A number of studies have established that stochasticity in gene expression may play an important role in many biological phenomena. This therefore calls for further investigations to identify the molecular mechanisms at stake, in order to understand and manipulate cell-to-cell variability. In this work, we explored the role played by chromatin dynamics in the regulation of stochastic gene expression in higher eukaryotic cells.For this purpose, we generated isogenic chicken-cell populations expressing a fluorescent reporter integrated in one copy per clone. Although the clones differed only in the genetic locus at which the reporter was inserted, they showed markedly different fluorescence distributions, revealing different levels of stochastic gene expression. Use of chromatin-modifying agents showed that direct manipulation of chromatin dynamics had a marked effect on the extent of stochastic gene expression. To better understand the molecular mechanism involved in these phenomena, we fitted these data to a two-state model describing the opening/closing process of the chromatin. We found that the differences between clones seemed to be due mainly to the duration of the closed state, and that the agents we used mainly seem to act on the opening probability.In this study, we report biological experiments combined with computational modeling, highlighting the importance of chromatin dynamics in stochastic gene expression. This work sheds a new light on the mechanisms of gene expression in higher eukaryotic cells, and argues in favor of relatively slow dynamics with long (hours to days) periods of quiet state. Although the importance of stochasticity in gene expression has been anticipated more than three decades ago -3, the eDrosophila and mammals) is a well-characterized example of the influence of chromatin on gene expression; with a stably integrated transgene, a progressive silencing of the reporter occurs, at a rate that strongly depends on the integration site [Many studies have shown that the average expression level of a gene depends strongly on its genomic location -25. In cion site . Severalion site ) and wition site ) have shion site ,29. Almoion site -35 or hiion site -39.et al., who in 2005 showed the existence of genomic domains in the yeast genome, which produce a low transcriptional noise  [et al. identified the influence of genomic domain on transcriptional noise, suggesting the importance of the switching rate between chromatin states via remodeling. Gene activation or inactivation would occur in cases of chromatin decondensation or condensation, respectively [Initially conducted in prokaryotes ,40, expeduction) . The folectively . To analectively . Howeverectively ,36,42,44In many situations, from prokaryotes to eukaryotes, simple mathematical models describing the transcriptional dynamics as a two-state process have been shown to account effectively for the stochastic expression of a gene ,46. Indeet al. monitored transcription at high temporal resolution in single mammalian cells, and identified bursts of transcription, a mechanism previously suggested in prokaryotes and eukaryotes [et al. performed a complementary analysis of transcriptional bursting in single mammalian cells [et al. concluded that chromatin remodeling may play an important role in the timing of transcriptional bursting. Finally, based on time-lapse fluorescence microscopy experiments, coupled with the use of the two-state model, Dar et al. gave a recent comprehensive study on noise in mammalian cells [Recently, using a short-lived luciferase protein, Suter karyotes ,36. Usinkaryotes . Harper an cells . By quanan cells . In theiIn a preliminary study, our group showed, using isogenic cell populations expressing a fluorescent reporter, that modification of chromatin marks, using chromatin-modifying agents such as 5-azacytidine (5-AzaC) and trichostatin A (TSA), induced significant effects on mean fluorescence intensity (MFI) and normalized variance  . We alsoTo assess the possible influence of chromatin-opening/closing dynamics on the stochasticity of gene expression, the next step was to combine biological experiments with a modeling analysis. For that purpose, we generated a series of clonal isogenic cell populations from chicken erythrocyte progenitors (6C2 cells). These populations were stably transfected with a unique copy of a reporter-gene coding for the red fluorescent protein mCherry, but the reporter was inserted at different chromosomal positions in each clone Figure , left. UOur current study supports the view that expression dynamics is strongly driven by short and infrequent transcriptional bursts, as previously described in other models, including mammalian models. However, the major advance of this work is that, whereas the duration and intensity of bursts did not show strong clone-to-clone differences, the time between bursts was found to depend strongly on genomic location and was broadly affected by drug treatments that affect chromatin. Hence, the position-dependent opening dynamics of chromatin emerges as a key determinant of the stochasticity in gene expression.mCherry reporter, driven by the cytomegalovirus (CMV) promoter, then using splinkerette PCR [We generated a series of clones stably transfected with the ette PCR , we retaette PCR , the cloAn explanation of these observations comes from a previous preliminary study, in which we investigated whether chromatin dynamics are involved in these observed differences . Using tThus, for this purpose in the current study, we fitted these data to a two-state model of gene expression, and evaluated to what extent chromatin dynamics act on stochastic gene expression. Under the assumption that all parameters but those describing the dynamics of chromatin would be identical in all the clones, we performed an iterative screening of model parameters. This allowed us to find these common parameters, and to characterize the position-specific dynamics of chromatin for each individual clone Figure .The choice of the model used to analyze our biological data was crucial. Two models are classically used to describe transcriptional stochasticity: 1) a Poisson model, in which the gene has, at each instant, a given chance to produce an mRNA, ,47,57 anon kand off k, respectively describing the rates of chromatin opening and closing, ρ and γ, describing the transcription and translation rates, α, representing the fluorescence intensity of a single mCherry protein in the arbitrary unit measured by the flow cytometer. In order to fit the model, the optimal set of parameters must be identified, under the assumption that ρ, γ, α are identical in every clone, but that on kand off k are clone-specific. From this point, we refer to the five former parameters as the 'transcription-translation parameters' and to the two latter ones as the 'chromatin-dynamics parameters'. Because we had six clones, we actually had to determine 17 parameters ((6 × 2) + 5) in order to fully specify the model and to ultimately estimate the chromatin-dynamics parameters for each clone. For these 17 parameters, the two degradation rates  and -4/min . The sensitivity of our results with regard to uncertainty in these experimentally determined values will be discussed later. These values are consistent with average mRNA and protein half-lives previously measured in mammalian cells  [Because flow cytometry quantifies protein fluorescence, the model must describe the expression process up to the protein level (including mRNA and protein production and degradation rates) and requires an additional parameter to convert protein quantity into fluorescence intensity Figure , right. ers (6 ×  + 5) in ctively) . FollowiSeveral methods can be used to find such a parameter set. In particular, there are various optimization methods available, such as simulated annealing. However, because the model-experiment comparisons in our study involved stochastic simulations, the objective functions that have to be minimized  are only estimated up to a certain error level. Although small, this error level makes most optimization algorithms inadequate. Indeed, these algorithms rely on estimating the gradient or Hessian of the objective function, based on a finite difference procedure . In a context where successive estimations of the objective function, even for the same parameters, may display random variations, these optimization algorithms are clearly doomed to failure. Overcoming this issue would require both running extremely long and computationally intensive simulations to minimize the error, and using coarse variation steps in the gradient-estimation procedure, which could result in numerical instabilities during the optimization.For this reason, we decided to conduct a systematic parametric exploration, as this is a procedure that does not require local smoothness of the objective function. In addition, a single evaluation of the objective function represents a heavy computation load; for example, involving thousands of realizations of a Gillespie simulation that are followed over long periods of simulated time (see Methods). In this context, a systematic parametric exploration allows massive parallelization of the computations on a grid. The sequential evaluation imposed by optimization algorithms makes this approach prohibitive. However, because the systematic exploration still requires intensive computations, we used iterative screening of the model parameters to progressively reduce the parameter space that has to be simulated.This iterative screening was based on three steps in which we successively used analytical derivations on the model (step 1), additional experimental data (step 2), and finally, stochastic simulation (step 3). Thanks to these successive screenings, we were able to reduce by a factor of 30 the number of parameter sets to be simulated, thus making the problem computationally tractable. In the following sections, we describe the three screening steps and the results we obtained from them.onk, off k, ρ, γ, α. By inverting these equations (see Methods), we were able to compute the chromatin-dynamics parameters (on kand offk) for each clone from: 1) the experimentally measured MFI and NV of the clone, 2), the experimentally determined values of ρ, γ and α). Thus, only three transcription-translation parameters remained to be determined, making their combinatorial exploration computationally tractable.Mathematical derivations by Paulsson from the two-state model  analyticρ , 15 values for γ , and 12 values for α (from 0.1 to 200 fluorescence units per protein) [ρ, γ, and α), we computed on kand off kfor each of the six clones from their experimental values of MFI and NV. Of the 3,600 initial parameter sets, only 1,087 led to valid solutions, with the others leading to negative values for on kor off kfor at least one clone. Figure onk; offk) that resulted from this exploration for all the clones. It was found that, although the chromatin-dynamics parameters could be the same order of magnitude, the mean open time  was markedly shorter than the mean closed time . This is characteristic of a transcriptional activity in which mRNA production events occur in brief bursts separated by longer silent periods.We explored wide ranges of these parameters that included all biologically relevant values : 20 valuprotein)  , corresponding to the transcription-translation sets that were kept. All retained cases had in common that the mean open time 1/off kwas very short compared with any other timescale in the model , reducing again by 1 the number of fitting parameters.Based on these additional data, we could then exclude all transcription-translation parameter sets that did not account for the observed increase in expression levels even if the chromatin was considered as constantly open (see Methods). It is important to emphasize that we made the assumption that the TSA and 5-AzaC treatments affected only the chromatin-dynamics parameters. Using this strategy, we were able to reject 86% of the parameter sets, thus we kept only 114 transcription-translation parameter sets for further analyses. Figure  studies , we couly number . Becauseoff k; 1/onk) couples retained after the second screening , as shown in Figure ρ/off kfor the six clones . By contrast, the mean closed time of chromatin seemed to be highly clone-dependent, ranging from 6 to 12 hours for clone C1 and C17 to more than 2 days for C5 and C7  , to simuAnalyzing the comparison scores (distances) from the Kolmogorov-Smirnov test of the 114 parameter sets, we were able to identify the subsets of parameters, and therefore the corresponding chromatin dynamics, that were the best fit to the distributions measured by the flow cytometer Figure . Note thmCherry reporter. It seems clear that the transcriptional activity of the reporter can vary from frequent bursts (C1) to rare bursts (C5), depending on the chromatin context. These important differences could very well be the dependence of the local chromatin properties at the reporter insertion site. Finally, the mRNA transcription rates and mRNA copies per cell we defined for the six clones (on average 2.1 and 21 respectively) . During TSA treatment, the distributions of fluorescence, reflecting the expression of the s Figure , 6A. Accs Figure . We founs Figure . By conts Figure . For theThe evolution of the comparison score between the measured and simulated data Figure , insets To illustrate the consequences of the new chromatin dynamics on the transcription and translation induced by the TSA treatment, Figure The importance of stochasticity of gene expression in many key cellular activities was appreciated many decades ago, and is now supported by strong experimental evidence .Analyzing stochastic expression of a stably integrated fluorescent reporter in six isogenic cell populations, differing only in their reporter integration site, this study provides new evidence suggesting that the local chromatin environment (reporter insertion site) influences stochastic gene expression. Our results are in agreement with previous studies on HIV gene expression, where it was shown that the existence of different fates for infected cells correlated with the virus-integration sites ,50, and Using a two-state model, we found that the observed NVs and MFIs for each clone alone are not sufficient to identify efficiently, for a specific chromatin environment, a restricted set of parameters that best explain the observed differences between the six clones. We thus used a more complex strategy exploiting the full distribution of fluorescence as measured by flow cytometry. By mixing analytical models, complementary experiments, and stochastic simulations, we progressively identified the parameters that best fit the flow-cytometry distributions. The final set of parameters we obtained was able to reproduce accurately the experimental data for all clones except the unique bi-modal one, C7, for which the simulated distribution fit only the high modality. This bi-modal distribution observed for clone C7 could be due to: 1) specific chromatin dynamics related to the genomic insertion site of the reporter, or 2) a genetic mutation event affecting the reporter-gene integrity and resulting in two genetically distinct subpopulations. In the first case, if the transition rates between active and inactive states are extremely slow relative to transcript and protein degradations, each promoter state would be relatively stable, and this transcription regime could result in bi-modal protein expression -6. Howevet al., the authors showed that the genomic-integration site influences burst kinetics, with a the promoter type having a marginal influence [After selection of the best parameter sets and characterization of the chromatin dynamics for each clone, our work provided elements suggesting that the chromatin state is essentially dominated by the closed state, as previously shown , but mosnfluence . Understoffk) and transcription rate (ρ) separately. Note, however, that having half-lives that are too short could impair the ability to probe long timescales, such as the time between bursts. In addition, resolving experimentally the full distribution of chromatin open/closed times  is only possible with single-cell time-lapse experiments [The results presented here also show how, using a two-state model and fluorescence distributions measured by flow cytometry, possible chromatin-dynamics parameters can be identified. In this study, the filtering of promoter activity by mRNA and protein dynamics allows inference of temporal information from a steady-state measurement . In this regard, the mRNA and protein half-lives are the components that define the range of timescales that can be assessed from the experiment. Using destabilized reporters -53,70 woeriments ,52.Finally, using our mathematical model, we simulated a situation in which the chromatin dynamics were directly modified by TSA. As expected, TSA treatment activated the mean reporter-gene expression ,29,72 anEscherichia coli, using a somewhat different strategy, identified that the off kparameter  was the main parameter used by the bacterium for gene upregulation [et al., in which the authors proposed a method to discriminate between mRNA birth/death and promoter fluctuations as intrinsic sources of noise [Our work suggests that the probability of chromatin entering an open state is a key determinant of gene expression in our system. A recent study in gulation , which igulation ,75, elongulation , the comgulation , presencgulation , and othgulation . Solutioof noise , coupledIn this study, we highlight the importance of the dynamics of chromatin in the control of cell-to-cell variability. Our results suggest that long periods of 'off' time (during which transcription does not occur) followed by brief period of 'open' times  can best explain the observed difference between clones in terms of stochastic gene expression. This paves the way for future studies exploring the role of chromatin dynamics at a more local scale.AEV) [6 cells per ml.All experiments were performed on 6C2 cells, a chicken erythroblast cell line transformed by the avian erythroblastosis virus (AEV) ,80. CellmCherry/pCAGGS-T2TP plasmid mix (ratio 5/1). The pT2.CMV-mCherry plasmid was constructed using the same strategy as described for the pT2.CMV-hKO plasmid [hKO reporter gene was replaced by mCherry, extracted from the pRSET-B plasmid . mRNA birth/death fluctuations constitute a major source of stochasticity in gene expression because many mRNA species are present at very low molecular counts within cells [mCherry sequence, flanked by Tol2 motifs, is recognized by a transposase (pCAGGS-T2TP), and randomly inserted into 6C2 genomic DNA. Seven days after transfection, stably transfected cells expressing the reporter gene were sorted and individually cloned in U-shaped 96-well microplates  using a cytometer .Stably transfected clones, expressing a fluorescent reporter, were obtained as previously described . Briefly plasmid , except in cells ,70,82,83in cells ; the CMVTaiI, a restriction enzyme with a 4 bp recognition site. The digested DNA was then ligated to a splinkerette adaptor for 1 hour at 22°C. After purification of the ligated product, two rounds of PCR (PCR1 and nested PCR2) were performed using primers specific for the reporter transgene mCherry and for the annealed splinkerette adaptor, and a commercial polymerase . The PCR products were then purified and sequenced. Finally, the genomic reporter insertion sites were identified by similarity searches using the sequence analysis tool iMapper [For each clone, the genomic reporter insertion sites were identified using a splinkerette PCR method as previously described , in orde iMapper . The ide iMapper , allowinFor characterization of clones and analysis of treatment effects (see below), flow-cytometry analyses were performed  on cells extemporaneously pelleted and resuspended in Dulbecco's phosphate-buffered saline 1× solution (Gibco-BRL). Each sample was analyzed using an acquisition of 50,000 events (gated on living cells), and the positive fluorescence threshold was fixed using non-transfected cells. Possible variability resulting from flow-cytometer calibration was taken into account by systematically analyzing flow-calibration particles , as a calibration reference.Non-transfected cells were used to measure 6C2 native autofluorescence, and the difference between the fluorescence of transfected and non-transfected ones was used as an indicator of the transgene activity .For each clone, two indicators were systematically used: MFI (mean fluorescence intensity) and NV (the variance divided by the square mean).f (from the flow cytometer) is f = t f+ af; that is, the sum of the true fluorescence t f (coming from the reporter proteins) and the autofluorescence a f(coming from the rest of the cell). The autofluorescence is not a constant, but has a distribution that is obtained using non-transfected cells. The two first moments of f read simply asFor a given cell, the measured fluorescence andHence, with MFI and NV being the mean and normalized variance of the true fluorescence, we get:andFinally, to compare the theoretical distributions obtained from simulations (which only included the reporter fluorescence) with those obtained from experiments , the model's output was first combined with the experimental autofluorescence. This was carried out by summing each simulation result with the value of a randomly selected cell from the autofluorescence distribution. The resulting distribution was the convolution between the theoretical and the autofluorescence distributions, and was then compared with the experimental distributions using a Kolmogorov-Smirnov test.mCherry mRNA degradation rate, the mRNA concentration was estimated using quantitative reverse transcription (qRT)-PCR after transcription inactivation was achieved using actinomycin D treatment. Two clones (C5 and C11) were treated, in duplicate, for 0, 60, 124, 244 and 488 minutes with a final concentration of 10 µg/ml actinomycin D , before extracting the mRNA after the instructions of RNeasy® Plus Mini Kit . To prepare the real-time PCR assay, 1 µg of total RNA from each sample was reversed transcribed using the SuperScript™ III First-Strand Synthesis System for RT-PCR (Invitrogen Inc.) in the presence of random hexamers. Quantification of mRNA levels by real-time PCR was performed in 96-well plates using a real-time PCR system . The measurement was performed in a final volume of 10 µl of reaction mixture (containing 2.5 µl of cDNA template diluted 1 in 5), prepared using a commercial kit  in accordance with the manufacturer's instructions, and with the primer set at a final concentration of 0.5 µmol/l . An internal standard curve was generated using serial dilutions (from 2000 to 0.02 fg/µl) of purified PCR product. The reactions were initiated by activation of Taq DNA polymerase at 95°C for 5 minutes, followed by 45 three-step amplification cycles consisting of denaturation at 95°C for 15 seconds, annealing at 55°C for 15 seconds, and extension at 72°C for 15 seconds. The fluorescence signal was measured at the end of each extension step. After the amplification, a dissociation stage was run to generate a melting curve for verification of amplification-product specificity. The crossing point (CP) was determined by the second derivative maximum method in the LightCycler® 480 software (version 1.5.0). After normalization, taking into account cellular viability and mRNA quantity used for the retrotranscription step, the mRNA half-life was determined by fitting mRNA quantity evolution by a decreased exponential (least square) method.To determine the To determine the mCherry protein degradation rate, we used flow cytometry to measure the protein half-life after translation inactivation using cycloheximide treatment. C5 and C11 clones were treated in duplicate for 0, 16, and 24 hours with a final concentration of 100 µg/µl cycloheximide , and for each time point, the fluorescence of the treated cells was measured by flow cytometry. The autofluorescence component was removed as explained earlier. The protein half-life was determined using exponential fit of the fluorescence mean decrease curve, similarly to the procedure used for determining the mRNA half-life.6 cells  or 5 × 105 cells (for 32 and 48 hours) were treated with the relevant drug and characterized by flow cytometry.To analyze the effect of chromatin state on the stochasticity of gene expression, clones were treated with TSA, a histone deacetylase inhibitor  and 5-AzaC, an inhibitor of DNA methylation . For each clone, kinetic treatment experiments were performed; clones were treated with 500 nmol/l TSA or 500 µmol/l 5-AzaC at five time points . For each time point, 1 × 10on kand off k(respectively representing the 'off-on' transition and the 'on-off' transition). To enable comparison with the experimental data, a simple model of mRNA and protein dynamics based on two production/degradation models completed the model. The production of mRNA was allowed only in the 'on' state (open chromatin) but completely forbidden in the 'off' state (closed chromatin). The model thus corresponds to the following equations:The two-state model of gene expression represents the chromatin activity as an 'on-off' process specified through the transition rates on ,kis the closed-to-open transition rate, off kis the open-to-closed transition rate, and T kis the resulting proportion of the 'on' state; R is the number of mRNAs, ρ is the mRNA production rate (when chromatin is open), and P is the number of mCherry proteins, γ is the mCherry production rate (per mRNA) and f is the fluorescence intensity of the cell (after subtraction of the autofluorescence) and α, is a linear proportionality coefficient to convert the number of proteins into arbitrary fluorescence measures.whereThis model can be simulated with the SSA (see below) to ascertain the behavior of single cells and eventually to compute the fluorescence distributions. It can also be analytically derived to compute the MFI and NV of large cell populations at steady state.α, Paulsson's equation gives:Paulsson proposed an analytic expression of the mean quantity and NV of protein in the two-state model, as a function of chromatin-dynamics parameters and transcription-translation parameters . In the on kand off k as a function of MFI, NV, and the transcription-translation parameter sets. Rewriting the equation gives:This equation can be used to express ρ, γ, α.ρ, γ and α remained unknown. We explored a wide range of these parameters : ρ = 6.0, 1.0, 0.5, 0.333, 0.25, 0.200, 0.1666, 0.14286, 0.125, 0.111, 0.100, 0.0500, 0.0333, 0.0250, 0.02, 0.01666, 0.01333, 0.0111, 0.00952, and 0.00833 mRNA/min, corresponding to one mRNA produced each 1/ρ = 10 seconds, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, and 50 minutes, and 1, 1.25, 1.5, 1.75, and 2 hours, when chromatin is in the open state; γ = 1.0, 0.200, 0.100, 0.0333, 0.01667, 0.006667, 0.00333, 0.00222, 0.001666, 0.001333, 0.00111, 0.0008333, 0.00069444, 0.00046296, and 0.0003472 protein/min/mRNA, corresponding to a protein produced each 1/γ = 1, 5, 10, and 30 minutes, 1, 2.5, 5, 7.5, 10, 12.5, 15, and 20 hours, and 1, 1.5 and 2 days per mRNA molecule; and α = 0.10, 0.15, 0.50, 1.0, 1.5, 5.0, 10.0, 15.0, 50.0, 100.0, 150.0, and 200.0 arbitrary units.Because the clonal populations differed only in their insertion points , equation 3 enabled us to find the clone-specific parameters from MFI and NV (measured by flow cytometry) and the transcription-translation parameters ρ, γ and α gave us 3,600 couples , of which only 1,047 respected the condition mentioned in equation 3 (onk>0).Exploring all values of R) and the mean protein number (P) at steady state, from the values of the chromatin-dynamics and transcription-translation parameters:Equation 1 enabled us to compute the mean mRNA number , compute R(t), (the evolution of mRNA number), and P(t), (the evolution of protein number following the TSA treatment) can be computed. If Then, assuming that at The exact values of Note that this equation represents an extreme situation, not the exact TSA influence on chromatin.Introducing equation 6 into the dynamics of equation 5, we were able to compute, for a given transcription-translation parameter set, the maximum rate of protein concentration increase, and thus the maximum increase of reporter fluorescence. For each parameter set, we compared the predicted fluorescence increase under the extreme condition of a fully open chromatin. We then rejected all parameter sets for which the protein number did not increase sufficiently rapidly to account for the fluorescence increase measured experimentally during TSA treatment.The model can be simulated using an SSA, which is an exact continuous-time algorithm that enables simulation of chemical-reaction systems . Each siUsing the best parameter set, we simulated 50,000 cells of the two TSA-treated clones C5 and C11 for 30,000 minutes. The chromatin-dynamics parameters were then modified to account for the TSA treatment, and the two clones were simulated for a further 1,152 minutes (48 hours). For each clones, the simulated distributions were computed after 8, 24, 32 and 48 hours, and compared with the experimental distributions using a Kolmogorov-Smirnov test. The best chromatin-dynamics parameters ((from equation 1), we can use this analytical value to simplify the parametric exploration.The authors declare that they have no competing interests.JV, GK, AC, GB, and OG conceived and designed the research. JV performed all the biological experiments. GK performed all the computational analyses and model simulations. AC provided assistance for the modeling analyses. EV and VM provided technical assistance for the biological experiments. CMP and JJK advised on the design and interpretation of the experiments. JV, GK, AC, GB, and OG wrote the paper. OG and GB co-supervised the project. All authors have read and approved the final manuscript.mCherry genomic insertion sites for six 6C2 cellular clonesTable S1. Identification by splinkerette PCR of the .Click here for filemCherry reporter mRNA and protein half-livesFigure S1. Determination of the . (A) Quantitative reverse transcription PCR measurement of mCherry mRNA decay after actinomycin D treatment in two different clones of the 6C2 cell line. The best-fitting exponential curve (black line) was found by minimizing least squares . The deduced mCherry mRNA half-life was 7 hours and 4 minutes (424 minutes). (B) Flow-cytometry measurement of mCherry protein fluorescence decay after cycloheximide treatment in two different clones of the 6C2 cell line. The best-fitting exponential curve (black line) was found by minimizing least squares . The deduced mCherry protein half-life was 65 hours and 47 minutes . For both parts, ordinates are on a logarithmic scale.Click here for fileFigure S2. Exploration of model parameters based on a comparison of fluorescence distributions and SSA simulations. This figure is similar to the Figure Click here for filemCherry transcription rates and mRNA levels for six cellular clones of the 6C2 cell lineTable S2. .Click here for fileFigure S3. Model simulation of the perturbation of chromatin dynamics by TSA treatment. This figure is similar to the Figure Click here for file


Cebpa is associated with loss of self-renewal and HSC exhaustion. By combining gene expression analysis with genome-wide assessment of C/EBPα binding and epigenetic configurations, we show that C/EBPα acts to modulate the epigenetic states of genes belonging to molecular pathways important for HSC function. Moreover, our data suggest that C/EBPα acts as a priming factor at the HSC level where it actively promotes myeloid differentiation and counteracts lymphoid lineage choice. Taken together, our results show that C/EBPα is a key regulator of HSC biology, which influences the epigenetic landscape of HSCs in order to balance different cell fate options.Transcription factors are key regulators of hematopoietic stem cells (HSCs) and act through their ability to bind DNA and impact on gene transcription. Their functions are interpreted in the complex landscape of chromatin, but current knowledge on how this is achieved is very limited. C/EBPα is an important transcriptional regulator of hematopoiesis, but its potential functions in HSCs have remained elusive. Here we report that C/EBPα serves to protect adult HSCs from apoptosis and to maintain their quiescent state. Consequently, deletion of  Cebpa deletion impacts on HSC self-renewal, differentiation, quiescence and survival. Through gene expression and ChIP-seq analyses of stem and progenitor cell-enriched cell populations, we further show that C/EBPα binds to regulatory regions of genes that are induced during granulocytic differentiation, suggesting that C/EBPα acts to prime HSCs for differentiation along the myeloid lineage. Finally, we demonstrate that C/EBPα loss leads to epigenetic changes at genes central to HSC biology, which implies that it may act to recruit chromatin writers/erasers through mechanisms that remain to be characterized. In conclusion, our work identifies C/EBPα as a central hub for HSC function and highlights how a single transcription factor may coordinate several HSC fate options.Hematopoietic stem cells (HSCs) are required for the lifelong generation of blood cells. To fulfill this requirement HSCs carefully balance cell fate decisions such as self-renewal, differentiation, quiescence, proliferation and death. These features are regulated in part by transcription factors, which act by controlling the expression of genes important for the functional properties of HSCs. C/EBPα is a well-known inducer of myeloid differentiation. It is lowly expressed in HSCs and its potential function in these cells has been extensively debated. Here, we demonstrate that  Hematopoietic stem cells (HSCs) are responsible for the maintenance of a constant production of blood cells throughout life. To achieve this, HSCs have to tightly regulate their different fate options including self-renewal, proliferation, differentiation and apoptosis, as alterations in any of these may lead to HSC exhaustion, expansion or leukemia Given their impact on gene expression, the influence of TFs on HSC properties has been the focus of several studies. Indeed, factors such as C-MYB, ERG, and PU.1 are all essential for preserving HSC self-renewal and their deletion have dramatic impact on hematopoietic maintenance both during fetal and adult life TF function is interpreted in a chromatin context and, accordingly, chromatin readers and writers have been shown to be important for HSC function and maintenance. Examples include the PRC1 component BMI-1 C/EBPα is an important myeloid TF that functions not only by binding to regulatory DNA elements and directing transcription, but also through its ability to constrain proliferation by inhibiting the transcriptional activity of E2F-complexes CEBPA is found mutated in approximately 10% of cases, and studies in mouse have shown that the tumor-suppressive functions of C/EBPα can be ascribed to its ability to balance the proliferation and differentiation of hematopoietic stem and progenitor cells (HSPCs) populations In acute myeloid leukemia (AML), Cebpa leads to HSC exhaustion, identifying C/EBPα as a crucial regulator of HSC self-renewal. To molecularly assess the function of C/EBPα in HSCs, we combined gene expression analysis with genome-wide profiling of key epigenetic marks and C/EBPα binding using Chromatin ImmunoPrecipitation coupled to sequencing (ChIP-seq). Our findings demonstrate that C/EBPα modulates key pathways associated with HSC function and differentiation, in part by affecting the epigenetic states of genes associated with some of these.In the present work we have used mouse genetics to characterize the role of C/EBPα in adult HSCs. We found that loss of Cebpafl/fl and Cebpafl/fl;Mx1Cre mice  and injected them with pIpC. Mice were analyzed 18–21 days later at which time point the Cebpa allele was completely recombined in CebpaΔ/Δ mice  Cebpa is lowly expressed in the stem cell compartment and becomes transiently upregulated during commitment to the granulocytic monocytic lineage  and a compensatory increase of CebpaΔ/Δ HSCs in G1 . In total, we found 110 and 261 genes to be more than 1.5x down- and upregulated, respectively, in CebpaΔ/Δ HSCs   positive HSCs that we observe in CebpaΔ/Δ BMs   . This micis-regulatory regions by associating proximal and distal genomic regions with putative targets myeloid differentiation , which predicts functions of ntiation . SurprisΔ/Δ HSCs . One expntiation . Finallyntiation . Thus ouCebpaΔ/Δ HSCs with the nearest C/EBPα bound region identified by ChIP-seq in LSK cells. In total 6/102 genes (6%) and 21/257 (8%) of the down- and upregulated genes, respectively, contained a C/EBPα bound region within 100 kb from the TSS  around the TSS in CebpaΔ/Δ LSKs  and H3K27me3 (p = 1.41×10−5) marks to be reduced and increased, respectively, in CebpaΔ/Δ LSK cells consistent with the changes in gene expression in HSCs. The alterations in H3K4me3 and H3K27me3 identified by ChIP-seq were verified by qPCR on selected genes as well as genes, which are part of the bivalent cluster and/or the stemness signature  in CebpaΔ/Δ HSCs (−16). Moreover, when TET1 occupancy was used to stratify the bivalent genes, the TET1 bound fraction (56%) showed a marked decrease in H3K4me3 levels in CebpaΔ/Δ LSKs (−20). Interestingly, this effect was restricted to the bivalent genes as CebpaΔ/Δ and control LSKs showed similar H3K4me3 levels irrespectively of the binding of TET1 . These genes are expressed at intermediate level (data not shown) and their epigenetic changes may reflect a drift away from an epigenetic state favoring self-renewal. In addition, whereas the overall distribution of H3K4me3 and H3K27me3 marks are largely unaltered in C/EBPα-deficient LSKs, we find that loss of C/EBPα specifically affects the H3K4me3 status of bivalent genes. Interestingly, TET1 was marginally downregulated in CebpaΔ/Δ HSCs and the H3K4me3 levels were selectively reduced in CebpaΔ/Δ LSKs at bivalent genes bound by TET1. Recently it was suggested that TET1, together with the epigenetic regulators CFP1 (a recruiter of the H3K4me3 methylase SETD1) and KDM2A (a H3K36 demethylase), work synergistically to maintain a distinct epigenetic configuration on their target genes Cebpa may affect H3K4me3 status on selected genes. Whether C/EBPα affects other histone modifications such as H3K4me1 or H3K9Ac remains to be elucidated.In the present work we therefore analyzed the impact of deleting C/EBPα, one of the key transcriptional regulators of hematopoiesis, on HSC function, gene expression and the epigenetic status of key histone modifications. We find that conditional acute deletion of N-Myc to be bound by C/EBPα in c-Kit+ cells and suggested that loss of C/EBPα de-repressed N-Myc and promoted HSC expansion through an N-Myc-driven increase in proliferation. These findings contrast those reported in the present study, where we find C/EBPα to be important for the maintenance of a functional HSC pool as defined by serial transplantation and quantification of immunophenotypic HSCs. Moreover, in our HSC gene expression analysis, we do not find N-Myc to be upregulated, and C/EBPα does not bind the N-Myc promoter before the GMP state (data not shown), suggesting that C/EBPα regulates N-Myc in progenitors rather than in HSCs.The importance of C/EBPα for HSC function has recently been analyzed in an adult setting. Specifically, Ye et al. CebpaCebpa, the two models are not completely identical, which could potentially explain some of the diverging results.A number of methodological differences are likely to explain at least some of the differences between the findings in our study and those reported by Ye et al. First of all, whereas Ye et al. performed their analyses shortly (4–7 days after the last pIpC injection), we waited until 14–17 days after the last pIpC injection to avoid any confounding issues with the pIpC induced interferon response. Indeed, it has previously been reported that interferons influence key HSC parameters such as proliferation, apoptosis and self-renewal Cebpa loss have also been assessed in other developmental settings. Thus, in a pre-leukemic setting, mutation of Cebpa leads to increased HSC proliferation but its impact on HSC function was obscured by transformation to AML Cebpa has dramatic consequences for HSC self-renewal, these observations suggest that different mechanisms are governing HSC self-renewal during embryonic development, in the adult, and in a disease context. While this concept is not novel, and is supported by the selective importance of transcriptional regulators such as SOX17 The functional consequences of CebpaΔ/Δ HSCs may be explained through crosstalk between C/EBPα and PU.1 - two TFs, which have been reported to act both synergistically and antagonistically in progenitor cells CebpaΔ/Δ HSCs suggest that C/EBPα may collaborate with PU.1 to activate a transcriptional program already in HSCs. PU.1 is crucial for the development of almost all blood cells and even minor alterations in its expression level have been found to impair HSC self-renewal and lead to HSC exhaustion TFs often operate in combinations and our data suggest that at least some of the functional deficits of In order to maintain hematopoietic homeostasis, HSCs have to carefully balance the choice between a number of cell fate options. Whereas early reports suggested that HSCs sampled different lineage-affiliated transcriptional programs uniformly, more recent data suggest a considerable degree of heterogeneity within the HSC compartment CebpaΔ/Δ HSCs, suggesting that the sampling of myeloid transcriptional programs is in part controlled by C/EBPα in HSCs perhaps involving PU.1 crosstalk. Hence, in addition to its accepted role as a lineage-instructive factor required for the transition from preGMs to GMPs When analyzing the C/EBPα bound regions in the HSC/MPP-enriched LSK population we find these to be enriched for genes that were upregulated during granulocytic-monocytic differentiation. In addition, gene sets associated with myeloid differentiation are downregulated in Cebpa leads to upregulation of lymphoid gene expression programs in CebpaΔ/Δ HSCs, which suggests that C/EBPα may actively repress lymphoid differentiation perhaps through the inhibition of the transcriptional activities of EGR family members Cebpa mutant GMPs readily differentiate into T-cells Lineage choice may be viewed as a competition between distinct lineage-affiliated programs. Interestingly, we find that loss of In conclusion, we find that C/EBPα regulates HSC functions at several distinct levels. Not only is its loss associated with HSC exhaustion, but C/EBPα also appear to be involved in the selection of the myeloid vs. lymphoid lineage suggesting that C/EBPα primes lineage choice at the HSC level. Thus, our work highlight the complexities by which transcriptional circuitries are operating to maintain functional HSCs by balancing key cell fate options such as quiescence, cell death, self-renewal and differentiation.Cebpa allele was achieved by subjecting 10–12 weeks old Cebpafl/fl or Cebpafl/fl;Mx1Cre mice to 3 injections  with 200 ul polyinosinic-polycytidylic acid (pIpC)  as described previously Cebpa, the mice were treated with ciprofloxacin  in the drinking water. Mice were analysed 18–21 days after first pIpC injection.Animals were housed according to institutional guidelines at the University of Copenhagen and housed according to institutional guidelines. Excision of the All animal work was done with approval from the Danish Animal Ethical Committee. This study was approved by the review board at the Faculty of Health Sciences, University of Copenhagen.BM and PB was stained with antibodies and run on a LSRII (Becton Dickinson) or FACSAria (Becton Dickinson) and analysed using the FlowJo software. Student two-tailed T-test was used to test for significance.All reconstitution assays were performed using the Ly-5 congenic mouse system. Briefly, whole BM or HSCs  from Ly-5.2 (CD45.2) donors were transplanted by tail vein injection into 10–12 week old lethally irradiated (900 cGy) Ly-5.1 (CD45.1) mice +/− Ly-5.1 support BM cells. The recipient mice were treated with ciprofloxacin  in the drinking water. For all transplantation assays, PB was analyzed at 3–4 weeks, 8 weeks and 16–18 weeks after transplantation. The BM from the recipients was analyzed at the experimental endpoint, i.e. 16–18 weeks after transplantation. Student two-tailed T-test was used to test for significance.HSCs  were isolated as described above and stained with carboxyfluorescein diacetate succinimidyl ester (CFSE)  according to manufacturer's protocol. Next, HSCs (3000/mouse) were resuspended in PBS+3% FCS and transplanted by tail vein injection into irradiated (900 cGy) Ly-5.1 (CD45.1) mice. Twelve hours later BM and spleen from recipients were isolated, enriched for c-Kit+ cells, and analyzed on a LSRII for CFSE+ cells. Student two-tailed T-test was used to test for significance.Single HSCs  were sorted directly to round-bottom 96-well plates and cultured in serum-free medium  supplemented with 100 ng/ml SCF and 100 ng/ml TPO (Peprotech). Each well was examined by microscopy and cell numbers were recorded every other day for a week. P-values for differences in colony size were calculated using chi-square test.HSCs  were sorted and cultured in serum-free medium  supplemented with SCF (100 ng/ml), IL-3 (10 ng/ml), IL-6 (10 ng/ml), TPO (100 ng/ml) (Peprotech), EPO (5 U/ml) (Promocell), penicillin (50 U), streptomycin (50 ug/ml) and Hepes (10 mM) (Gibco) in T12.5 flasks coated with fibronectin (Innovative Research). Using cell observer (Zeiss), images were acquired every 90 seconds using a VBA module controlling Zeiss AxioVision 4.8 software and analyzed using TTT software RNA was purified from sorted HSCs , amplified, labeled and hybridized to the Mouse Gene 1.0 ST GeneChip Array . Raw gene expression data are available at the Gene Expression Omnibus (GEO) online database under ID GSE42498.Cebpafl/fl or CebpaΔ/Δ mice. Chromatin from 100.000 or 500.000 cells was incubated with antibodies for histone marks  or C/EBPα (Santa Cruz Biotechnology), respectively. The antibody-bound chromatin was captured with Protein-A sepharose beads, washed, de-cross-linked and precipitated. Precipitated DNA were mixed with 2 ng E. Coli DNA and amplified using NEB Next ChIP-seq sample prep reagent set 1 (New England Biolabs) according to manufacturer's protocol. Libraries were sequenced on an Illumina Genome Analyzer IIx or and Illumina Hiseq2000. Data was deposited in the NCBI Gene Expression Omnibus online database under ID GSE43007. See ChIP-seq was performed using LSK or GMP cells from −5 as the p-value threshold. Clusters in both positional and regular heatmaps were identified by k-means clustering using the biopython Bio Cluster module All reads were mapped using bowtie 0.12.7 Additional and detailed Figure S1CebpaΔ/Δ mice have reduced cellularity within the BM and altered distribution of myeloid progenitors. (A) PCR on whole BM and spleen for wt, floxed (fl) and deleted (del) Cebpa allele from Cebpafl/fl and CebpaΔ/Δ mice. (B) Cytospins of whole BM from Cebpafl/fl and CebpaΔ/Δ mice. (C) BM cell number in femur and tibia of Cebpafl/fl (n = 7) and CebpaΔ/Δ (n = 7) mice. (D) Distribution of myeloid progenitors in Cebpafl/fl and CebpaΔ/Δ BM. (E) Mean of (D). Cebpafl/fl (n = 6) and CebpaΔ/Δ (n = 3). Data are represented as mean+SEM.(EPS)Click here for additional data file.Figure S2Cebpafl/fl and CebpaΔ/Δ HSCs and ChIP-seq analysis of C/EBPα binding in LSK stem and progenitor cells. (A) Gene expression of selected genes in Cebpafl/fl (n = 3) and CebpaΔ/Δ (n = 3) HSCs was quantified using qPCR (B) Selected examples of ChIP-seq coverage of C/EBPα peaks in LSK cells (replicate 2). (C) Correlation of C/EBPα ChIP-seq replicates. (D) qPCR of selected genes in IgG and C/EBPα ChIP (replicate 2).Gene expression analysis in (EPS)Click here for additional data file.Figure S3Analysis of replicate H3K4me3 and H3K4me27 samples. (A) Correlation of replicates in TSS +/−1000 bp for H3K4me3 and in gene bodies for H3K27me3. (B) Mapped reads of each of the ChIP-seq samples. (C) Selected examples of ChIP-seq coverage in LSK cells. (D) qPCR of selected genes (examples from (EPS)Click here for additional data file.Figure S4GO-term analysis of the bivalently marked cluster.(EPS)Click here for additional data file.Figure S5Cebpafl/fl and CebpaΔ/Δ in signatures identified in GSEA. The corresponding p-values were determined. The black line in the p-value histogram indicates P = 0.01.Analysis of H3K4me3 and H3K27me3 level in (TIF)Click here for additional data file.Table S1CebpaΔ/Δ HSCs and Cebpafl/fl HSCs.Gene expression analysis of (XLS)Click here for additional data file.Table S2Cebpafl/fl and CebpaΔ/Δ HSCs and p-values for gene expression changes. NA designates genes not assigned on the microarray.Genes associated with C/EBPα bound regions in LSKs. C/EBPα peaks were associated to nearest TSS. Shown are the peak positions, the distance from the C/EBPα peaks to the nearest gene TSS, the gene expression fold changes between (XLSX)Click here for additional data file.Table S3Motif analysis at C/EBPα bound regions in LSKs. Pos_hit and pos_no_hits designates the number of hits and the number with no hits in the tested dataset, respectively. Neg_hit and neg_no_hits designates the number of hits and the number with no hits in the background dataset, respectively.(XLSX)Click here for additional data file.Table S4via GREAT analysis of C/EBPα bound regions in LSK cells.Pathways, GO-terms and mouse phenotypes identified (XLSX)Click here for additional data file.Table S5Genes associated with C/EBPα bound regions in GMPs. C/EBPα peaks were associated to nearest TSS. Shown are the peak positions and the distance from the C/EBPα peaks to the nearest gene TSS.(XLSX)Click here for additional data file.Table S6CebpaΔ/Δ HSCs and their association with nearest C/EBPα bound region. The TSSs of deregulated (>1.5 fold) genes were correlated to nearest C/EBPα bound region. Shown are gene expression (log2) in HSCs, fold change (FC), p-value and distance to nearest C/EBPα bound region.De-regulated genes in (XLSX)Click here for additional data file.Table S7Distance from the TSS of genes in cluster 6 to nearest C/EBPα bound region. Shown are normalized and scaled values for peak coverage at the TSS (+/−2000 bp) and in gene bodies (GB) in Cluster 6 for H3K4me3 and H3K27me3, gene expression (log2) in HSCs, fold change (FC), p-value and distance to nearest C/EBPα bound region.(XLSX)Click here for additional data file.Table S8GO-term analysis of bivalent genes in cluster 6. The significance of enriched GO terms was assessed using hypergeometric tests. In order to account for multiple testing, p-values were corrected by Bonferroni correction.(XLSX)Click here for additional data file.Table S9CebpaΔ/Δ and Cebpafl/fl HSCs and distance to nearest C/EBPα bound region.Genes constituting the stemness signature analyzed for gene expression in (XLSX)Click here for additional data file.Table S10Primer sets used for qPCR.(XLSX)Click here for additional data file.Text S1Detailed (DOCX)Click here for additional data file.


Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease. Transcription factors (TFs) are crucial to the precise regulation of many cellular processes and thus, are responsible for many human phenotypes and diseases. Now that the ENCODE project has mapped hundreds of TFs to their genomic binding locations, extracting functional biological signals is the next step in understanding their role in disease. In this paper, we present a novel approach to identifying TF targets and use these targets to find regulatory relationships between TFs and diseases. We present a large open dataset of putative TF-TF interactions and TF-disease associations which includes known connections as well as novel ones. We validate the association of one of our novel TF-disease associations, MEF2A and Crohn's disease, suggesting that our approach generates testable disease association hypotheses. Integrating these datasets will be crucial for understanding phenotypes and complex diseases. Transcriptional networks are fundamental to many aspects of biology and disease. Gene expression is a carefully controlled process orchestrated by the activities of transcription factors (TFs) which regulate the transcription of each gene. TFs usually do not work in isolation, but instead multiple factors combine in different ways to regulate groups of genes in a concerted, often cooperative fashion Despite the abundance of data about the genomic binding sites for transcription factors, determining transcription factor targets and when factors are active remains challenging. ChIP-Seq measurements can be noisy and reflect the particular condition in which the experiments are performed. Collecting more data alone will not solve this problem. As additional experiments are performed for each factor, critical and frequently used binding regions do become apparent, but it is often difficult to determine a signal threshold to distinguish common sites from condition-specific sites and general non-thematic associations from interesting biology. For example, NFκB binds to over 15,000 regions of the genome covering all possible regulatory targets of the factor. But in any given biological context, such as a local cooperative interaction with another transcription factor such as Stat1, only a handful of these genes are actively regulated by NFκB at any one time Thus, to understand transcription factor function, there is a need for computationally-efficient methods to (i) improve TF-target identification, (ii) identify small functional modules that represent context-specific biology, regulated by transcription factors, and (iii) annotate those modules with their functional implications (e.g. the role of the module in human disease).Recently, a number of methods were developed to derive a network structure to connect sets of genes (modules) to the factors that control their expression a priori knowledge of the regulatory structure which is often not available, and introduces bias in the associations between TFs and functional components. On the other hand, independent component analysis (ICA) is an unbiased and efficient method for deconvolving the signal from a fixed set of sources measured by a set of sensors  improve the identification of transcription factor targets, (ii) identify functional modules from gene expression data, and (iii) use these modules to annotate transcription factors and connect them to diseases. There are several methods for decomposing expression data to find groups of genes that work together. Network Component Analysis (NCA) is a method for inferring transcription factor activity from expression data  sensors . In esseHere, we hypothesize that regulation of each of these ICA-derived modules is controlled by a small set of TFs. Using our method , we associated transcription factors to modules and then analyzed the genes contained within each module. Intersecting these target modules with ChIP-Seq binding sites improves target identification and elucidates the functional roles of the factors–both individually and in combination. We compare our approach to traditional methods in three areas: the identification of (i) transcription factor targets, (ii) TF-TF cooperativity, (iii) and the functional roles in the context of various diseases. In each of these cases, we found that our approach significantly outperforms the traditional methods. Further, we found improved performance when our approach is used in combination with traditional methods, implying that we are capturing an independent modality of transcription factor activity. Our data-driven approach is unbiased and computationally efficient enabling systematic identification of novel TF-disease relationships. Finally, we validate one such association between MEF2A and Crohn's disease.We used a set of functional modules derived using ICA We found that many of the modules nearly or fully overlap with targets of only one or a few transcription factors –C. We foUsed in combination with the ChIP-Seq data, we hypothesized that these modules can improve identification of transcription factor targets. Specifically, we believe that putative targets (as determined by ChIP-Seq) which are also contained within significantly associated modules will be more likely to be “true” targets of the TF. To test this hypothesis, we used a set of specific GO terms In an analogous fashion to the shared functional annotation approach, we used expression analysis to validate our TF-target associations under the assumption that factor expression can be used as a proxy for factor activity. We hypothesized that for high-confidence modules (i.e. those associated with just one TF) the genes within this module should be controlled predominantly by that single factor. To test this hypothesis we examined the correlation between the expression of the module (see Methods) and the expression of the factor across the compendium of 9,395 gene expression experiments. For example, AP-2γ is the sole significant association for module 360  and we found a significant correlation between the expression correlation between module 360 and AP-2γ .We systematically evaluated all 5,002 TF-module pairs in this manner . We compMany modules connected to TFs were significantly enriched for functional annotations known to be associated with the factor . For insAnother example is the association between the transcription factor ZNF274 and module 111. Module 111 includes many zinc finger proteins of which ZNF274 is a known regulator Module 57 was associated with the greatest number of transcription factors  that share a significant proportion of target modules . ER-α, c-Jun, STAT3, and STAT1 are associated with module 4, and all have known relationships with arrhythmias Finally, for breast cancer, we found significant associations with modules 2, 13, 46, and 154 with odds ratios of 8.6 , 3.1 , 3.1 , and 4.2 , respectively. Based on their connectivity to these modules, the transcription factors E2F6, CHD2, NFYA, IRF1, HEY1, and E2F1 all cluster together in the TF-TF network .We performed an evaluation of our TF-disease associations by comparing our derived associations to an independent standard created by combining (1) 37 transcription factor-disease associations from the GWAS Catalog, and (2) 46 associations from OMIM (see Methods). We assessed overall performance using the Area Under the Receiver Operating Characteristic Curve (AUROC). TFICA achieved an AUROC of 0.712 on this test dataset . For comUsing the TFICA disease annotations, we visualized the highest confidence transcription factor-disease associations see . As expeUsing our network analysis, we identified MEF2A as the factor with the highest association with Crohn's disease. MEF2A has been previously associated with cardiovascular disease To investigate the role of module 262 and MEF2A in classification of Crohn's disease, we projected the expression values in the Crohn's dataset to generate an expression value of the module see . FinallyWe present a computationally efficient and conceptually simple method that is useful in linking transcription factors to their targets and to disease as well as derive several novel such relationships. Using current approaches, such an analysis is challenging as TFs in the ENCODE dataset bind near an average of 6,050 genes. Simple enrichment analysis on the full target set often does not reveal coherent functional groups. Factors may exhibit multifaceted functional roles and target genes in very different cellular contexts, and when all of a TF's targets are grouped together, it becomes difficult to isolate these individual contexts. Our method overlays data-driven functional module information – from a large compendium of human gene expression data – on top of TF binding data from ChIP-Seq. We demonstrate that our method (1) significantly improves TF target identification, (2) accurately identifies the functional roles of factors both independently and in combination with another factor, and (3) discovers new disease associations through these functional modules.We show that TFICA identifies targets that are significantly more functionally coherent than targets identified by naive (peak-based) methods. Importantly, TFICA can identify these targets even in cases that lack strong support from ChIP-Seq binding data (i.e. sites that are not among the strongest bound peaks). We hypothesized that TFICA would be better able to identify targets that, despite lower binding levels, are biologically important. Our “matched” analyses tests this hypothesis and we observe that TF-target functional annotation sharing  and exprOur factor interaction analysis is useful for suggesting the functional roles of TF through guilt by association, particularly for poorly described factors. For example, CHD2 is a helicase whose function remains to be fully understood. In our TF-TF network, we found that CHD2 is connected with the cyclin CCNT2 , supportFurther, we were able to recapitulate many known TF-disease associations without any prior knowledge linking the factor to the disease. For example, our factor-disease network  links ERIt is important to note, however, that this approach is dependent on the quality of the functional networks used. Other methods for generating cohesive functional networks, including data-driven approaches (e.g. PPI networks) and knowledge-based , may complement the approach and improve performance. Further, we derived our modules using a set of 9,395 gene expression experiments without regard to the particular context in which the experiment was performed. Focusing this analysis using a contextually specific set of experiments (e.g. only data focused on cardiovascular disease) could provide further specificity to the disease associations that are derived.Our work dissects transcription factor function by examining associations with specific gene modules derived from a large compendium of human expression experiments under a wide variety of conditions. This approach is generally applicable in cases where the biological function can neither be described by a single gene nor the entire genome, but instead operates at an intermediate level – groups of genes or groups of functional pathways. We demonstrate improved identification of TF targets and construct a regulatory network of human disease. Finally, we find and validate a novel transcription factor-disease association. We make three databases publicly available to the community: (1) a database of 5,002 transcription factor-module associations, (2) a database of 3,696 putative transcription factor interacting pairs, and (3) a database of 7,808 transcription factor-disease relationships. These resources should further enable researchers to explore TF interactions as well as their roles in human disease.https://simtk.org/home/fcanalysishttp://omim.org), respectively. For both of these datasets, we mapped local disease terms to standardized terms in the Disease Ontology using the annotator, resulting in 1,842 and 9,866 annotations (of which 35 and 46 map to the transcription factors in our dataset), respectively.We obtained transcription factor binding data from UCSC, which included 2,750,490 reproducible binding sites from the ENCODE project We mapped transcription factor binding sites to the nearest gene within 100 kb (gene boundaries 100 kb upstream or downstream of the boundaries of the binding site) in the RefGene annotation to determine putative binding targets of each factor . We linkWe associated each module with between 1 and 121 transcription factors. Our hypothesis is that those that are associated with fewer TFs will be of higher confidence than those associated with many, which may contain more noise. We therefore identified two subsets of TF-module pairs: (i) a “high-confidence” set of TF-module pairs where the module was only associated with one TF and (ii) a set of “medium-confidence” TF-module pairs where the module was associated to at most three TFs. We use these subsets in our evaluation of the TFICA TF-target predictions we make through the associated modules. Additionally, we computed the (Spearman) expression correlations between all TFs and all modules (projections) and used this distribution of correlations as an empirical null distribution. The correlations from the 5,002 significantly overlapping TF-module pairs were z-transformed to this distribution to determine the number of pairs that were empirically significantly correlated at the expression level.For each of the 9,395 gene expression datasets, we projected the (log-transformed) gene expression measurements of each gene onto the loadings for each module. In cases where the module contained the transcription factor itself, we removed the transcription factor prior to computing the prediction. For modules with significant associations with at most three transcription factors, we tested the (Spearman) correlation between the projection and the logged gene expression value of the transcription factor.2 values between TF-module correlations from TFICA and the naive methods. Finally, we examined the proportion of TF-target pairs that share functional annotations based on the hypothesis that true regulatory targets are more likely to share these annotations. We split the TFICA associations into three groups: (i) high confidence associations, (ii) medium confidence associations, and (iii) all associations. We compared each of these three groups to the naive approach.We evaluated our TFICA approach by comparing to two different naive approaches: a “best” naive approach consisting of a set of equal size to the module and comprised of the top bound genes (i.e. those with the highest ChIP-Seq scores), and a “matched” naive approach, which is an equal-sized set with randomly chosen targets matched to the score distribution of the targets in the component. We then assessed whether TF expression is better correlated with projections from modules from TFICA (from above) compared to the average expression of the genes in these naive modules. We segregated by number of transcription factors associated with each module and presented how often TFICA outperforms the naive method for that “number of TFs” threshold. Additionally, we performed a paired t-test at each threshold comparing the rWe used the Tanimoto coefficient as a measure of similarity between each pair of transcription factors. The Tanimoto coefficient is a common set similarity comparison technique used to compare two sets. It is defined as the size of the intersection of the two sets divided by the size of the union of the two sets. In this case, the sets were the modules that were significantly associated with each transcription factor. We used these similarities to build two transcription factor interaction networks. The first was a small network of the top 30 interactions based on the tanimoto coefficient . We annoThe second network contains 140 factors and was generated by connecting transcription factors whose pairwise tanimoto score was greater than 0.2. We used Cytoscape A and B are the sets of modules associated with the two factors, N is the total number of modules, and TFiN is the total number of TFs significantly associated with module i. We compared these three TF-TF similarity values for their relationship with correlation in TF expression, co-reporting in literature, and shared functional annotations. For each, we fit a linear model and report the β coefficients, confidence intervals, and p-values. We directly compared the TFICA approaches to the naive approach using an ANOVA.We evaluated our TFICA TF-TF network against a naive approach which linked TFs simply by their shared ChIP-Seq targets. In the naive approach, we used Tanimoto similarity to quantify the relationship between each pair of TFs. For TFICA TF similarity we used two approaches: (i) the Tanimoto similarity between the TFICA modules associated with each TF and (ii) a weighted version of the Tanimoto similarity between the TFICA modules associated with each TF. In the weighted version we upweighted high confidence TF-module associations over low-confidence. We believe that the fewer TFs a given module is associated with the higher the confidence. We used the following equation to generate a weighted Tanimoto similarity score for each TF-TF pair:We associated the derived 423 ICA modules to the 632 diseases reported in GAD (Genetic Association Database) by analyzing the overlap of the genes associated with a given disease with the genes in a given module. We used enrichment analysis with correction (FDR) for multiplicity to determine significant overlaps. Previously we linked factors to modules to produce TF-module pairs. From this analysis, we have module-disease pairs and thus, we can link TFs to diseases through their shared modules. For example, if a TF significantly targets a module and that module has a significant proportion of a disease's known genes, then we predict a relationship between the TF and the disease.We generated two lists of transcription factor disease associations. The first contains all significant associations (adjusted p<0.01 and odds ratio >1), which was used for validation (below). The second is a small set of high confidence associations, which included a stringent set of associations , as well as the best association with adjusted p<0.01 and odds ratio greater than 1 for those diseases that had no “stringent” associations used for visualization. We used Cytoscape In order to assess the predictive performance of the algorithm we needed to optimize the alpha level for determining significance between modules and diseases. We used a subset of 632 associations from the GAD that are for transcriptions factors directly to calibrate this parameter. We computed the AUROC at a range of thresholds  to deterWe tested the performance of our method  against an independent validation dataset of known disease to gene  associations, a combination of the NHGRI GWAS catalog and OMIM . We compWe obtained a publicly-available expression dataset for 59 Crohn's patients and 42 healthy controls (GEO Accession Number: GSE3365) To generate an expression value for the module, a projection was calculated, using the dot product of the expression values of each gene in the module and their individual loadings derived from ICA. Expression of MEF2A and the projection of the module were normalized to a z-score and summed to create a “combined” metric. These three metrics were compared among cases and controls using a Wilcoxon rank-sum test and fit using a logistic regression .All statistical analyses were performed with the R statistical computing package (version 2.14.1). Enrichment analyses were computed using Fisher's exact test, with p-values corrected using the Benjamini-Hochberg FDR correction for multiple hypotheses. All expression correlations were performed using Spearman correlations. ROC curves were generated using the ROCR package for R Figure S1Method details. (A): The distance between TF binding sites from ENCODE and the nearest gene in RefGene are shown. (B): The distribution of genes putatively regulated by each of the transcription factors (TFs) on the basis of their proximity is shown. Many transcription factors map to thousands or tens of thousands of genes. (C): Enrichment between genes regulated by each TF and genes found in modules generated by ICA results in 5,002 TF-module associations. These modules are associated with up to 121 TFs.(PDF)Click here for additional data file.Figure S2TF-module associations. Enrichments between transcription factor target sets and genes found in each module from ICA are plotted and hierarchically clustered by TF and module. The most striking cluster along modules is module 57 (red box), which includes many transcription factors as targets themselves. See (PDF)Click here for additional data file.Figure S3TFs can explain modules identified by ICA. The number of significantly enriched transcription factors that are required to regulate (A) 80%, (B) 90%, and (C) 100% of all possible genes in a module. Possible genes are defined as genes that are targeted – as determined by ChIP-Seq experimental data – by at least one of the 148 TFs in the dataset. 87 modules could not be explained by targets of any associated TF (shown in the N/A column). Only significant TF-module associations are used to calculate TFs required. (D) A histogram of the proportion of the modules that are “explainable” by TF targets determined by ChIP-Seq.(PDF)Click here for additional data file.Figure S4Variance explained by modules. The number of transcription factors significantly associated with each module correlates with (A) the rank of the module when sorted by the module's variance  and (B) the percent of total variance of that module .(PDF)Click here for additional data file.Figure S5TFICA outperforms naive modules in expression correlation. For each of the 9,395 gene expression experiments, the expression values of each gene in a module are projected using the ICA loadings. For each TF-module pair tested, this projection is compared to the mean expression of two modules based directly on ChIP-Seq data: a “best” naive module, a set of genes (the same number as the TFICA module) with the highest ChIP-Seq binding scores (black), and another equally-sized “matched” set with binding scores matched to the scores of bound genes in the module (red). These comparisons are separated on the basis of modules associated with a varying number of transcription factors. (A): The proportion of cases where TFICA is more correlated than expression than each of the two naive modules. Diamonds indicate significantly higher differences, as determined by a binomial test. Note that TFICA outperforms the “matched” module at every threshold, and the “best” module at high- and medium-confidence associations . (B–C): The correlation values at each threshold are compared using a t-test and the one-sided p-value where the TFICA correlation is higher (B) and lower (C) is shown here. (D): The number of significant TF-module pairs in each bin are plotted.(PDF)Click here for additional data file.Figure S6TF-TF interaction prediction performance comparison. Similarity of target modules among TF pairs is correlated with gene expression correlation (top row), literature co-reporting (middle row), and shared functional annotations (bottom row): We compared three approaches: (i) a naive similarity approach based on the proportion of targets two TFs share (left column), (ii) a TFICA approach based on the proportion of significant modules two TFs share (middle column), and (iii) a TFICA approach where the TF-module were weighted by the confidence of the association (right column). In each case the weighted method is most correlated, followed by the non-weighted TFICA method, with the naive approach being the least correlated.(PDF)Click here for additional data file.Figure S7Performance assessment. (A): A number of module-disease FDR cutoffs were assessed against a training dataset of associations from GAD to train our method (B): The TFICA method (red) identifies TF-disease associations, which are compared to enrichments using GREAT (blue), as well as a simple target enrichment method (black). Performance is visualized using ROC curves using a combination of the NHGRI GWAS catalog and OMIM as a gold standard dataset. A composite measuring using our method and GREAT is shown in purple.(PDF)Click here for additional data file.Figure S8Associated diseases by TF. A histogram of the number of TFs associated with a given number of diseases is shown in (a). Additionally, Two estimates of global function are plotted: (b) the number of GO annotations for a given TF and (c) the number of ChIP-Seq targets for a given TF. In both cases the number of diseases associated to a TF using the TFICA algorithm is significantly correlated to the TF's diversity of function.(PDF)Click here for additional data file.Table S1Significant associations between transcription factors and modules.(CSV)Click here for additional data file.Table S2Projections of gene expression measurements for module-TF pairs.(CSV)Click here for additional data file.Table S3Modules shared, expression correlation, and literature evidence between pairs of transcription factors.(CSV)Click here for additional data file.Table S4A regulatory network of human disease.(CSV)Click here for additional data file.


ChIP-seq experiments identify genome-wide profiles of DNA-binding molecules including transcription factors, enzymes and epigenetic marks. Biological replicates are critical for reliable site discovery and are required for the deposition of data in the ENCODE and modENCODE projects. While early reports suggested two replicates were sufficient, the widespread application of the technique has led to emerging consensus that the technique is noisy and that increasing replication may be worthwhile. Additional biological replicates also allow for quantitative assessment of differences between conditions. To date it has remained controversial about how to confirm peak identification and to determine signal strength across biological replicates, particularly when the number of replicates is greater than two. Using objective metrics, we evaluate the consistency of biological replicates in ChIP-seq experiments with more than two replicates. We compare several approaches for binding site determination, including two popular but disparate peak callers, CisGenome and MACS2. Here we propose read coverage as a quantitative measurement of signal strength for estimating sample concordance. Determining binding based on genomic features, such as promoters, is also examined. We find that increasing the number of biological replicates increases the reliability of peak identification. Critically, binding sites with strong biological evidence may be missed if researchers rely on only two biological replicates. When more than two replicates are performed, a simple majority rule (>50% of samples identify a peak) identifies peaks more reliably in all biological replicates than the absolute concordance of peak identification between any two replicates, further demonstrating the utility of increasing replicate numbers in ChIP-seq experiments. The goal of chromatin immunoprecipitation (ChIP) experiments is to map the binding sites of a molecule  across the genome in a cell type or tissue . ChIP asDifferent patterns of “peaks” will form at putative binding sites after the sequence reads are aligned to a reference genome. Peaks produced by site-specific binding of transcription factors are very narrow, while peaks of specific histone modifications are more diffusive and can cover large domains of DNA across several nucleosomes –9. TheseIn addition to sequences truly associated with the molecule of interest, random background noise is also present due to non-specific binding or biases in library construction and sequencing 13–16]. –16. 13–1There is not yet consensus on how to analyze multiple-replicate ChIP-seq samples . PoolingIDR combines pairs of replicates. However, IDR has many limitations. For the bivariate model of IDR, the preliminary peaks have to contain both high quality peaks and peaks that are most likely to be only noise, and the algorithm is currently implemented for only a few peak callers such as SPP  and MACSIn genomic experiments, independent processing of biological replicates is standard. Combined data may be unduly influenced by an outlier sample. Detection rates are also reduced, with binding sites with smaller signal-to-noise ratios being especially affected. However, detection is critical in ChIP-seq experiments for investigators who want to obtain maximal information. Another severe limitation of analyzing a single combined sample is that it precludes downstream quantitative comparisons across samples. Recently attention has been drawn to analyzing individual samples separately in ChIP-seq experiments , 35–41. In this study, we analyzed five ChIP-seq experiments with three or more replicates. Multiple methods for defining the consensus peaks using biological replicates were considered in order to minimize variability and maximize consistency. We confirm results from genomic studies and conclude that more than two biological replicates are essential for ChIP-seq experiments. We propose using a simple majority rule for peak identification and show that this yields more reliable peaks than absolute concordance with fewer replicates.Drosophila melanogaster with three replicates, and one input DNA control (GEO accession: GSE36107).RNA Polymerase II ChIP-seq in Transcription factor NFKB ChIP-seq  .H3K4me3 ChIP-seq in H3K27me3 ChIP-seq in mouse ganglia with three biological replicates, and no input control (unpublished)We used five ChIP-seq data sets for this study. Two are previously unpublished and created in our labs. The raw data (fastq files) of the other three were downloaded from Gene Expression Omnibus (GEO).Biological replicates from each dataset were individually processed and underwent three levels of quality control . The fasPeak identification from noisy ChIP-seq data is a challenging process, for which over 30 programs have been developed (for a review see ). In thiBoth programs were run with default settings with the input DNA samples as the control (except the H3K27me3 dataset for which the input control is unavailable). Notably, the default setting of MACS2 removes duplicate tags at the same location (–keep-dup=auto) and report peaks with FDR <0.05 (-q 0.05), while CisGenome does not automatically remove duplicates by default, and the cutoff for peak identification is a fold of enrichment >3 (-c=3.0) when a input control is used and >10 (-c=10) when the ChIP sample is analyzed alone.Additional settings were explored. For the H3K27me3 data, we also present analysis results when removing duplicate tags first and using –c=6 besides those generated by the default setting. Parameter choices are important and investigators should spend time adjusting the parameters in order to obtain a reasonable list of binding sites for their factor of interest. Our intention here is not to compare the peak callers themselves but to use disparate peak callers with disparate settings and diverse data to see if there are universal conclusions about processing biological replicates that can be made.http://genome.ucsc.edu/ENCODE/qualityMetrics.html).QC2 is performed after peak identification and included summarizing the number of peaks identified as well as metrics to evaluate peak quality. The fraction of reads in peaks  was calFor peaks independently identified from multiple replicates, it is unlikely that the exact peak position is the same across independent replicates. Peaks were considered overlapping among replicates if at least one nucleotide was shared. Unique and common peaks were identified across replicates. Peaks found only in a single replicate were considered unique. Peaks present in all replicates were considered to be common. The simple agreement coefficient was calculated as the number of overlapping peaks over all peaks identified in a pair of replicates. McNemar's test  evaluateWe explored several different ways to define a consensus region from peaks overlapping among a set of replicates with various exact positions . We compth percentile were considered the background. We used a Z-test where the null hypothesis is that its RPKM was not greater than the background. The peak was considered to be detected above background (DABG) when the null hypothesis was rejected .In many cases peaks were present in all replicates, but there are also cases where peaks were only identified in a subset of replicates. We proposed a “simple majority” rule and considered a peak identification to be consensus if it was detected in a majority of replicates, based on the reasoning that (1) if peak detection were random the likelihood of seeing a peak in the same location in multiple replicates would be small, and (2) given the noisy nature of ChIP-seq samples, a particular tool's chance of not identifying a peak in a region  is known to be large . As the sample size of a ChIP-seq experiment increases, requiring an absolute consensus (100% agreement) will increase the false negative rate substantially. The majority rule allows for the simple extension of consensus between two replicates (the guideline proposed by ), to morD. melanogaster was downloaded from: ftp://ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r5.30_FB2010_07/.The Gene Feature Format (GFF) file containing the genomic annotation of The promoters were defined as +/-2kb from the TSSs. The genic regions were taken as the upstream 2kb from the TSSs until the downstream 2kb from the transcript terminate sites (TTSs). Agreement between the RPKM of pairs of replicates was inspected using Bland-Altman plots for both promoters and genic regions.For all of the experiments we examined, the read level QC1 showed that the sequencing depth and quality varied among replicates . Sufficient numbers of total reads and uniquely mapped reads were necessary for binding site discovery. The RNAPII data met the rule of thumb promoted for the minimal mapped reads per sample, which is 2 million for drosophila, and 10 million for mammalian genome . Under tQC2 revealed that the numbers of peaks independently identified were different for replicates of the same experiment  and the difference between peak calling programs was evident. The performance of same parameter settings depended upon the particular experiment, and there was not an immediately transparent mapping between the two underlying models of MACS2 and CisGenome. Using default settings, MACS2  identifiWithout prohibitively costly independent validation experiments, the rate of false positive and false negative peaks cannot be accurately estimated. However, consistency of replicates provides a proxy for such an estimate, as the general assumption is that peaks identified in multiple samples, in approximately the same region, represent the same protein/DNA binding phenomenon. As showed by the peak level QC2, despite discrepancies in the number of peaks identified by CisGenome and MACS2 in individual replicates, the numbers of common peaks were more comparable between the two programs . The agreement was reasonable for the H3K27me3 data when MACS or adjusted CisGenome was used, but decreased when the peaks were identified using the default settings of CisGenome . For H3K27me3 dataset, we focused on the results from adjusted instead of the default settings of CisGenome. Similarly, the default CisGenome also did not perform well for the H3K4me3 data. This was probably because CisGenome, unlike MACS2, was not optimized for histone signals (broad peaks). The FOXA1 data also had few reproducible peaks across replicates. Compared to the other datasets, the FOXA1 data appeared noisier in the genome browser and we were not able to observe noticeable peaks near known selected FOXA1 target genes. The metric we proposed (proportion of overlapping peaks) and the existing metrics (sequencing and mapped reads) all suggest high background noise in these data. The researchers in the original report combined the five replicates into one sample prior to analysis.Generally, the number of peaks increases with the number of sequence reads for both CisGenome and MACS2 , consistent with previous studies . McNemarRead coverage within specific peaks provides a quantitative measurement of enrichment above background. We calculated the Reads Per Kilobase per Million mapped reads  in the For RNAPII and NFKB, CisGenome called fewer peaks that had higher agreement across replicates , indicating these peaks were of higher quality. These peaks were also wider, including more reads that covered broader regions. In the H3K4me3 data, MACS2 identified fewer but higher quality peaks compared to CisGenome. The first replicate of H3K4me3 data was less correlated with the other replicates , possibly an outlier, which was hinted by its lower read counts. The adjusted CisGenome and MACS2 yielded comparable Kappa and Spearman's coefficients for the H3K27me3 data. However, the distribution of the BA plots indicated that CisGenome peaks have better agreement .Despite the difference in the number of identified peaks, the RNAPII, NFKB and H3K27me3 replicates were highly correlated in terms of signal quantification  there is sufficient support for its existence. More peaks were included as common under this majority rule  produced comparable quantitative measurements of consensus peaks and led to similar conclusions about the utility of replicates. Although we focused on default settings for this exercise, adjusting settings on peak callers can improve the concordance of peak identification among replicates.The real binding sites are unknown for most ChIP studies. The strategy that requires identification of a peak in all replicates (absolute consensus) will exclude genuine binding sites. The failure to detect a peak in a particular sample may be due to low coverage or high background at a particular peak position, in combination with the uncertainty in peak calling algorithms. A practical approach to maximize site discovery is to increase the number of replicates. We showed that peaks that were identified in the majority of replicates were likely to be enriched above background in the replicates where the initial peak calling process had failed. When more than two replicates were examined, many peaks that would be considered unqiue in the pair of replicates were confirmed in an additional replicate. Peaks identified in the majority (>50%) of replicates were frequently confirmed in the missing replicates when they were specifically tested for detection among background, while the confirmation rate for unique peaks were much lower, suggesting these majority peaks were more likely to be true positives. Equally importantly, no single replicates were the source of most discrepancies and so the inclusion of more replicates improved the number and quality of peaks for all replicates. The majority rule may be applied to other IP-seq studies. Twice as many microRNA binding sites were identified from two out of three replicates than from all three replicates using high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP) technology .Real target sites may not recur uniformly across replicates above background as defined by a particular peak discovery algorithm. Annotation-based approaches provide quantification that is independent of peak calling. They are complementary to peak identification for promoter/transcript-associated protein binding, or can be employed when peak calling is difficult. Notably, they cannot replace peak callers, as many binding sites would be missed, as it has been demonstrated by previous ChIP experiments that transcription factors, even transcription activators such as STAT1The decade-long debates on replication for microarray experiments  and more