Autonomous Metabolic Oscillations Robustly Gate the Early and Late Cell Cycle.

Eukaryotic cell division is known to be controlled by the cyclin/cyclin dependent kinase (CDK) machinery. However, eukaryotes have evolved prior to CDKs, and cells can divide in the absence of major cyclin/CDK components. We hypothesized that an autonomous metabolic oscillator provides dynamic triggers for cell-cycle initiation and progression. Using microfluidics, cell-cycle reporters, and single-cell metabolite measurements, we found that metabolism of budding yeast is a CDK-independent oscillator that oscillates across different growth conditions, both in synchrony with and also in the absence of the cell cycle. Using environmental perturbations and dynamic single-protein depletion experiments, we found that the metabolic oscillator and the cell cycle form a system of coupled oscillators, with the metabolic oscillator separately gating and maintaining synchrony with the early and late cell cycle. Establishing metabolism as a dynamic component within the cell-cycle network opens new avenues for cell-cycle research and therapeutic interventions for proliferative disorders.

Scalable inference of cell differentiation networks in gene therapy clonal tracking studies of haematopoiesis.

MOTIVATION: Investigating cell differentiation under a genetic disorder offers the potential for improving current gene therapy strategies. Clonal tracking provides a basis for mathematical modelling of population stem cell dynamics that sustain the blood cell formation, a process known as haematopoiesis. However, many clonal tracking protocols rely on a subset of cell types for the characterization of the stem cell output, and the data generated are subject to measurement errors and noise. RESULTS: We propose a stochastic framework to infer dynamic models of cell differentiation from clonal tracking data. A state-space formulation combines a stochastic quasi-reaction network, describing cell differentiation, with a Gaussian measurement model accounting for data errors and noise. We developed an inference algorithm based on an extended Kalman filter, a nonlinear optimization, and a Rauch-Tung-Striebel smoother. Simulations show that our proposed method outperforms the state-of-the-art and scales to complex structures of cell differentiations in terms of nodes size and network depth. The application of our method to five in vivo gene therapy studies reveals different dynamics of cell differentiation. Our tool can provide statistical support to biologists and clinicians to better understand cell differentiation and haematopoietic reconstitution after a gene therapy treatment. The equations of the state-space model can be modified to infer other dynamics besides cell differentiation. AVAILABILITY AND IMPLEMENTATION: The stochastic framework is implemented in the R package Karen which is available for download at https://cran.r-project.org/package=Karen. The code that supports the findings of this study is openly available at https://github.com/delcore-luca/CellDifferentiationNetworks.

A mixed-effects stochastic model reveals clonal dominance in gene therapy safety studies.

BACKGROUND: Mathematical models of haematopoiesis can provide insights on abnormal cell expansions (clonal dominance), and in turn can guide safety monitoring in gene therapy clinical applications. Clonal tracking is a recent high-throughput technology that can be used to quantify cells arising from a single haematopoietic stem cell ancestor after a gene therapy treatment. Thus, clonal tracking data can be used to calibrate the stochastic differential equations describing clonal population dynamics and hierarchical relationships in vivo. RESULTS: In this work we propose a random-effects stochastic framework that allows to investigate the presence of events of clonal dominance from high-dimensional clonal tracking data. Our framework is based on the combination between stochastic reaction networks and mixed-effects generalized linear models. Starting from the Kramers-Moyal approximated Master equation, the dynamics of cells duplication, death and differentiation at clonal level, can be described by a local linear approximation. The parameters of this formulation, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones and are not sufficient to describe situation in which clones exhibit heterogeneity in their fitness that can lead to clonal dominance. In order to overcome this limitation, we extend the base model by introducing random-effects for the clonal parameters. This extended formulation is calibrated to the clonal data using a tailor-made expectation-maximization algorithm. We also provide the companion  package RestoreNet, publicly available for download at https://cran.r-project.org/package=RestoreNet . CONCLUSIONS: Simulation studies show that our proposed method outperforms the state-of-the-art. The application of our method in two in-vivo studies unveils the dynamics of clonal dominance. Our tool can provide statistical support to biologists in gene therapy safety analyses.

