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Background: The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. Results: We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. Conclusion: We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease. [ABSTRACT FROM AUTHOR]

Background: Soybean establishes a mutualistic interaction with nitrogen-fixing rhizobacteria, acquiring most of its nitrogen requirements through symbiotic nitrogen fixation. This crop is susceptible to water deficit; evidence suggests that its nodulation status—whether it is nodulated or not—can influence how it responds to water deficit. The translational control step of gene expression has proven relevant in plants subjected to water deficit. Results: Here, we analyzed soybean roots' differential responses to water deficit at transcriptional, translational, and mixed (transcriptional + translational) levels. Thus, the transcriptome and translatome of four combined-treated soybean roots were analyzed. We found hormone metabolism-related genes among the differentially expressed genes (DEGs) at the translatome level in nodulated and water-restricted plants. Also, weighted gene co-expression network analysis followed by differential expression analysis identified gene modules associated with nodulation and water deficit conditions. Protein-protein interaction network analysis was performed for subsets of mixed DEGs of the modules associated with the plant responses to nodulation, water deficit, or their combination. Conclusions: Our research reveals that the stand-out processes and pathways in the before-mentioned plant responses partially differ; terms related to glutathione metabolism and hormone signal transduction (2 C protein phosphatases) were associated with the response to water deficit, terms related to transmembrane transport, response to abscisic acid, pigment metabolic process were associated with the response to nodulation plus water deficit. Still, two processes were common: galactose metabolism and branched-chain amino acid catabolism. A comprehensive analysis of these processes could lead to identifying new sources of tolerance to drought in soybean. [ABSTRACT FROM AUTHOR]

Diabetes cell replacement therapy has the potential to be transformed by human pluripotent stem cell-derived β cells (SC-β cells). However, the precise identity of SC-β cells in relationship to primary fetal and adult β-cells remains unclear. Here, we used single-cell sequencing datasets to characterize the transcriptional identity of islets from in vitro differentiation, fetal islets, and adult islets. Our analysis revealed that SC-β cells share a core β-cell transcriptional identity with human adult and fetal β-cells, however SC-β cells possess a unique transcriptional profile characterized by the persistent expression and activation of progenitor and neural-biased gene networks. These networks are present in SC-β cells, irrespective of the derivation protocol used. Notably, fetal β-cells also exhibit this neural signature at the transcriptional level. Our findings offer insights into the transcriptional identity of SC-β cells and underscore the need for further investigation of the role of neural transcriptional networks in their development. [ABSTRACT FROM AUTHOR]

Background: Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human. Results: In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery. Conclusions: We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered. [ABSTRACT FROM AUTHOR]

Background: Banana fruits are rich in various high-value metabolites and play a key role in the human diet. Of these components, carotenoids have attracted considerable attention due to their physiological role and human health care functions. However, the accumulation patterns of carotenoids and genome-wide analysis of gene expression during banana fruit development have not been comprehensively evaluated. Results: In the present study, an integrative analysis of metabolites and transcriptome profiles in banana fruit with three different development stages was performed. A total of 11 carotenoid compounds were identified, and most of these compounds showed markedly higher abundances in mature green and/or mature fruit than in young fruit. Results were linked to the high expression of carotenoid synthesis and regulatory genes in the middle and late stages of fruit development. Co-expression network analysis revealed that 79 differentially expressed transcription factor genes may be responsible for the regulation of LCYB (lycopene β-cyclase), a key enzyme catalyzing the biosynthesis of α- and β-carotene. Conclusions: Collectively, the study provided new insights into the understanding of dynamic changes in carotenoid content and gene expression level during banana fruit development. [ABSTRACT FROM AUTHOR]

