 Methodology article
 Open Access
 Published:
Fine mapping chromatin contacts in capture HiC data
BMC Genomics volume 20, Article number: 77 (2019)
Abstract
Background
HiC and capture HiC (CHiC) are used to map physical contacts between chromatin regions in cell nuclei using highthroughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest.
Results
We hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts. Our model is conceptually novel, exploiting the spatial pattern of counts in CHiC data. Although we use only the CHiC count data in fitting the model, we show that the fragments prioritised display biological properties that would be expected of true contacts: for bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancertarget networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently cocontacting fragments can typically be explained using a subset of direct contacts consisting of <10% of the number in the full run, suggesting that greater resolution can be extracted from existing datasets.
Conclusions
Our results appear largely complementary to those from a perfragment analytical approach, suggesting that they provide an additional level of interpretation that may be used to increase resolution for mapping direct contacts in CHiC experiments.
Background
The threedimensional structure of the genome influences gene expression at varying levels of scale [1]. Multimegabase compartments of active and inactive chromatin, as well as topologicallyassociated domains (TADs) spanning hundreds of kilobases, can be readily identified by mapping physical interactions using genomewide chromatin conformation capture techniques (HiC) [2, 3]. However, as HiC quantifies interactions between all possible pairs of regions in the genome (e.g. HindIII fragments) via massively parallel sequencing, it is inefficient at characterizing individual enhancerpromoter interactions in great depth. To explore such regulatory interactions in detail, the more recently developed Capture HiC (CHiC) method targets sequencing efforts toward interactions between predefined regions of interest (“baits”, e.g. HindIII fragments overlapping gene promoters) on one end, and all other regions (“prey”) on the other [4, 5].
CHiC has enabled identification of contacts made by promoters in primary human cells [5, 6]. The contact maps thus generated show a tendency for multiple contiguous fragments to be linked with the same promoter [7, 8], but it is not clear whether enhancers overlapping all these fragments or only a subset of them are directly relevant to the promoter’s regulation. Conversely, the same enhancer region can appear to interact with promoters of multiple genes [9, 10], while it remains unclear whether this reflects coregulation of these genes [11]. Either phenomenon could also be caused by a lack of resolution in these maps, which are typically constrained by the restriction enzyme used (e.g. HindIII produces fragments of median length 4kb). Given that typical enhancers and promoters are considerably shorter than a single HindIII fragment [12], we hypothesised that collateral contacts may be identified along with the direct enhancerpromoter contacts they neighbour. Such collateral contacts might result from a bait traversing the regions around its primary target via Brownian motion, potentially during the formation of loops [13] or from inaccuracies in the crosslinking of proximal regions during the CHiC procedure [14, 15].
The CHiC signal around any given bait is represented by counts of read pairs (“counts”) linking that bait fragment to each of its neighbouring prey fragments. The signal exhibits a characteristic exponential decay around the location of the bait (Fig. 1a), thought to reflect Brownian motion rather than biologically interesting interactions. Existing approaches for calling interactions from CHiC data first fit a regression model to these counts. The model estimates the expected rate of decay by distance from the baited fragment, while accounting for other bait or preyspecific factors, such as capture efficiency and enrichment bias. Then, the count for each individual baitprey fragment pair is considered, and those whose count is substantially above that predicted by the regression model are identified (Fig. 1b) [6, 9]. We noted that CHiC signals often appeared spatially autocorrelated around interacting prey, in their raw as well as their regressionadjusted form. We sought to use this information to improve resolution of CHiC contact maps. We hypothesised that joint modelling of neighbouring prey fragments would allow direct contacts to be distinguished from collateral contacts under the assumption that the CHiC signal peaks at directly contacted preys and gradually decays amongst neighbouring fragments (Fig. 1c).