Pig fetal skeletal muscle development is associated with genome-wide DNA hypomethylation and corresponding alterations in transcript and microRNA expression.

Fetal myogenesis represents a critical period of porcine skeletal muscle development and requires coordinated expression of thousands of genes. Epigenetic mechanisms, including DNA methylation, drive transcriptional regulation during development; however, these processes are understudied in developing porcine tissues. We performed bisulfite sequencing to assess DNA methylation in pig longissimus dorsi muscle at 41- and 70-days gestation (dg), as well as RNA- and small RNA-sequencing to identify coordinated changes in methylation and expression between myogenic stages. We identified 45 739 differentially methylated regions (DMRs) between stages, and the majority (N = 34 232) were hypomethylated at 70 versus 41 dg. Integration of methylation and transcriptomic data revealed strong associations between differential gene methylation and expression. Differential miRNA methylation was significantly negatively correlated with abundance, and dynamic expression of assayed miRNAs persisted postnatally. Motif analysis revealed significant enrichment of myogenic regulatory factor motifs among hypomethylated regions, suggesting that DNA hypomethylation may function to increase accessibility of muscle-specific transcription factors. We show that developmental DMRs are enriched for GWAS SNPs for muscle- and meat-related traits, demonstrating the potential for epigenetic processes to influence phenotypic diversity. Our results enhance understanding of DNA methylation dynamics of porcine myogenesis and reveal putative cis-regulatory elements governed by epigenetic processes.

Bistability in fatty-acid oxidation resulting from substrate inhibition.

In this study we demonstrated through analytic considerations and numerical studies that the mitochondrial fatty-acid ß-oxidation can exhibit bistable-hysteresis behavior. In an experimentally validated computational model we identified a specific region in the parameter space in which two distinct stable and one unstable steady state could be attained with different fluxes. The two stable states were referred to as low-flux (disease) and high-flux (healthy) state. By a modular kinetic approach we traced the origin and causes of the bistability back to the distributive kinetics and the conservation of CoA, in particular in the last rounds of the ß-oxidation. We then extended the model to investigate various interventions that may confer health benefits by activating the pathway, including (i) activation of the last enzyme MCKAT via its endogenous regulator p46-SHC protein, (ii) addition of a thioesterase (an acyl-CoA hydrolysing enzyme) as a safety valve, and (iii) concomitant activation of a number of upstream and downstream enzymes by short-chain fatty-acids (SCFA), metabolites that are produced from nutritional fibers in the gut. A high concentration of SCFAs, thioesterase activity, and inhibition of the p46Shc protein led to a disappearance of the bistability, leaving only the high-flux state. A better understanding of the switch behavior of the mitochondrial fatty-acid oxidation process between a low- and a high-flux state may lead to dietary and pharmacological intervention in the treatment or prevention of obesity and or non-alcoholic fatty-liver disease.

Increased microchimerism in peripheral blood of women with systemic lupus erythematosus: relation with pregnancy.

OBJECTIVES: We aimed to determine the presence, amount and origin of microchimerism in peripheral blood of pregnant and non-pregnant parous women with systemic lupus erythematosus (SLE) as compared to control subjects. METHODS: We performed a comparative study in which peripheral blood was drawn from eleven female non-pregnant SLE-patients and 22 control subjects, and from six pregnant SLE-patients and eleven control subjects during gestation and up to six months postpartum. Quantitative PCR for insertion-deletion polymorphisms and null alleles was used to detect microchimerism in peripheral blood mononuclear cells and granulocytes. RESULTS: Microchimerism was detected more often in non-pregnant SLE-patients than control subjects (54.4% vs. 13.6%, respectively; p=0.03). When present, the median total number of foetal chimeric cells was 5 gEq/106 in patients and 2.5gEq/106 in control subjects (p=0.048). Microchimerism was mostly foetal in origin; maternal microchimerism was detected in one patient and one control subject. In control subjects, microchimerism was always derived from only one source whereas in 50% of patients it originated from multiple sources. The pregnant patients had a significantly higher median number of foetal chimeric cells in the granulocyte fraction just after delivery than control subjects (7.5 gEq/106 vs. 0 gEq/106, respectively; p=0.02). CONCLUSIONS: Just after delivery, SLE-patients had more microchimerism than control subjects. Three months post-partum, microchimerism was no longer detectable, only to reappear many years after the last pregnancy, more often and at higher levels in SLE-patients than in control subjects. This suggests that these chimeric cells may originate from non-circulating foetal chimeric stem cells.