Background: Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using L 1 -penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively. Results: We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein–protein interaction networks. Conclusions: The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with pronounced heritability in the general population. This is largely attributable to the effects of polygenic susceptibility, with inherited liability exhibiting distinct sex differences in phenotypic expression. Attempts to model ASD in human cellular systems have principally involved rare de novo mutations associated with ASD phenocopies. However, by definition, these models are not representative of polygenic liability, which accounts for the vast share of population-attributable risk. Methods: Here, we performed what is, to our knowledge, the first attempt to model multiplex autism using patient-derived induced pluripotent stem cells (iPSCs) in a family manifesting incremental degrees of phenotypic expression of inherited liability (absent, intermediate, severe). The family members share an inherited variant of uncertain significance (VUS) in GPD2, a gene that was previously associated with developmental disability but here is insufficient by itself to cause ASD. iPSCs from three first-degree relatives and an unrelated control were differentiated into both cortical excitatory (cExN) and cortical inhibitory (cIN) neurons, and cellular phenotyping and transcriptomic analysis were conducted. Results: cExN neurospheres from the two affected individuals were reduced in size, compared to those derived from unaffected related and unrelated individuals. This reduction was, at least in part, due to increased apoptosis of cells from affected individuals upon initiation of cExN neural induction. Likewise, cIN neural progenitor cells from affected individuals exhibited increased apoptosis, compared to both unaffected individuals. Transcriptomic analysis of both cExN and cIN neural progenitor cells revealed distinct molecular signatures associated with affectation, including the misregulation of suites of genes associated with neural development, neuronal function, and behavior, as well as altered expression of ASD risk-associated genes. Conclusions: We have provided evidence of morphological, physiological, and transcriptomic signatures of polygenic liability to ASD from an analysis of cellular models derived from a multiplex autism family. ASD is commonly inherited on the basis of additive genetic liability. Therefore, identifying convergent cellular and molecular phenotypes resulting from polygenic and monogenic susceptibility may provide a critical bridge for determining which of the disparate effects of rare highly deleterious mutations might also apply to common autistic syndromes. [ABSTRACT FROM AUTHOR]

Background: The ratio of sugars to organic acids, two of the major metabolites in fleshy fruits, has been considered the most important contributor to fruit sweetness. Although accumulation of sugars and acids have been extensively studied, whether plants evolve a mechanism to maintain, sense or respond to the fruit sugar/acid ratio remains a mystery. In a prior study, we used an integrated systems biology tool to identify a group of 39 acid-associated genes from the fruit transcriptomes in four sweet orange varieties (Citrus sinensis L. Osbeck) with varying fruit acidity, Succari (acidless), Bingtang (low acid), and Newhall and Xinhui (normal acid). Results: We reanalyzed the prior sweet orange fruit transcriptome data, leading to the identification of 72 genes highly correlated with the fruit sugar/acid ratio. The majority of these sugar/acid ratio-related genes are predicted to be involved in regulatory functions such as transport, signaling and transcription or encode enzymes involved in metabolism. Surprisingly, only three of these sugar/acid ratio-correlated genes are weakly correlated with sugar level and none of them overlaps with the acid-associated genes. Weighted Gene Coexpression Network Analysis (WGCNA) has revealed that these genes belong to four modules, Blue, Grey, Brown and Turquoise, with the former two modules being unique to the sugar/acid ratio control. Conclusion: Our results indicate that orange fruits contain a possible mechanistically distinct class of genes that may potentially be involved in maintaining fruit sugar/acid ratios and/or responding to the cellular sugar/acid ratio status. Therefore, our analysis of orange transcriptomes provides an intriguing insight into the potentially novel genetic or molecular mechanisms controlling the sugar/acid ratio in fruits. [ABSTRACT FROM AUTHOR]

Background: Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. Results: We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the diseaseassociated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. Conclusion: Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits. [ABSTRACT FROM AUTHOR]

