing stem cell compartments. We suggest that the observations on early life effects on adult diseases and the persistence of methylation changes in smokers support our hypothesis, for which a mechanistic basis, however, needs to be further clarified. We outline a new model based on methylation changes. Though these changes seem to be mainly adaptive, they are also implicated in the pathogenesis and onset of diseases, depending on individual genotypic background and/or types of subsequent exposures. Elucidating the relationships between the adaptive and maladaptive consequences of the epigenetic modifications that result from complex environmental exposures is a major challenge for current and future research in epigenetics.

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Section: Examples Of Dna Methylation Changes In Response To Environmementioning

confidence: 99%

Epigenetic memory in response to environmental stressors

et al. 2017

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“…The molecular pathway and functional analysis was performed using BioInfoMiner [ 13 , 67 ], which exploits several vocabularies with a hierarchical structure, such as Gene Ontology, Reactome Pathways, and MGI and HPO phenotype ontologies, in order to provide a multi-faceted, functional, gene-level description of the phenotypes studied. The analysis comprises the ranking and prioritization of enriched biological processes and genes.…”

Section: Methodsmentioning

confidence: 99%

“…These subsets of genes, termed “linker genes”, are implicated as central actors in various distinct biological processes, thus providing a holistic view of the disease under investigation. The methodology is described in Koutsandreas et al [ 67 ].…”

Section: Methodsmentioning

confidence: 99%

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE)

Yeles

Vlachavas

Papadodima

et al. 2017

Cancers

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Ionizing radiation-induced bystander effects (RIBE) encompass a number of effects with potential for a plethora of damages in adjacent non-irradiated tissue. The cascade of molecular events is initiated in response to the exposure to ionizing radiation (IR), something that may occur during diagnostic or therapeutic medical applications. In order to better investigate these complex response mechanisms, we employed a unified framework integrating statistical microarray analysis, signal normalization, and translational bioinformatics functional analysis techniques. This approach was applied to several microarray datasets from Gene Expression Omnibus (GEO) related to RIBE. The analysis produced lists of differentially expressed genes, contrasting bystander and irradiated samples versus sham-irradiated controls. Furthermore, comparative molecular analysis through BioInfoMiner, which integrates advanced statistical enrichment and prioritization methodologies, revealed discrete biological processes, at the cellular level. For example, the negative regulation of growth, cellular response to Zn2+-Cd2+, and Wnt and NIK/NF-kappaB signaling, thus refining the description of the phenotypic landscape of RIBE. Our results provide a more solid understanding of RIBE cell-specific response patterns, especially in the case of high-LET radiations, like α-particles and carbon-ions.

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“…These subsets of genes, termed "linker genes", are implicated as central actors in various distinct biological processes, thus providing a holistic view of the disease under investigation. The methodology is described in Koutsandreas et al [66] In order to derive a gene signature characterizing RIBE, we combined different subsets of linker genes, derived from the application of the methodology with different vocabularies. namely GO [11,12], Reactome [13,14], and MGI [15][16][17].…”

Section: Computational Pipeline and Data Analysismentioning

confidence: 99%

“…For all statistical comparisons (except the ANOVA tests in some specific cases), we used the same double cutoff to obtain the DE gene lists: an absolute value of log2 fold change greater than 0.5 and an adjusted p-value less than 0.05 (FDR) [65]. The molecular pathway and functional analysis was performed using BioInfoMiner [18,66], which exploits several vocabularies with hierarchical structure, such as Gene Ontology, Reactome Pathways, MGI and HPO phenotype ontologies, in order to provide a multi-faceted, functional, gene-level description of the phenotypes studied. The analysis comprises ranking and prioritization of enriched biological processes and genes.…”

Section: Computational Pipeline and Data Analysismentioning

confidence: 99%

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE) 

Yeles

Vlachavas

Papadodima

et al. 2017

Preprint

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Ionizing radiation-induced bystander effects (RIBE) encompass a number of effects with potential for a plethora of damages in adjacent non-irradiated tissue. The cascade of molecular events is initiated in response to the exposure to ionizing radiation (IR), something that may occur during diagnostic or therapeutic medical applications. In order to better investigate these complex response mechanisms, we employed a unified framework integrating statistical microarray analysis, signal normalization and translational bioinformatics functional analysis techniques. This approach was applied to several microarray datasets from Gene Expression Omnibus (GEO) related to RIBE. The analysis produced lists of differentially expressed genes, contrasting bystander and irradiated samples versus sham-irradiated controls. Furthermore, comparative molecular analysis through BioInfoMiner, which integrates advanced statistical enrichment and prioritization methodologies, revealed discrete biological processes, at the cellular level. For example, negative regulation of growth, cellular response to Zn2+-Cd2+, Wnt and NIK/NFkappaB signalling, which refine the description of the phenotypic landscape of RIBE. Our results provide a more solid understanding of RIBE cell-specific response patterns, especially in the case of high-LET radiations like α-particles and carbon-ions.

show abstract

Making sense of the biological complexity through the platform-driven unification of the analytical and visualization tasks

Cited by 4 publications

References 24 publications

Epigenetic memory in response to environmental stressors

Epigenetic memory in response to environmental stressors

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE)

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE)

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Making sense of the biological complexity through the platform-driven unification of the analytical and visualization tasks

Cited by 4 publications

References 24 publications

Epigenetic memory in response to environmental stressors

Epigenetic memory in response to environmental stressors

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE)

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE)&nbsp;

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Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE)