protein-DNA Interaction Sites Research Articles

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest

Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.

Genome-wide protein–DNA interaction site mapping in bacteria using a double-stranded DNA-specific cytosine deaminase

DNA–protein interactions are central to fundamental cellular processes, yet widely implemented technologies for measuring these interactions on a genome scale in bacteria are laborious and capture only a snapshot of binding events. We devised a facile method for mapping DNA–protein interaction sites in vivo using the double-stranded DNA-specific cytosine deaminase toxin DddA. In 3D-seq (DddA-sequencing), strains containing DddA fused to a DNA-binding protein of interest accumulate characteristic mutations in DNA sequence adjacent to sites occupied by the DNA-bound fusion protein. High-depth sequencing enables detection of sites of increased mutation frequency in these strains, yielding genome-wide maps of DNA–protein interaction sites. We validated 3D-seq for four transcription regulators in two bacterial species, Pseudomonas aeruginosa and Escherichia coli. We show that 3D-seq offers ease of implementation, the ability to record binding event signatures over time and the capacity for single-cell resolution.

ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis.

Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is a technique used to identify protein–DNA interaction sites through antibody pull-down, sequencing and analysis; with enrichment ‘peak’ calling being the most critical analytical step. Benchmarking studies have consistently shown that peak callers have distinct selectivity and specificity characteristics that are not additive and seldom completely overlap in many scenarios, even after parameter optimization. We therefore developed ChIP-AP, an integrated ChIP-seq analysis pipeline utilizing four independent peak callers, which seamlessly processes raw sequencing files to final result. This approach enables (1) better gauging of peak confidence through detection by multiple algorithms, and (2) more thoroughly surveys the binding landscape by capturing peaks not detected by individual callers. Final analysis results are then integrated into a single output table, enabling users to explore their data by applying selectivity and sensitivity thresholds that best address their biological questions, without needing any additional reprocessing. ChIP-AP therefore presents investigators with a more comprehensive coverage of the binding landscape without requiring additional wet-lab observations.

Application of affinity capillary electrophoresis in the study of protein-DNA interactions

Protein-DNA interactions play essential roles in various biological events that determine the cell fate. Research on the molecular mechanism of protein-DNA interactions has helped elucidate diverse fundamental life processes, thereby providing theoretical guidance for establishing clinical treatment and screening potential drug of target diseases. Furthermore, well-known protein-DNA interactions have been utilized to develop advanced bioengineering and bioanalytical techniques, therefore providing robust technical support for related research. Hence, it is important to establish sensitive and rapid analytical methods to study protein-DNA interactions. High-performance capillary electrophoresis (CE) has been widely used in many research fields such as chemistry, life sciences, and environmental sciences, mainly due to its advantages including ultra-high separation efficiency, extremely low sample consumption, and short analysis time. For instance, affinity capillary electrophoresis (ACE) has become an important analytical tool for investigating molecular interactions. In this paper, we review the applications of ACE in studying protein-DNA interactions since it was first proposed in 1992, addressing previous significant work in this field. Three major aspects have been summarized in this review: (1) brief introduction to the development of ACE technique; (2) applications of ACE in the fundamental research on the molecular mechanism of protein-DNA interactions; and (3) applications of well-known protein-DNA interactions in CE-based detection of target molecules and reactions. In the first aspect, along with the concept and separation modes of ACE, general strategies to enhance the analytical ability of ACE are briefly introduced. In the second aspect, the applications of ACE in studying several important protein-DNA interactions involving transcription factors (e.g., GCN4), DNA repair proteins (e.g., UvrA, UvrB, and RecA), and methylated DNA-binding proteins (MBDs) are reviewed. In the third aspect, the applications of well-known molecular interactions (e.g., antigen-antibody, aptamer-target, etc.) to facilitate CE-based detection of target molecules (e.g., DNA adducts, DNA methylation, microRNA, single nucleotide polymorphism, etc.) and target reactions (e.g., DNA strand exchange) are addressed. Finally, we prospect and discuss the advancements of ACE that can be established in future studies. The following two aspects should be improved in future ACE analysis: (1) the advantages of extremely low volume consumption and short analysis time should be fully utilized to develop sensitive and high-throughput CE platforms for the assessment of rare biological samples and massive uncertain samples, respectively; (2) ACE should be combined with other advanced techniques, such as DNA sequencing and mass spectrometry, to rapidly screen and identify the precise interacting sites of unknown protein-DNA interactions.

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments.

The DNA adenine methyltransferase identification (DamID) assay is a powerful method to detect protein-DNA interactions both locally and genome-wide. It is an alternative approach to chromatin immunoprecipitation (ChIP). An expressed fusion protein consisting of the protein of interest and the E. coli DNA adenine methyltransferase can methylate the adenine base in GATC motifs near the sites of protein-DNA interactions. Adenine-methylated DNA fragments can then be specifically amplified and detected. The original DamID assay detects the genomic locations of methylated DNA fragments by hybridization to DNA microarrays, which is limited by the availability of microarrays and the density of predetermined probes. In this paper, we report the detailed protocol of integrating high throughput DNA sequencing into DamID (DamID-seq). The large number of short reads generated from DamID-seq enables detecting and localizing protein-DNA interactions genome-wide with high precision and sensitivity. We have used the DamID-seq assay to study genome-nuclear lamina (NL) interactions in mammalian cells, and have noticed that DamID-seq provides a high resolution and a wide dynamic range in detecting genome-NL interactions. The DamID-seq approach enables probing NL associations within gene structures and allows comparing genome-NL interaction maps with other functional genomic data, such as ChIP-seq and RNA-seq.

Abstract 1652: Therapeutic targeting of ETS factor ERG for the treatment of prostate cancer

Abstract Prostate cancer (PCa) is one of the leading causes of cancer-related death in men worldwide. The common treatment option for recurring and advanced PCa focuses on inhibiting the androgen receptor (AR). Unfortunately, despite an initial response to this treatment, drug resistance occurs, and the cancer relapses to an incurable, castration-resistant form; thus, there is a pressing need for new therapeutics. Previous research has shown that in up to 50% of all prostate cancer cases, the cause of the disease may be attributed to a common genomic rearrangement, the fusion between TMPRSS2 and ERG (ETS-related gene). ERG is a transcription factor mainly involved in hematopoiesis regulation during embryonic development, and it is not normally expressed in prostate cells in adults. However, its fusion with the TMPRSS2 promoter puts ERG under the regulation of AR, and as a consequence, ERG is one of the most commonly overexpressed genes in PCa. ERG overexpression in prostate epithelium has been shown to induce transformation and promote epithelial-mesenchymal transition (EMT) that gives cancer cells enhanced migratory and invasive characteristics. Currently, there is no approved therapeutic targeting ERG or any other member of the ETS family. While targeting transcription factors has been challenging, our integrated research team is specialized in targeting protein-DNA interaction sites. Using our established computer-aided drug discovery pipeline, we have identified several small molecules that can bind to and inhibit ERG. A total of 133 candidate compounds, pre-selected from the in silico screening of millions of chemical structures, were tested using a luciferase-based transcriptional reporter assay across two cell lines: the TMPRSS2-ERG fusion positive VCaP cell line, and the human prostate epithelial cell line (PNT1B) engineered to constitutively express ERG. In addition to transcriptional inhibition, the most potent compounds inhibited migration of PNT1B-ERG cells as demonstrated by a Real-Time Cell Analyzer. Significantly, both compounds shifted the binding spectra in protein NMR assays, indicating their direct interactions with residues located in the DNA binding domain of the ERG protein. We anticipate that results from this project will lead to the development of new drugs that can be used alternatively or synergistically with current anti-AR therapy to benefit patients with the most deadly forms of prostate cancer. (Supported by a grant from Prostate Cancer Canada) Citation Format: Mani Roshan-Moniri, Michael Hsing, Miriam S. Butler, Desmond Lau, Peter Axerio-Cilies, Paul Yen, Ari Kim, Scott Lien, Marta Mroczek, Dennis Ma, Huifang Li, Yubin Guo, Fuqiang Ban, Fariba Ghaidi, Eric LeBlanc, Lawrence McIntosh, Michael Cox, Artem Cherkasov, Paul S. Rennie. Therapeutic targeting of ETS factor ERG for the treatment of prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1652. doi:10.1158/1538-7445.AM2015-1652

Direct optical mapping of transcription factor binding sites on field-stretched λ-DNA in nanofluidic devices.

Mapping transcription factor (TF) binding sites along a DNA backbone is crucial in understanding the regulatory circuits that control cellular processes. Here, we deployed a method adopting bioconjugation, nanofluidic confinement and fluorescence single molecule imaging for direct mapping of TF (RNA polymerase) binding sites on field-stretched single DNA molecules. Using this method, we have mapped out five of the TF binding sites of E. coli RNA polymerase to bacteriophage λ-DNA, where two promoter sites and three pseudo-promoter sites are identified with the corresponding binding frequency of 45% and 30%, respectively. Our method is quick, robust and capable of resolving protein-binding locations with high accuracy (∼ 300 bp), making our system a complementary platform to the methods currently practiced. It is advantageous in parallel analysis and less prone to false positive results over other single molecule mapping techniques such as optical tweezers, atomic force microscopy and molecular combing, and could potentially be extended to general mapping of protein–DNA interaction sites.

Predicting DNA-binding sites of proteins based on sequential and 3D structural information

Protein-DNA interactions play important roles in many biological processes. To understand the molecular mechanisms of protein-DNA interaction, it is necessary to identify the DNA-binding sites in DNA-binding proteins. In the last decade, computational approaches have been developed to predict protein-DNA-binding sites based solely on protein sequences. In this study, we developed a novel predictor based on support vector machine algorithm coupled with the maximum relevance minimum redundancy method followed by incremental feature selection. We incorporated not only features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure, solvent accessibility, but also five three-dimensional (3D) structural features calculated from PDB data to predict the protein-DNA interaction sites. Feature analysis showed that 3D structural features indeed contributed to the prediction of DNA-binding site and it was demonstrated that the prediction performance was better with 3D structural features than without them. It was also shown via analysis of features from each site that the features of DNA-binding site itself contribute the most to the prediction. Our prediction method may become a useful tool for identifying the DNA-binding sites and the feature analysis described in this paper may provide useful insights for in-depth investigations into the mechanisms of protein-DNA interaction.

Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain.

DNA methylation plays critical roles in the nervous system and has been traditionally considered to be restricted to CpG dinucleotides in metazoan genomes. Here we show that the single-base resolution DNA methylome from adult mouse dentate neurons consists of both CpG (~75%) and CpH (~25%) methylation (H = A/C/T). Neuronal CpH methylation is conserved in human brains, enriched in low CpG-density regions, depleted at protein-DNA interaction sites, and anti-correlated with gene expression. Functionally, both mCpGs and mCpHs can repress transcription in vitro and are recognized by MeCP2 in neurons in vivo. Unlike most CpG methylation, CpH methylation is established de novo during neuronal maturation and requires DNMT3A for active maintenance in post-mitotic neurons. These characteristics of CpH methylation suggest a significantly expanded proportion of the neuronal genome under cytosine methylation regulation and provide a new foundation for understanding the role of this key epigenetic modification in the nervous system.

The Cooperative Binding Energetics of CytR and cAMP Receptor Protein Support a Quantitative Model of Differential Activation and Repression of CytR-Regulated Class III Escherichia coli Promoters

cAMP receptor protein (CRP) and CytR mediate positive and negative control of nine genes in Escherichia coli, most of which are involved in nucleoside catabolism and recycling. Five promoters share a common architecture in which tandem CRP sites flank an intervening CytR operator (CytO). CytR and CRP bind cooperatively to these promoters to form a three-protein, DNA-bound complex that controls activation and repression, the levels of which vary markedly among the promoters. To understand the specific combinatorial control mechanisms that are responsible for this outcome, we have used quantitative DNase I footprinting to generate individual site isotherms for each site of protein-DNA interaction. The intrinsic affinities of each transcription factor for its respective site and the specific patterns of cooperativity and competition underlying the molecular interactions at each promoter were determined by a global analysis of these titration data. Here we present results obtained for nupGP and tsxP2, adding to results published previously for deoP2, udpP, and cddP. These data allowed us to correlate the reported levels of activation, repression, and induction with the ligation states of these five promoters under physiologically relevant conditions. A general pattern of transcriptional regulation emerges that allows for complex patterns of regulation in this seemingly simple system.

Understanding histone-DNA interactions in the common bean (Phaseolus vulgaris L.)

The common bean (Phaseolus vulgaris L.) is an important source of plant protein, and member of the legume family, which includes soybean, cowpea, and lentils. With its relatively small genome size, well-characterized cytogenetics, and recently sequenced genome, common bean serves as a good model for legume epigenetics. Accumulation of recent and ongoing bean community-wide genomic and transcriptomic sequence data along with advances in sequencing technology enables the further understanding of the roles of histone-DNA interactions, small RNAs, and cytosine methylation in gene regulation in common bean. Till date, there has not been much progress in studying various epigenomic marks in common bean, and therefore, the long-term objective of this research is to identify protein-DNA interaction sites in the common bean genome to include histones, as well as, non-histone proteins such as transcription factors involved in biotic and abiotic stress. The short-term objective of this work is to develop the first reference epigenomes in four common bean genotypes ({type:entrez-nucleotide,attrs:{text:G19833,term_id:1340404,term_text:G19833}}G19833, Bat93, Jalo, and Sierra) as related to histone modifications. Common bean genotypes can be broadly classified into those of Andean and Mesoamerican origin, and these genotypes were selected for their importance and use in genomics, as well as breeding. The immediate goal of this work is to identify the hierarchy of binding of histones H4K12ac and H3K9me2 in the common bean genome using Illumina sequencing. To achieve this goal, a chromatin immunoprecipitation (ChIP) protocol was developed for common bean, and chromatin from leaves of these four genotypes was isolated and used for ChIP and ChIP-sequencing (ChIP-seq). Sequence analyses of this data is enabling the understanding of the layout of these histone modifications across the genomes of these four genotypes when compared to the reference genome sequence of {type:entrez-nucleotide,attrs:{text:G19833,term_id:1340404,term_text:G19833}}G19833. This work serves as the first reference histone-DNA interaction map in common bean and lays a foundation for comprehensive analyses and large-scale studies of histone modifications and global protein binding sites in this important plant species, further facilitating the annotation of the common bean genome.

The Human Mitochondrial Transcriptome

The human mitochondrial genome comprises a distinct genetic system transcribed as precursor polycistronic transcripts that are subsequently cleaved to generate individual mRNAs, tRNAs, and rRNAs. Here, we provide a comprehensive analysis of the human mitochondrial transcriptome across multiple cell lines and tissues. Using directional deep sequencing and parallel analysis of RNA ends, we demonstrate wide variation in mitochondrial transcript abundance and precisely resolve transcript processing and maturation events. We identify previously undescribed transcripts, including small RNAs, and observe the enrichment of several nuclear RNAs in mitochondria. Using high-throughput in vivo DNaseI footprinting, we establish the global profile of DNA-binding protein occupancy across the mitochondrial genome at single-nucleotide resolution, revealing regulatory features at mitochondrial transcription initiation sites and functional insights into disease-associated variants. This integrated analysis of the mitochondrial transcriptome reveals unexpected complexity in the regulation, expression, and processing of mitochondrial RNA and provides a resource for future studies of mitochondrial function (accessed at http://mitochondria.matticklab.com).

5-Hydroxymethylcytosine is associated with enhancers and gene bodies in human embryonic stem cells

Background5-Hydroxymethylcytosine (5hmC) was recently found to be abundantly present in certain cell types, including embryonic stem cells. There is growing evidence that TET proteins, which convert 5-methylcytosine (5mC) to 5hmC, play important biological roles. To further understand the function of 5hmC, an analysis of the genome-wide localization of this mark is required.ResultsHere, we have generated a genome-wide map of 5hmC in human embryonic stem cells by hmeDIP-seq, in which hydroxymethyl-DNA immunoprecipitation is followed by massively parallel sequencing. We found that 5hmC is enriched in enhancers as well as in gene bodies, suggesting a potential role for 5hmC in gene regulation. Consistent with localization of 5hmC at enhancers, 5hmC was significantly enriched in histone modifications associated with enhancers, such as H3K4me1 and H3K27ac. 5hmC was also enriched in other protein-DNA interaction sites, such as OCT4 and NANOG binding sites. Furthermore, we found that 5hmC regions tend to have an excess of G over C on one strand of DNA.ConclusionsOur findings suggest that 5hmC may be targeted to certain genomic regions based both on gene expression and sequence composition.

Rapid innovation in ChIP-seq peak-calling algorithms is outdistancing benchmarking efforts

The current understanding of the regulation of transcription does not keep the pace with the spectacular advances in the determination of genomic sequences. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) promises to give better insight into transcription regulation by locating sites of protein-DNA interactions. Such loci of putative interactions can be inferred from the genome-wide distributions of ChIP-seq data by peak-calling software. The analysis of ChIP-seq data critically depends on this step and a multitude of these peak-callers have been deployed in the recent years. A recent study reported severe variation among peak-calling results. Yet, peak-calling still lacks systematic quantitative benchmarking. Here, we summarize benchmarking efforts and explain potential drawbacks of each benchmarking method.

Position weight matrix model as a tool for the study of regulatory elements distribution across the DNA sequence

Ab initio methods of DNA regulatory sequence region prediction known as transcription factor binding sites (TFBS) are a very big challenge to modern bioinformatics. Although the currently available methods are not perfect they are fairly reliable and can be used to search for new potential protein-DNA interaction sites. The biggest problem of ab initio approaches is the very high false positive rate of predicted sites which resul ts mainly from the fact that TFBS are very short and highly degenerate. Because of that they can occur by chance every few hundred bases making the task of computational prediction extremely difficult if one aims to reduce the high false positive rate keeping highest possible sensitiv ity to predict biologically meaningful sequence regions. In this work we present a new application that can be used to predict TFBS regions in very large datasets based on position weight matrix models (PWM’s) using one of the most popular prediction methods. The presented application was used to predict the concentration of TFBS in a set of nearly 2.2 thousand unique sequences of human gene promoter regions. The study revealed that the concentration of TFBS further than 1kbp from the transcription initiation site is constant but it decreases rapidly while getting closer to the transcriptio n initiation site. The decreasing TFBS concentration in the vicinity of genes might result from evolutionary selection which keeps only sites responsible for interactions with proteins being par t of a specific regulatory mechanism leading to cells survival.

Csm3, Tof1, and Mrc1 Form a Heterotrimeric Mediator Complex That Associates with DNA Replication Forks

Mrc1 (mediator of replication checkpoint), Tof1 (topoisomerase I interacting factor), and Csm3 (chromosome segregation in meiosis) are checkpoint-mediator proteins that function during DNA replication and activate the effector kinase Rad53. We reported previously that Mrc1 and Tof1 are constituents of the replication machinery and that both proteins are required for the proper arrest and stabilization of replication forks in the presence of hydroxyurea. In our current study, we show that Csm3 is a component of moving replication forks and that both Tof1 and Csm3 are specifically required for the association of Mrc1 with these structures. In contrast, the deletion of mrc1 did not affect the association of Tof1 and Csm3 with the replication fork complex. In agreement with previous observations in yeast cells, the results of a baculovirus coexpression system showed that these three proteins interact directly with each other to form a mediator complex in the absence of replication forks.

Human DNA methylomes at base resolution show widespread epigenomic differences

DNA cytosine methylation is a central epigenetic modification that has essential roles in cellular processes including genome regulation, development and disease. Here we present the first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human embryonic stem cells and fetal fibroblasts, along with comparative analysis of messenger RNA and small RNA components of the transcriptome, several histone modifications, and sites of DNA-protein interaction for several key regulatory factors. Widespread differences were identified in the composition and patterning of cytosine methylation between the two genomes. Nearly one-quarter of all methylation identified in embryonic stem cells was in a non-CG context, suggesting that embryonic stem cells may use different methylation mechanisms to affect gene regulation. Methylation in non-CG contexts showed enrichment in gene bodies and depletion in protein binding sites and enhancers. Non-CG methylation disappeared upon induced differentiation of the embryonic stem cells, and was restored in induced pluripotent stem cells. We identified hundreds of differentially methylated regions proximal to genes involved in pluripotency and differentiation, and widespread reduced methylation levels in fibroblasts associated with lower transcriptional activity. These reference epigenomes provide a foundation for future studies exploring this key epigenetic modification in human disease and development.

Improved ChIP-chip analysis by a mixture model approach

BackgroundMicroarray analysis of immunoprecipitated chromatin (ChIP-chip) has evolved from a novel technique to a standard approach for the systematic study of protein-DNA interactions. In ChIP-chip, sites of protein-DNA interactions are identified by signals from the hybridization of selected DNA to tiled oligomers and are graphically represented as peaks. Most existing methods were designed for the identification of relatively sparse peaks, in the presence of replicates.ResultsWe propose a data normalization method and a statistical method for peak identification from ChIP-chip data based on a mixture model approach. In contrast to many existing methods, including methods that also employ mixture model approaches, our method is more flexible by imposing less restrictive assumptions and allowing a relatively large proportion of peak regions. In addition, our method does not require experimental replicates and is computationally efficient. We compared the performance of our method with several representative existing methods on three datasets, including a spike-in dataset. These comparisons demonstrate that our approach is more robust and has comparable or higher power than the other methods, especially in the context of abundant peak regions.ConclusionOur data normalization and peak detection methods have improved performance to detect peak regions in ChIP-chip data.

Impact of genome assembly status on ChIP-Seq and ChIP-PET data mapping

BackgroundChIP-Seq and ChIP-PET can potentially be used with any genome for genome wide profiling of protein-DNA interaction sites. Unfortunately, it is probable that most genome assemblies will never reach the quality of the human genome assembly. Therefore, it remains to be determined whether ChIP-Seq and ChIP-PET are practicable with genome sequences other than a few (e.g. human and mouse).FindingsHere, we used in silico simulations to assess the impact of completeness or fragmentation of genome assemblies on ChIP-Seq and ChIP-PET data mapping.ConclusionsMost currently published genome assemblies are suitable for mapping the short sequence tags produced by ChIP-Seq or ChIP-PET.

Survey of microarray technologies suitable to elucidate transcriptional networks as exemplified by studying KRAB zinc finger gene families

Current microarray systems are suitable to monitor genome-wide expression patterns, to detect single-nucleotide polymorphisms (SNP), to identify target genes of transcription factors and DNA-protein interaction sites thereof as well as to determine genomic sites that are modified by methylation of CpG islands. In this review, advantages and limitations of individual microarray technologies are presented as well as experiences from ongoing studies on KRAB zinc finger gene families are taken to exemplify how different microarray approaches are applicable to elucidate complex transcriptional networks of gene regulation. However, bioinformaticians should be aware that each microarray technology has limitations in its sensitivity and selectivity that has to be taken into account once data mining on comprehensive genome-wide microarray data is conducted. In many cases, microarray results are the initial step to identify target genes of interest and to study the molecular regulation of biological processes thereof followed and validated by complementary proteome, metabolome or toponome analysis. Thus, microarray technologies can be considered a reliable approach for determining gene functions that might be modulated by electromagnetic fields.