Here, we propose a statistical model in which, for any given bait, the expected CHiC signal at each prey is expressed as a sum of contributions from a sparse set of fragments directly contacting that bait. This decomposition model allows us to view the CHiC signal at each prey in the context of the signals in its local environment. We fit the model through reversible jump Markov Chain Monte Carlo (RJMCMC) to identify primary contacts in published CHiC data from two cell types, nonactivated and activated CD4 ^{+} T cells [8].
Results
A spatial model for CHiC data
In common with other approaches [6, 9], we first process the read count data from CHiC using a negative binomial (NB) model to adjust for fragmentspecific effects as well as the decay in counts around the bait (Fig. 1a, see “Methods” section). We then define a joint model for the resulting standardised residuals, which we call “NB residuals” (Fig. 1b). We assume that these NB residuals follow a normal distribution with unit variance (owing to their standardisation) and, in the absence of interactions, zero mean. Where either direct or collateral contacts exist, we propose the NB residuals still follow a normal distribution with unit variance, but with a nonzero mean which we expect is positive. These assumptions are compatible with observed data (see below). Focusing on a single bait, b, and its nearest F_{b} neighbouring prey fragments, let Y_{bp} be the observed NB residual for prey fragment p. Then
where μ_{bp} is the expected NB residual. We assume that a direct contact between b and a fragment p causes μ_{bp} to increase by some value β_{bp}, the magnitude of which reflects the strength of interaction. In the absence of any other contacts we would simply have μ_{bp}=β_{bp}. However, we also assume that a direct contact at another fragment, q, can affect μ_{bp}, particularly when it is near to p. Such additional contacts increase μ_{bp} by
where β_{bq} captures the strength of the interaction at q and d(p,q) is the absolute linear distance between the midpoints of fragments p and q, with parameter ω (assumed fixed and known) controlling the rate of decay.
The exponential form in (2) was chosen by examining model fits with this and other possible forms of decay functions to a subset of baits. The value of ω was fixed at 10^{−4.7}, chosen from a range of values tried because it produced the best fit to our data (full details in Additional file 1).
Thus, μ_{bp} can be expressed as a sum of contributions:
where the sum is taken over all fragments q in some neighbourhood of the bait b, and γ_{bq} is a latent indicator variable, taking the value 1 if there is a direct contact between fragments b and q (i.e. β_{bq}≠0) and 0 otherwise.
We provide functions to implement this model in the R package Peaky, available from http://github.com/cqgd/pky.
Inference of baitprey interactions
We fit the model using an RJMCMC sampler, R2BGLiMS [16]. For each bait b, the distribution of sampled coefficients \(\phantom {\dot {i}\!}\beta _{b1},\ldots,\beta _{{bF}_{b}}\) reflect the posterior distribution of contact strengths between the bait b and its neighbouring preys. Given our prior assumption that contacts lead to increased rather than decreased counts, we decided not to use the common marginal posterior probability of inclusion, defined as the proprtion of samples in which β_{bp}≠0. Instead, we defined an analogous statistic: the marginal posterior probability of a contact (MPPC) between bait b and prey p, as the proportion of sampled models in which β_{bp}>0, and use this as the primary statistic for inference.
Reproducibility of MPPC calls on replicate data from macrophages
Previous analysis has shown strong similarities between the interactomes of different macrophages [8]. In order to examine the reproducibility of MPPCbased calls, we used macrophage data which had been collected on three stages of differentiation from three samples, giving results from nine individual samples [8]. As our intention was to apply our method to CD4 ^{+} T cell data from three samples, we combined data to compare calls from nonoverlapping sets of three or six samples, each derived from one or two individuals across all developmental stages. We constructed all 12 possible ordered pairs from these data that did not contain the same individual twice, labelling one member “reference” and the other “test”. We ran peaky independently on each dataset, and stored the MPPC for each baitprey pair on chromosomes 1–10. The distribution of MPPC was similar in each dataset, with an overall 83% of values <0.01 and 99% <0.1 (Fig. 2a). We called “true negatives” in each reference dataset at MPPC ≤0.01 and “true positives” at MPPC >α for α=0.01,0.02,0.05,0.1, then drew ROC curves to assess reproducibility in each corresponding test dataset (Fig. 2b). Reproducibility was best when both test and reference datasets were the same size, and worst when the reference data was bigger than the test data, as might be expected when attempting to detect contacts in a less powerful dataset. Overall, AUC values were very good, ranging from 0.80–0.92 (Fig. 2c) with higher values seen when higher alpha threshold were used to define “true positives”.