Background: While hundreds of genes have been implicated already in the etiology of schizophrenia, the exact cause is not known or the disease is considered multigenic in origin. Recent discoveries of new types of RNAs and the gradual elimination of the "junk DNA" hypothesis refocused the attention on the noncoding part of the human genome. Here we re-analyzed a recent dataset of differentially methylated genes from schizophrenic patients and cross-tabulated them with cis regulatory and repetitive elements and microRNAs known to be involved in schizophrenia. Results: We found that the number of schizophrenia-related (SZ) microRNA targets follows a scale-free distribution with several microRNA hubs and that schizophrenia-related microRNAs with shared targets form a small-world network. The top ten microRNAs with the highest number of SZ gene targets regulate approximately 80 % of all microRNA-regulated genes whereas the top two microRNAs regulate 40-52 % of all such genes. We also found that genes that are regulated by the same microRNAs tend to have more protein-protein interactions than randomly selected schizophrenia genes. This highlights the role microRNAs possibly play in coordinating the abundance of interacting proteins, an important function that has not been sufficiently explored before. The analysis revealed that GABBR1 is regulated by both of the top two microRNAs and acts as a hub by interacting with many schizophrenia-related genes and sharing several types of transcription-binding sites with its interactors. We also found that differentially methylated repetitive elements are significantly more methylated in schizophrenia, pointing out their potential role in the disease. Conclusions: We find that GABBR1 has a central importance in schizophrenia, even if no direct cause and effect have been shown for it for the time. In addition to being a hub in microRNA-derived regulatory pathways and protein-protein interactions, its centrality is also supported by the high number of cis regulatory elements and transcription factor-binding sites that regulate its transcription. These findings are in line with several genome-wide association studies that repeatedly find the major histocompatibility region (where GABBR1 is located) to have the highest number of single nucleotide polymorphisms in schizophrenics. Our model also offers an explanation for the downregulation of protein kinase B, another consistent finding in schizophrenic patients. Our observations support the notion that microRNAs fine-tune the amount of proteins acting in the same biological pathways in schizophrenia, giving further support to the emerging theory of competing endogenous RNAs. [ABSTRACT FROM AUTHOR]

Background: The transcriptional response of adult zebra mussels (Dreissena polymorpha) to heavy metals (mercury, copper, and cadmium) was analyzed by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) to study the coordinated regulation of different metal-, oxidative stress- and xenobiotic defence-related genes in gills and digestive gland. Regulatory network analyses allowed the comparison of this response between different species and taxa. Results: Chemometric analyses allowed identifying the effects of these metals clearly separating control and treated samples of both tissues. Interactions between the different genes, either in the same or between both tissues, were analysed to identify correlations and to propose stress-related genes' regulatory networks. These networks were finally compared with existing data from human, mouse, zebrafish, Drosophila and the roundworm to evaluate their mechanistically-known response to metals (and to stressors in general) with the correlations observed in the still poorly-known, invasive zebra mussel. Conclusions: Our analyses found a general conservation of regulation genes and of their interactions among the different considered species, and may serve as a guide to extrapolate regulatory data from model species to lesser-known environmentally (or medically) relevant species. [ABSTRACT FROM AUTHOR]

Background: In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis. Results: We introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership. Conclusions: The results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html. [ABSTRACT FROM AUTHOR]

Background Krüppel-like factors (KLFs) are a group of master regulators of gene expression conserved from flies to human. However, scant information is available on either the mechanisms or functional impact of the coupling of KLF proteins to chromatin remodeling machines, a deterministic step in transcriptional regulation. Results and discussion In the current study, we use genome-wide analyses of chromatin immunoprecipitation (ChIPon- Chip) and Affymetrix-based expression profiling to gain insight into how KLF11, a human transcription factor involved in tumor suppression and metabolic diseases, works by coupling to three co-factor groups: the Sin3-histone deacetylase system, WD40-domain containing proteins, and the HP1-histone methyltransferase system. Our results reveal that KLF11 regulates distinct gene networks involved in metabolism and growth by using single or combinatorial coupling events. Conclusion This study, the first of its type for any KLF protein, reveals that interactions with multiple chromatin systems are required for the full gene regulatory function of these proteins. [ABSTRACT FROM AUTHOR]