Sparse relative risk regression models.

Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and a (near) non-convex regularizer. The disadvantages of such methods are that they are typically non-invariant to scale changes of the covariates, they struggle with highly correlated covariates, and they have a practical problem of determining the amount of regularization. In this article, we propose an extension of the differential geometric least angle regression method for sparse inference in relative risk regression models. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/dgcox).

De novo construction of polyploid linkage maps using discrete graphical models.

MOTIVATION: Linkage maps are used to identify the location of genes responsible for traits and diseases. New sequencing techniques have created opportunities to substantially increase the density of genetic markers. Such revolutionary advances in technology have given rise to new challenges, such as creating high-density linkage maps. Current multiple testing approaches based on pairwise recombination fractions are underpowered in the high-dimensional setting and do not extend easily to polyploid species. To remedy these issues, we propose to construct linkage maps using graphical models either via a sparse Gaussian copula or a non-paranormal skeptic approach. RESULTS: We determine linkage groups, typically chromosomes, and the order of markers in each linkage group by inferring the conditional independence relationships among large numbers of markers in the genome. Through simulations, we illustrate the utility of our map construction method and compare its performance with other available methods, both when the data are clean and contain no missing observations and when data contain genotyping errors. Our comprehensive map construction method makes full use of the dosage SNP data to reconstruct linkage map for any bi-parental diploid and polyploid species. We apply the proposed method to three genotype datasets: barley, peanut and potato from diploid and polyploid populations. AVAILABILITY AND IMPLEMENTATION: The method is implemented in the R package netgwas which is freely available at https://cran.r-project.org/web/packages/netgwas. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MeCP2-regulated miRNAs control early human neurogenesis through differential effects on ERK and AKT signaling.

Rett syndrome (RTT) is an X-linked, neurodevelopmental disorder caused primarily by mutations in the methyl-CpG-binding protein 2 (MECP2) gene, which encodes a multifunctional epigenetic regulator with known links to a wide spectrum of neuropsychiatric disorders. Although postnatal functions of MeCP2 have been thoroughly investigated, its role in prenatal brain development remains poorly understood. Given the well-established importance of microRNAs (miRNAs) in neurogenesis, we employed isogenic human RTT patient-derived induced pluripotent stem cell (iPSC) and MeCP2 short hairpin RNA knockdown approaches to identify novel MeCP2-regulated miRNAs enriched during early human neuronal development. Focusing on the most dysregulated miRNAs, we found miR-199 and miR-214 to be increased during early brain development and to differentially regulate extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase and protein kinase B (PKB/AKT) signaling. In parallel, we characterized the effects on human neurogenesis and neuronal differentiation brought about by MeCP2 deficiency using both monolayer and three-dimensional (cerebral organoid) patient-derived and MeCP2-deficient neuronal culture models. Inhibiting miR-199 or miR-214 expression in iPSC-derived neural progenitors deficient in MeCP2 restored AKT and ERK activation, respectively, and ameliorated the observed alterations in neuronal differentiation. Moreover, overexpression of miR-199 or miR-214 in the wild-type mouse embryonic brains was sufficient to disturb neurogenesis and neuronal migration in a similar manner to Mecp2 knockdown. Taken together, our data support a novel miRNA-mediated pathway downstream of MeCP2 that influences neurogenesis via interactions with central molecular hubs linked to autism spectrum disorders.