Application to CHiC data from activated and nonactivated CD4 ^{+} T cells
We applied the above model to four parallel data sets generated from CD4 ^{+} T cells: two from nonactivated cells (“non”) cultured for four hours in buffer and two from activated cells (“act”) cultured for four hours with antiCD3/CD28 beads, all previously analysed with CHiCAGO [8]. We chose to use two cell types so we could check any results were representative rather than specific to a single dataset (which might indicate overfitting), and chose these cell types specifically because of availability of external datasets for biological validation. Each pair consisted of a promoter capture set, with 22,032 bait fragments representing the promoters of 28,007 unique annotated genes (16,116 baits representing 17,731 protein coding genes), and a validation capture set, with 945 bait fragments that were preys contacting baits in the promoter dataset according to analysis using the standard CHiCAGO pipeline [6] in CD4 ^{+} T cells, megakaryocytes or erythroblasts [8]. Note that the validation set are used here to provide a complementary dataset whose true contacts would be expected to have alternative biological characteristics than the promoter capture set, owing to the opposite fragment being captured. The validation capture array was designed before peaky was conceived, on the basis of CHiCAGO scores only. Thus, because baits were not selected into the validation data on the basis of MPPC they cannot be used to validate the MPPC itself. We preprocessed raw counts from each dataset separately to generate NB residuals. QQ plots showed that our assumptions of central normality and a long right tail were met (Additional file 2: Figure S1).
Suggested practice by the authors of CHiCAGO is to declare “significant” interactions when CHiCAGO scores exceed 5 [6]. We followed this advice, and focused on baits which had at least one prey fragment with a CHiCAGO score >5 within a window of at least 10 mb around the bait (5 mb either side). We fitted the joint model described by (1) to the NB residuals within these windows for each bait separately in two parallel RJMCMC chains, running additional iterations until the correlation between MPPC from each chain exceeded 0.75. This was achieved for over 94% of baits within 20 million iterations (Table 1, Additional file 2: Figure S2), and we focus on our inference of these below. The union of samples from both chains was used for inference.
MPPC provides additional information for distinguishing biologically plausible contacts
We first compared the MPPC and the CHiCAGO scores for each baitprey pair. We noted that the CHiCAGO score decayed more rapidly with increasing distance from the bait fragment compared to the MPPC (Additional file 2: Figure S3), presumably reflecting, in part, the different approaches taken to longdistance contacts. CHiCAGO deliberately downweights the significance of longer distance (with weights learned based on reproducibility of signals across technical replicates). As our intention is to finemap longer runs of contacts identified by CHiCAGO, we chose not to apply any downweighting in order to avoid doubly penalising them. We also noted that the MPPC and CHiCAGO score were positively correlated (Spearman’s ρ>0.23; Additional file 2: Figure S4), although a substantial fraction of baitprey pairs showed high CHiCAGO scores and low MPPC or vice versa. We therefore investigated whether one measure alone, or both together, were better at predicting biologically plausible contacts using a variety of measures. We considered that direct contacts from baits in the promoter capture set should be more likely among prey that overlap active chromatin states or that corresponded to published CD4 ^{+} T cell promoterenhancer networks [17], and in the validation set among prey that contain a gene promoter or the promoter of a more strongly expressed gene. We found that for all of these measures, regression models indicated that either MPPC and CHiCAGO scores together (7 cases) or MPPC alone (1 case) were best able to predict these features (Table 2). In all cases, measures of biological plausibility increased with increasing MPPC at any CHiCAGO threshold, suggesting that MPPC can be used to discriminate between fragments with similar CHiCAGO scores (Additional file 2: Figure S5). This suggested that MPPC and CHiCAGO could be used together to better predict biologically plausible direct contacts than a model which considers each baitprey pair independently, such as CHiCAGO.
MPPC can be used to prioritise direct contacts amongst long runs
The median number of prey fragments per bait identified by a CHiCAGO score >5 ranged from 7–8, but with a maximum over 200 (Additional file 2: Figure S6, Table S1). In comparison, our model tended to have a slightly greater expected number of contacts per bait when the CHiCAGO count was low (many of these related to fragments with CHiCAGO scores ∈ [ 3,5)), but many fewer when the CHiCAGO count was high (Additional file 2: Figure S6). To enable discussion of longer runs of fragments with high CHiCAGO scores, we define a “stretch” of length n to be a series of n adjacent fragments with CHiCAGO scores >5. In the longest stretches of length 50 or more, our model estimated the expected number of direct contacts to be ∼7% of the number of prey (Additional file 2: Figure S7). The posterior was spread over a larger number of fragments than the number of expected contacts, but it was not uniform, and we found that the majority of the posterior was often concentrated within a minority of the fragments: for example, within stretches of length 50 or more, a median of 76% of the corresponding sum of posterior probabilities over all fragments could be captured by just the top 30% of the fragments ranked by MPPC, while 90% of the posterior could be captured by the top 50% of the fragments (Fig. 3).
This suggested that long stretches could result from direct contacts at a small subset, and that our joint model could distinguish these, ranking some as more probable direct contacts than others. As the true sets of direct contacts are unknown, we again used external data to assess whether this prioritisation corresponded to fragments with more biologically supportable characteristics. We found that, within these stretches, the MPPC remained significant predictors of whether fragments corresponded to biologically plausible features across all run lengths (Additional file 2: Table S2).
Finally, we illustrate these results by considering example baits with long runs of CHiCAGOsignificant prey fragments. First, a bait on chromosome 2, which is annotated with both an antisense gene AC009505.2 and an alternative promoter of NCK2, is linked to long runs totalling 198 CHiCAGOsignificant prey fragments which our model suggests can be explained by subsets of 7 fragments (Fig. 4). Visually, the MPPC profiles here also cluster into 7 groups. In particular, the stretch of fragments surrounding the promoter which are split by MPPC into 5 groups, each of which correspond to regions with active chromatin marks. Distinct clusters are not always visually distinguishable, of course. Figure 5 shows the analysis in the region of the ETS1 gene promoter. Here, runs totalling 218 prey fragments have CHiCAGO scores >5 which our joint analysis suggests may be explained by a subset of 7 fragments. Visually, 6 clusters can be discerned in the MPPC profiles, all in similar locations to peaks in H3K27ac, but the sum of MPPC over fragments in the third cluster from the left is 1.91, suggesting this cluster may harbour two direct contacts, although their distinct locations are not clearly separable.
The MPPC is a continuous measure from 0 to 1, rather than a yes/no discriminator. We evaluated its utility according to its correspondence with characteristics expected in direct contacts.
Discussion
Our results support our hypothesis that long runs of prey fragments with high counts in CHiC data can result from a smaller number of direct contacts together with collateral signal at their neighbours. This suggests that efforts to jointly model the pattern of counts across multiple fragments have potential to distinguish those direct contacts. Joint modelling to improve resolution is already used to fine map genetic causal variants in genomewide association studies (GWAS). It is accepted in GWAS that the p value corresponding to a test of association between a single genetic variant and some phenotype should be interpreted in the context of the p values of its neighbours, either by highlighting the variant in the region with the smallest p value, or by fitting a variable selection model to find a sparse subset of variants which could explain the association signals across the region. The primary difference between our CHiC model and the class of GWAS fine mapping models that also fit the association statistics directly (e.g. PAINTOR, [18]) is that the decay of GWAS association signals across genetic variants has been established to relate to the linkage disequilibrium or correlation between those variants within the population, while our model assumes an exponential decay specified by a single parameter ω. We chose ω by considering a range of values and choosing that which produced residuals without obvious autocorrelation. This meant we could parallelise our analysis, considering each bait independently, but different values of ω would produce different results. Future work will explore whether it is computationally feasible to specify ω within a hierarchical framework that considers multiple baits simultaneously. In addition, we intend to investigate whether these ideas – using information from sets of proximal locations in a joint model to make inference about each individual location – could be adapted to other techniques used to call DNA contacts, such as next generation CaptureC [19], ChIAPET [20] and HiChIP [21], although different decay functions might be required.
In addition to jointly modelling the signal across multiple fragments, our proposed model contrasts to previous efforts to analyse CHiC data by producing a Bayesian measure of confidence in the location of a direct contact  the MPPC. Both the MPPC and the CHiCAGO score decay with distance from bait, emphasising that short range contacts predominate, at least within the set of contacts detectable through CHiC. There is, though, a notable difference between the rates of decay (Additional file 2: Figure S3). This reflects the deliberate choice of the CHiCAGO authors to weight p values such that more distant interactions were less likely to be called significant. We chose not to adopt any distantdependent prior as our intention was to fine map contacts already called by a method that incorporates this distance penalty, such as CHiCAGO, and we did not wish to doubly penalise distant contacts. However, it is possible that adopting such a prior would lead to improved inference were our joint model to be applied alone. We also note that while we have broadly followed the CHiCAGO preprocessing approach here so that our results can be considered a fine mapping layer on top of a standard CHiCAGO analysis, other preprocessing would be possible. For instance, for bait fragments near a TAD boundary, the counts are likely to have a different distribution (fewer counts) towards the boundary rather than towards the centre of the TAD. While our approach is agnostic to the location of TAD boundaries, other methods such as PeakC [22] explicitly account for asymmetrically distributed counts around bait and could be used as alternate models to generate standardised residuals, followed by peaky fine mapping.
We argue that our joint analysis of neighbouring prey fragments adds a further useful dimension to the analysis of CHiC data, with a CHiCAGO score reflecting the (distance from baitadjusted) evidence for there being any contacts in a neighbourhood, and the MPPC reflecting the expected number and the uncertainty in the precise location of direct contacts. Other advantages of adopting a Bayesian framework include the ability to extend the model to include not just baitprey distance, but other prior information on the likelihood of direct contacts. This would enable, for example, information from previous experiments in related cell types to inform future analyses.
Conclusions
We have proposed a new model for calling direct contacts from CHiC data that, in contrast to existing fragmentbyfragment analysis methods, exploits information from each prey’s neighbouring fragments. Our joint model identifies prey fragments with biological characteristics that would be expected at sites of direct contact, such as an active chromatin state when they contact promoters. We have shown this information is largely complementary to that produced by the perfragment method, CHiCAGO. Combining inference across these two approaches is more stringent – a prey fragment needs to simultaneously have a higher count than expected and a supporting pattern among neighbouring fragments – and leads to improved resolution of direct contacts in CHiC datasets.
Methods
Preprocessing of read count data
We first preprocess the read count data using similar methods to standard CHiC analysis to produce residuals which have a standard normal distribution in the absence of interactions. The raw data for a CHiC experiment takes the form of a sparse matrix of counts for pairs of baits and preys. In practice, most entries in this matrix are zero, and analysis focuses on modelling the counts at preys that are within some linear genomic distance of each bait. Statistical inference of contacts is based on a two step approach. First, counts are modelled to adjust for systematic effects such as distance between bait and prey, and capture efficiency using either negative binomial (NB) regression [9] or a convolution of NB and Poisson regression, to model biological and technical noise separately [6]. Second, a decision is taken to call contacts based on comparing observed counts to those expected under this empirical model estimated under a null hypothesis of no true interactions, either using raw p values [9] or p values weighted to allow for the complication that we expect to find more interactions among fragments proximal to the bait, but test many more long distance pairs. We wished to use the first part of this procedure to account for the systematic effects in the data, and generate standardised residuals (that is, residuals with unit variance) for input into our proposed joint model.