Background Discovering functional relationships of genes through cell-based phenotyping has become an important approach in functional genomics. High-throughput imaging offers the ability to quantitatively assess complex phenotypes after perturbation by RNA interference (RNAi). Such image-based high-throughput RNAi screening studies have facilitated the discovery of novel components of gene networks and their interactions. Images generated by automated microscopy are typically analyzed by extracting quantitative features of individual cells, resulting in large multidimensional data sets. Robust and sensitive methods to interpret these data sets and to derive biologically relevant information in a high-throughput and unbiased manner remain to be developed. Results Here we propose a new analysis method, PhenoDissim, which computes the phenotypic dissimilarity between cell populations via Support Vector Machine classification and cross validation. Applying this method to a kinome RNAi screening data set, we demonstrate that the proposed method shows a good replicate reproducibility, separation of controls and clustering quality, and we are able to identify siRNA phenotypes and discover potential functional links between genes. Conclusions PhenoDissim is a novel analysis method for image-based high-throughput screen, relying on two parameters which can be automatically optimized without a priori knowledge. PhenoDissim is freely available as an R package. [ABSTRACT FROM AUTHOR]

Background: Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic sideeffects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids. Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects. Results: We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes. With this approach we found several genes that already are considered markers of GC induced IR, such as phosphoenolpyruvate carboxykinase (PCK) and glucose-6- phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR. Conclusions: With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks. [ABSTRACT FROM AUTHOR]

Background: Insufficient production of flower buds is an intractable problem in 'Fuji' apple orchards. Although cytokinin (CK) promotes flower bud formation in apple trees, little is known about the mechanisms regulating this phenomenon. Results: In the present study, high-throughput RNA sequencing (RNA-Seq) of 'Nagafu No. 2' buds was conducted to characterize the transcriptional response to 6-BA treatment during key period of floral transition. A weighted gene co-expression network analysis (WGCNA) of the differentially expressed genes identified hormone signal transduction pathways, totaling 84 genes were highly correlated with the expression pattern of flowering-time genes. The up-regulation of CK signal components and a gibberellin (GA) signal repressor were found to contribute to the promotion of floral transition. In relative comparison to non-treated buds, a series of sugar metabolism- and signal- related genes were associated with relatively high levels of sucrose, fructose, and glucose during floral induction in the 6-BA treated buds. Several transcription factors (i.e. SPLs, SOC1, FD, and COL) that are involved in GA, aging, and photoperiod-regulated flowering pathways were also upregulated by the 6-BA treatment. In addition, potential transcription factors integrating CK signaling to trigger floral induction in apple were also assessed; including PHYTO-CHROME-INTERACTING FACTOR (PIF1,3), WUSCHEL-related homeobox (WOX3,13), and CK response regulators (ARR2). Conclusions: The present study provides insight into the response of flowering and development-related pathways and transcription factors to 6-BA during the period of floral transition in apple. It extends our knowledge of the fundamental mechanisms associated with CK-regulated floral transition in apple trees. [ABSTRACT FROM AUTHOR]

GJA1 (connexin43) has been predicted as the top key driver of an astrocyte enriched subnetwork associated with Alzheimer's disease (AD). In this study, we comprehensively examined GJA1 expression across 29 transcriptomic and proteomic datasets from post-mortem AD and normal control brains. We demonstrated that GJA1 was strongly associated with AD amyloid and tau pathologies and cognitive functions. RNA sequencing analysis of Gja1−/− astrocytes validated that Gja1 regulated the subnetwork identified in AD, and many genes involved in Aβ metabolism. Astrocytes lacking Gja1 showed reduced Apoe protein levels as well as impaired Aβ phagocytosis. Consistent with this, wildtype neurons co-cultured with Gja1−/− astrocytes contained higher levels of Aβ species than those with wildtype astrocytes. Moreover, Gja1−/− astrocytes was more neuroprotective under Aβ stress. Our results underscore the importance of GJA1 in AD pathogenesis and its potential for further investigation as a promising pharmacological target in AD. [ABSTRACT FROM AUTHOR]

Background: Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members., Results: We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones., Conclusions: FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of experimental gene sets, both for different global networks and for different types of interactions. Using examples of thyroid cancer and apoptosis networks, we have shown that the links over-represented in the analyzed network in comparison with the random ones make possible a biological interpretation of the original gene/protein sets. The FunGeneNet web tool for assessment of the functional enrichment of networks is available at http://www-bionet.sscc.ru/fungenenet/ .