Antibiotic Treatment in the First Week of Life Impacts the Growth Trajectory in the First Year of Life in Term Infants.

OBJECTIVE: Antibiotic treatment in early life appears to increase the risk for childhood overweight and obesity. So far, the association between antibiotics administrated specifically during the first week of life and growth has not been studied. Therefore, we studied the association between growth and antibiotics, given in the first week of life and antibiotic courses later in the first year of life. METHOD: A prospective observational birth cohort of 436 term infants with 151 receiving broad-spectrum antibiotics for suspected neonatal infection (AB+), and 285 healthy controls (AB-) was followed during their first year. Weight, height, and additional antibiotic courses were collected monthly. A generalized-additive-mixed-effects model was used to fit the growth data. Growth curve estimation was controlled for differences in sex, gestational age, delivery mode, exclusive breast-feeding, tobacco exposure, presence of siblings, and additional antibiotic courses. RESULTS: Weight-for-age and length-for-age increase was lower in AB+ compared with AB- (P < 0.0001), resulting in a lower weight and length increase 6.26 kg (standard error [SE] 0.07 kg) and 25.4 cm (SE 0.27 cm) versus 6.47 kg (SE 0.06 kg) and 26.4 cm (SE 0.21 cm) (P < 0.05 and P < 0.005, respectively) in the first year of life. Approximately 30% of the children in both groups received additional antibiotic course(s) in their first year, whereafter additional weight gain of 76 g per course was observed (P = 0.0285). CONCLUSIONS: Decreased growth was observed after antibiotics in the first week of life, whereas increased growth was observed after later antibiotic course(s) in term born infants in the first year of life. Therefore, timing of antibiotics may determine the association with growth.

Using dust shed from asteroids as microsamples to link remote measurements with meteorite classes.

Given the compositional diversity of asteroids, and their distribution in space, it is impossible to consider returning samples from each one to establish their origin. However, the velocity and molecular composition of primary minerals, hydrated silicates, and organic materials can be determined by in situ dust detector instruments. Such instruments could sample the cloud of micrometer-scale particles shed by asteroids to provide direct links to known meteorite groups without returning the samples to terrestrial laboratories. We extend models of the measured lunar dust cloud from LADEE to show that the abundance of detectable impact-generated microsamples around asteroids is a function of the parent body radius, heliocentric distance, flyby distance, and speed. We use Monte Carlo modeling to show that several tens to hundreds of particles, if randomly ejected and detected during a flyby, would be a sufficient number to classify the parent body as an ordinary chondrite, basaltic achondrite, or other class of meteorite. Encountering and measuring microsamples shed from near-Earth and Main Belt asteroids, coupled with complementary imaging and multispectral measurements, could accomplish a thorough characterization of small, airless bodies.

Variance Components Models for Analysis of Big Family Data of Health Outcomes in the Lifelines Cohort Study.

Large multigenerational cohort studies offer powerful ways to study the hereditary effects on various health outcomes. However, accounting for complex kinship relations in big data structures can be methodologically challenging. The traditional kinship model is computationally infeasible when considering thousands of individuals. In this article, we propose a computationally efficient alternative that employs fractional relatedness of family members through a series of founding members. The primary goal of this study is to investigate whether the effect of determinants on health outcome variables differs with and without accounting for family structure. We compare a fixed-effects model without familial effects with several variance components models that account for heritability and shared environment structure. Our secondary goal is to apply the fractional relatedness model in a realistic setting. Lifelines is a three-generation cohort study investigating the biological, behavioral, and environmental determinants of healthy aging. We analyzed a sample of 89,353 participants from 32,452 reconstructed families. Our primary conclusion is that the effect of determinants on health outcome variables does not differ with and without accounting for family structure. However, accounting for family structure through fractional relatedness allows for estimating heritability in a computationally efficient way, showing some interesting differences between physical and mental quality of life heritability. We have shown through simulations that the proposed fractional relatedness model performs better than the standard kinship model, not only in terms of computational time and convenience of fitting using standard functions in R, but also in terms of bias of heritability estimates and coverage.

Characterization of efficacy and safety of pathogen inactivated and quarantine plasma in routine use for treatment of acquired immune thrombotic thrombocytopenic purpura.

BACKGROUND: Auto-immune thrombotic thrombocytopenic purpura (TTP) is a morbid multi-organ disorder. Cardiac involvement not recognized in initial disease descriptions is a major cause of morbidity. Therapeutic plasma exchange (TPE) requires exposure to multiple plasma donors with risk of transfusion-transmitted infection (TTI). Pathogen inactivation (PI) with amotosalen-UVA, the INTERCEPT Blood System for Plasma (IBSP) is licensed to reduce TTI risk. METHODS: An open-label, retrospective study evaluated the efficacy of quarantine plasma (QP) and IBSP in TTP and defined treatment emergent cardiac abnormalities. Medical record review of sequential patient cohorts treated with QP and IBSP characterized efficacy by remission at 30 and 60 days (d) of treatment, time to remission, and volume (L/kg) of plasma required. Safety outcomes focused on cardiac adverse events (AE), relapse rates, and mortality. RESULTS: Thirty-one patients (18 IBSP and 13 QP) met study criteria for auto-immune TTP. The proportions (%) of patients in remission at 30 d (IBSP = 61·1, QP = 46·2, P = 0·570) and 60 d (IBSP = 77·8, QP = 76·9, P = 1·00) were not different. Median days to remission were less for IBSP (15·0 vs. 24·0, P = 0·003). Relapse rates (%) 60 d after remission were not different between cohorts (IBSP = 7·1, QP = 40·0, P = 0·150). ECG abnormalities before and during TPE were frequent; however, cardiac AE and mortality were not different between treatment cohorts. CONCLUSIONS: Cardiac and a spectrum of ECG findings are common in TTP. In this study, IBSP and QP had similar therapeutic profiles for TPE.

Genome-wide association analyses of lesion counts in group-housed pigs.

Aggression in group-housed pigs is a welfare concern and can negatively affect production. Skin lesions are reliable indicators of aggression and are moderately heritable, suggesting that selective breeding may reduce aggression. To further understand the genetic control of behavioral traits, such as the aggressive response to regrouping, associated single nucleotide polymorphisms (SNPs) can be identified within the genome, and the region in which these SNPs are located can be related to known genes. To investigate SNPs associated with aggression, 1093 purebred Yorkshire pigs were strategically remixed into new groups of familiar and unfamiliar animals at three life stages and lesion counts were recorded. Genomic best linear unbiased prediction (GBLUP) models were fitted for each trait. The genetic additive effect was obtained from a genetic relationship matrix constructed from the 50 924 SNPs. SNP effects and their variances were estimated from the GBLUP objects. SNPs that were associated with a significant portion of the trait variance were identified for lesions to the anterior (three SNPs, FDR <5%) and central (one SNP, FDR <5%) portions of the body in grow-finish pigs. These SNPs were located on chromosome 11, suggesting that chromosome 11 contains a region explaining variation in lesion counts that should be further explored to identify genes underlying biological control of aggression.

Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order - some entries of the precision matrix are a priori zeros - or equal dependency strengths across time lags - some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.

Calorie restriction does not elicit a robust extension of replicative lifespan in Saccharomyces cerevisiae.