The CHiC data from CD4 ^{+} cells that we propose to use for this study have previously been processed by the HiCUP pipeline [23] and CHiCAGO [6] as described in [8]. We noted that the technical noise component had a generally small contribution compared to the biological noise (Additional file 2: Figure S8). We therefore applied NB regression alone to the raw counts using standard software to generate these standardised residuals, which we call NB residuals in the text below. This allowed us to add additional covariates to the regression which we found provided small improvements to the model fit. Besides the distance between an interaction’s bait and prey fragments, and whether both fragments in a putative contact were baited, we used the length of both fragments, as well as transchromosomal bait activity, as covariates. Assuming that transchromosomal contacts are equally rare across baits [24], the latter is a proxy for enrichment and capture biases. To account for the difference in the number of possible transchromosomal interaction sites between baits on different chromosomes, transchromosomal bait activity is defined for each bait as the residual following from the regression of the sum of its transchromosomal counts against its chromosome number. We used the R package GAMLSS to fit zerotruncated NB models to counts for each pair of bait (b) and prey (p) within ten distance bins (Additional file 2: Table S3). Assuming most baitprey fragment pairs do not make direct contacts, the null model can be parametrized using the full dataset. We used normalized, randomized quantile residuals [25] as the input for our joint model. A comparison between the predicted fits from CHiCAGO and our NB models applied to the same data showed a good correspondence (Additional file 2: Figure S9).
Priors on model parameters
For each bait b, the nonzero strengths were assigned independent normal priors centered on 0, with a common variance \(\sigma ^{2}_{\beta }\):
Rather than fixing σ_{β}, which controls the magnitude of interaction strengths supported by the model, and therefore can have an important impact on the efficiency of the algorithm, we use a flexible hyperprior allowing adaption to the data. Specifically, we placed a weakly informative Uniform(0.01,2) hyperprior on σ_{β}. The median, σ_{β}=1, corresponds to 95% support for interaction strengths up to a plausible 1.96. However, this hyperprior equally supports much smaller values of σ_{β}, as well as values up to the maximum of 2, corresponding to support for interaction strengths as large as 8 — marginally larger than any individual NB residual we observed (Additional file 2: Figure S1).
Our model selection framework is completed by specifying a prior for each γ_{bp}. To avoid problems of overfitting from simultaneous estimation of too many interaction parameters, and because we believe a priori that direct contacts only exist at a small proportion of prey fragments, our prior on γ_{bp} is designed to encourage a “sparse” selection of interactions. To this end we first define θ_{b}, the expected proportion of prey fragments which contact b, i.e. \(\frac {1}{F_{b}}\sum _{q} \gamma _{bq}\), which has prior distribution
Conditional on θ_{b}, each γ_{bq} is then i.i.d. Bernoulli(θ_{b}). This prior has two attractive properties. First, the marginal prior odds that a particular fragment interacts with b is 1/F_{b}, and therefore decreases with the total number of fragments considered. Meanwhile, the prior odds for there being no interactions is a constant 0.5 for every bait. This setup provides an intrinsic multiplicity correction for the number of fragments in each bait, and allows fair comparison of inference across baits, due to the common prior on the null model [26]. Note too that this corresponds to a very small prior odds of interaction for each individual fragment, since F_{b} is usually in the order of 3000, and thereby encourages the exploration of sparse models.
Model fitting via reversible jump MCMC
For bait b, the Reversible Jump MCMC [27] sampling scheme starts at an initial set of interactions, γ_{b}, and corresponding strengths, β_{b}, denoted γ_{b}(0) and β_{b}(0) respectively. Bold symbols are used to denote that these are vectors across all fragments in the neighbourhood of bait b. To sample the next set of interactions and strengths, which we denote γ_{b}(1) and β_{b}(1), we propose moving from the current state to another combination of interactions and/or set of strengths, γ_{b}∗ and \(\boldsymbol \beta _{b}^{*}\), by using a proposal function \(g(\boldsymbol \gamma _{b}*, \boldsymbol \beta _{b}^{*}  \boldsymbol \gamma _{b}, \boldsymbol \beta _{b})\). We then accept these proposed values as the next sample with probability equal to the Reversible Jump MetropolisHastings ratio:
where Y_{b} are the residuals of all fragments captured for bait b, P(Y_{b}.) is the model described by (1) and (3). π(β_{b}γ_{b}) is the prior distribution of the strengths defined in (4) conditional on the corresponding interactions being included in the model. p(γ_{b}) is the model space prior defined in (5). Therefore the proposed combination of interactions and new strength values are accepted with a probability proportional to their likelihood and prior. If this new set of values is accepted, the proposed set is accepted as γ_{b}(1) and β_{b}(1); otherwise, the sample value remains equal to the current sample value, i.e., γ_{b}(1)=γ_{b}(0) and β_{b}(1)=β_{b}(0). It can be shown that this produces a sequence of parameter samples that converge to the required posterior distribution [27]. We used the Reversible Jump MCMC implementation in R2BGLiMS (https://github.com/pjnewcombe/R2BGLiMS, [16]), to fit the model described above to each bait in turn. Because our aim was to fit this model to 28,214 baits, we took some time to define a strategy for thinning these samples in order to perform reliable posterior inference on γ_{b} while minimising the computational burden (Additional file 1). This led us to run R2BGLiMS sampling 5000 models per chain, at a density of 1 per 1000 iterations, with no burnin. We ran two parallel chains for each bait, and checked convergence between MPPC derived from each chain. If this was <0.75, we ran a further 5000 samples to improve convergence. Autocorrelation plots were also used to evaluate model space exploration for individual baits (Additional file 2: Figure S10).
Assessing relationship of CHiCAGO scores and MPPC to outcome measures
CHiCAGO scores are nonnegative real numbers, and are typically asinh transformed for presentation or downstream inference, to prevent overleverage of points in the extreme right of the distribution [8]. In constrast, MPPC lies between 0 and 1, although rarely reaches 1 in practice. We found MPPC were generally positively correlated with CHiCAGO scores, with the relationship closest to linear when sqrt(MPPC) was compared to asinh(CHiCAGO) (Additional file 2: Figure S4). We therefore use a square root transform in following analyses to perform a fair comparison with CHiCAGO scores.
We defined the following four outcome measures:

Promoter: match to [17] For validation with external promoterenhancer networks, we used the positions given in http://yiplab.cse.cuhk.edu.hk/jeme/encoderoadmap_lasso/encoderoadmap_lasso.34.csv (accessed 2017/09/11). We used GenomicRanges to identifying baitprey fragment pairs which overlapped the paired coordinates given in this file, and set a binary outcome 1 if such an overlap was found and 0 otherwise. Analyses of this measure were restricted to prey fragments within 200 kb of the bait, because 95% of these reported links were within that range.

Promoter: link to active chromatin These cells had previously been assayed by ChIPseq, and a 15 state CHROMHMM model fitted. 8 of these states showed characteristics of “active chromatin” and we combined these into a binary measure for active or inactive chromatin [7]. We used these results to quantify the overlap, for each prey fragment, with regions of active chromatin. For the most part (∼90%), a fragment showed complete overlap or lack of overlap with active chromatin regions, in which case the outcome measures was set to 1 or 0 respectively. To allow logistic regression of this mainly binary outcome, the observations with fractional overlap were set to missing for analysis.