Calorie restriction (CR) is often described as the most robust manner to extend lifespan in a large variety of organisms. Hence, considerable research effort is directed toward understanding the mechanisms underlying CR, especially in the yeast Saccharomyces cerevisiae. However, the effect of CR on lifespan has never been systematically reviewed in this organism. Here, we performed a meta-analysis of replicative lifespan (RLS) data published in more than 40 different papers. Our analysis revealed that there is significant variation in the reported RLS data, which appears to be mainly due to the low number of cells analyzed per experiment. Furthermore, we found that the RLS measured at 2% (wt/vol) glucose in CR experiments is partly biased toward shorter lifespans compared with identical lifespan measurements from other studies. Excluding the 2% (wt/vol) glucose experiments from CR experiments, we determined that the average RLS of the yeast strains BY4741 and BY4742 is 25.9 buds at 2% (wt/vol) glucose and 30.2 buds under CR conditions. RLS measurements with a microfluidic dissection platform produced identical RLS data at 2% (wt/vol) glucose. However, CR conditions did not induce lifespan extension. As we excluded obvious methodological differences, such as temperature and medium, as causes, we conclude that subtle method-specific factors are crucial to induce lifespan extension under CR conditions in S. cerevisiae.

The potential of eHealth in otorhinolaryngology-head and neck surgery: patients' perspectives.

The use of modern information and communication technologies (ICT) in daily life has significantly increased during the last several years. These essential online technologies have also found their way into the healthcare system. The use of modern ICT for health reasons can be summarized by the term 'eHealth'. Despite the potential importance of eHealth in the field of otorhinolaryngology (ORL), there is little understanding of patients' attitudes towards the deeper integration of these technologies into intersectoral care. The aim of this study was to gain a better understanding of patients' attitudes towards the use of modern ICT for intersectoral communication and information transfer in the field of ORL. Therefore, a structured interview was developed by an interdisciplinary team of otorhinolaryngologists, public health researchers, and information technology (IT) specialists. Overall, 211 ORL patients were interviewed at the Department of Otorhinolaryngology-Head and Neck Surgery, Tuebingen University Hospital, Germany, and 203 of these patients completed the interview. This study revealed ORL patients' perspectives on the potential of eHealth, especially for appointment scheduling, appointment reminders, and intersectoral communication of personal medical information. Furthermore, this study provides evidence that data security and the impacts of eHealth on the physician-patient relationship and on treatment quality warrant special attention in future research.

Estimates of the actual relationship between half-sibs in a pig population.

Genomic relationships based on markers capture the actual instead of the expected (based on pedigree) proportion of genome shared identical by descent (IBD). Several methods exist to estimate genomic relationships. In this research, we compare four such methods that were tested looking at the empirical distribution of the estimated relationships across 6704 pairs of half-sibs from a cross-bred pig population. The first method based on multiple marker linkage analysis displayed a mean and standard deviation (SD) in close agreement with the expected ones and was robust to changes in the minor allele frequencies (MAF). A single marker method that accounts for linkage disequilibrium (LD) and inbreeding came second, showing more sensitivity to changes in the MAF. Another single marker method that considers neither inbreeding nor LD showed the smallest empirical SD and was the most sensible to changes in MAF. A higher mean and SD were displayed by VanRaden's method, which was not sensitive to changes in MAF. Therefore, the method based on multiple marker linkage analysis and the single marker method that considers LD and inbreeding performed closer to theoretical values and were consistent with the estimates reported in literature for human half-sibs.

Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data.

Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of next-generation sequencing (NGS) experiments, for example, the signal observed in the data might be produced by two (or more) different biological processes operating together and a gene could participate in both (or all) of them. We propose a novel approach to cluster NGS discrete data, coming from a ChIP-Seq experiment, with a mixture model, allowing each unit to belong potentially to more than one group: these multiple allocation clusters can be flexibly defined via a function combining the features of the original groups without introducing new parameters. The formulation naturally gives rise to a 'zero-inflation group' in which values close to zero can be allocated, acting as a correction for the abundance of zeros that manifest in this type of data. We take into account the spatial dependency between observations, which is described through a latent conditional autoregressive process that can reflect different dependency patterns. We assess the performance of our model within a simulation environment and then we apply it to ChIP-seq real data.

NEAT: an efficient network enrichment analysis test.

BACKGROUND: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. RESULTS: We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. CONCLUSIONS: NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).