Validation: overlap baited promoter For a measure of promoter overlap, we used the binary indicator of whether a prey fragment in the validation experiment had also been baited in the promoter experiment.

Validation: expression at linked promoter Given evidence that recruitment of prey fragments is associated with increased expression of the baited gene [7], we expected that, amongst prey that did correspond to a baited promoter in the promoter capture experiment, the level of expression of the target gene should be higher when there was a direct contact. RNAseq has previously been used to quantify transcription in these cells, and we used the expression of the target gene (log_{2}(count + 1)) as an outcome measure in linear regression. Analyses of this measure were restricted to baitprey pairs where the prey corresponded to a gene promoter.
Because each prey fragment is represented multiple times (with different baits), we assessed the relationship between asinhtransformed CHiCAGO scores and sqrttransformed MPPC with each outcome measure using robust clustered linear or logistic regression implemented in the R library rms (https://cran.rproject.org/web/packages/rms/), clustering on the prey fragment.
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Acknowledgments
We thank Frank Dudbridge and Mikhail Spivakov for helpful discussions throughout the development of our method.
Funding
This work was funded by the MRC (MC_UU_00002/4, MC_UU_00002/9) and the Wellcome Trust (WT107881).
Availability of data and materials
Data used is available as described in the primary publications and as Additional Files (see below). CHiC data, inferred CHROMHMM states, RNAseq quantification are available as described in [7]. We downloaded summaries of the enhancerpromoter networks [17] from http://yiplab.cse.cuhk.edu.hk/jeme/encoderoadmap_lasso/encoderoadmap_lasso.34.csv. We provide functions to implement this model in the R package Peaky, available from http://github.com/cqgd/pky. Code used to run peaky on these data, and generate the tables and figures in this paper are available at https://github.com/chr1swallace/eijsboutsetal. All raw data used in are available in Additional file 3.
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Contributions
CE developed the statistical method, implemented it, applied it to the CHiC datasets, wrote the paper, authored the software. OB annotated gene TSS and CHiC HindIII fragments, critically evaluated the enrichment analysis, wrote the paper. PN developed the statistical method, wrote the paper. CW devised the study, developed the statistical method, performed statistical enrichment analyses, wrote the paper. All authors read and approved the final manuscript.
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Ethics approval and consent to participate
No new data were generated in this study. All data comes from a previous study [7] for which all samples and information were collected with written and signed informed consent. The study was approved by the local Peterborough and Fenland research ethics committee for the project entitled: ’An investigation into genes and mechanisms based on genotypephenotype correlations in type 1 diabetes and related diseases using peripheral blood mononuclear cells from volunteers that are part of the Cambridge BioResource project’ (05/Q0106/20). Experimental methods comply with the Helsinki Declaration.
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Not applicable.
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The authors declare that they have no competing interests.
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Additional files
Additional file 1
Supplementary Note. Detailed description of the statistical model underlying peaky, and parameter choices. (PDF 1650 kb)
Additional file 2
Supplementary Figures and Tables. (PDF 4180 kb)
Additional file 3
Supplementary Data. The processed CD4 and macrophage data are in supplementary data suppdata.tgz. This gzipped archive contains .csv files (suppdata*.csv), one for each experiment, with bait and prey hindIII fragment IDs together with columns:
∙ N read count
∙ residual NB residual
∙ mppc MPPC
∙ beta.post posterior expectation of β
∙ chicago CHiCAGO score (only for CD4 cells)
and two annotation files:
∙hindpositions.csv gives the chromosome coordinates of each hindIII fragment
∙bait2gene.csv gives the links between promoter baits and annotated genes, using ensembl 75. (ZIP 673,425 kb)
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Eijsbouts, C.Q., Burren, O.S., Newcombe, P.J. et al. Fine mapping chromatin contacts in capture HiC data. BMC Genomics 20, 77 (2019). https://doi.org/10.1186/s1286401853145
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Keywords
 Capture HiC
 Chromatin conformation
 Bayesian statistics
 Variable selection