- Open Access
CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data
- Jonathan Cairns†1,
- Paula Freire-Pritchett†1,
- Steven W. Wingett1, 2,
- Csilla Várnai1,
- Andrew Dimond1,
- Vincent Plagnol3,
- Daniel Zerbino4,
- Stefan Schoenfelder1,
- Biola-Maria Javierre1,
- Cameron Osborne5,
- Peter Fraser1 and
- Mikhail Spivakov1Email author
© The Author(s). 2016
- Received: 1 April 2016
- Accepted: 25 May 2016
- Published: 15 June 2016
Capture Hi-C (CHi-C) is a method for profiling chromosomal interactions involving targeted regions of interest, such as gene promoters, globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments. We implement these procedures in CHiCAGO (http://regulatorygenomicsgroup.org/chicago), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs.
- Gene regulation
- Nuclear organisation
- Promoter-enhancer interactions
- Capture Hi-C
- Convolution background model
- P value weighting
CHi-C data possess statistical properties that set them apart from other 3C/4C/Hi-C-like methods. First, in contrast to traditional Hi-C or 5C, baits in CHi-C comprise a subset of restriction fragments, while any fragment in the genome can be detected on the “other end” of an interaction. This asymmetry of CHi-C interaction matrices is not accounted for by the normalisation procedures developed for traditional Hi-C and 5C [8–10]. Secondly, CHi-C baits, but not other ends, have a further source of bias associated with uneven capture efficiency. In addition, the need for detecting interactions globally and at a single-fragment resolution creates specific multiple testing challenges that are less pronounced with binned Hi-C data or the more focused 4C and 5C assays, which involve fewer interaction tests. Finally, CHi-C designs such as Promoter CHi-C and HiCap [3–5, 11] involve large numbers (many thousands) of spatially dispersed baits. This presents the opportunity to increase the robustness of signal detection by sharing information across baits. Such sharing is impossible in the analysis of 4C data that focuses on only a single bait and is of limited use in 4C-seq containing a small number of baits [12–14].
These distinct features of CHi-C data have prompted us to develop a bespoke statistical model and a background correction procedure for detecting significant interactions in CHi-C data at a single restriction fragment resolution. The algorithm, termed CHiCAGO (“Capture Hi-C Analysis of Genomic Organisation”), is presented here and implemented as an open-source R package. CHiCAGO features a novel background correction procedure and a two-component convolution background model accounting for both real, but expected, interactions as well as assay and sequencing artefacts. In addition, CHiCAGO implements a weighted false discovery control procedure that builds on the theoretical foundations of Genovese et al. . This procedure specifically accommodates the fact that increasingly larger numbers of tests are performed at regions where progressively smaller numbers of interactions are expected.
Methodological foundations of CHiCAGO
A convolution background model for Hi-C data
In addition to Brownian collisions, background in CHi-C is generated by assay artefacts, such as sequencing errors. We model this “technical noise” component as a Poisson random variable whose mean depends on the properties of interacting fragments but is independent of genomic distance between them.
We further assume that these two sources of background counts are independent. Therefore, the combined background distribution can be obtained as a convolution of negative binomial (Brownian collisions) and Poisson (technical noise) distributions that is known as the Delaporte distribution.
We first construct this null distribution from the data in a robust way, based on all possible fragment pairs (including those that have zero observed read counts). We then find the pairs with counts that greatly exceed the expected background level (Fig. 2; as described in the next section). The full mathematical specification of the algorithm is given in Additional file 1.
Background estimation in asymmetrical interaction matrices
A practical advantage of the two-component background model is that the Brownian and technical normalisation factors can be estimated on separate subsets of data, each of which predominantly represents only one background component.
The bait-specific factors reflect the technical biases of both Hi-C and sequence capture, as well as local effects such as chromatin accessibility. We estimate these factors in a way that is robust to the presence of a small fraction of interactions in the data. Figure 4a provides examples of three baits with very diverse bias factors, illustrating that local read enrichment correlates with the bias factor.
Estimating other end-specific bias factors poses a challenge, as the majority of interactions are removed at the capture stage that enriches for only a small subset of interactions with baits. We assume that the overall fragment-level read count corresponding to trans-chromosomal pairs primarily reflects the general “noisiness” of a fragment (a similar approach has been taken independently in Dryden et al. ). While we do not preclude the presence of individual trans-chromosomal interaction signals, our reasoning that the overall per-fragment levels of trans-chromosomal pairs are dominated by noise is supported by evidence from Hi-C and random ligation control data (Additional file 2: Figure S2). We therefore pool fragments according to this property and estimate bias factors for each pool. As expected, bias factors are higher for fragments associated with higher numbers of trans-chromosomal read pairs (Fig. 4c). Similarly, baits detected at the “other ends” of bait-to-bait pairs had higher background levels than non-baits, as expected given the preferential recovery of “double-baited” ligation products at the capture stage.
In parallel, we compute the dependence between the Brownian background component and linear chromosomal distance (plotted in Fig. 4b for GM12878 CHi-C data). It can be seen that this dependence approximately follows a piecewise power law, consistent with previous studies on the subject, both theoretical and experimental [18, 19]. We further show by cross-validation that the estimate of this dependence is stable (Additional file 2: Figure S3) and, therefore, unlikely to be influenced by bait-specific or interaction-specific signals.
To estimate the magnitude of technical noise, we again use the per-fragment total trans-chromosomal read pairs (see “Methods”). In doing so, we assume that the contribution of true signals from specific trans-chromosomal looping interactions, as well as from Brownian collisions between chromosomes to the total trans-chromosomal counts, is negligible for the reasons outlined above (Additional file 2: Figure S2). Indeed, as we see in Fig. 4d, the expected level of technical noise is typically a small fraction of a count.
The estimated parameters of both background components are then combined into the Delaporte distribution. In Additional file 2: Figure S4 we show evidence that CHiCAGO’s parameter estimation procedures are robust in the presence of undersampling; the implications of undersampling in CHi-C data are further examined in the “Discussion”. After appropriate normalisation and bias correction, we detect fragment pairs showing read coverage higher than expected under the Delaporte assumptions with a one-tailed hypothesis test.
Weighted multiple testing correction for Capture Hi-C
To address this issue, the long-range and trans-chromosomal interaction tests need to be more stringent than the short-range ones. We achieve this with an approach based on p value weighting [15, 20]. This procedure permits a smooth change of behaviour with distance, thereby bypassing the need to choose a hard distance threshold. Briefly, we assign each fragment pair a weight, estimating how probable it is that the fragments interact. The weights are then used to adjust the p values (see Additional file 1 for full specification). P value weighting can be seen as a simplified version of the empirical Bayesian treatment, with weights related to prior probabilities. One practical advantage of this method for our framework is that it avoids the need to make specific assumptions about the read count distribution of true interactions, which would be required for computing Bayes factors.
The optimal choice of weights depends on the relative abundance of true positives at each bait–other end distance. We estimate this abundance by assessing reproducibility across samples and fitting a bounded logistic curve to the observed reproducibility levels at different distances. Generally similar weight profiles were obtained in GM12878 cells and mESCs, and swapping them between these two datasets yielded highly correlated score profiles (Fig. 5a; Additional file 2: Figure S5). This is consistent with our expectation that weights are largely independent of specific cell type and organism given comparable genome sizes, as they predominantly reflect the overall distance distribution of true interactions. Emerging multi-replicate CHi-C datasets will further refine our weight estimates and enable a more comprehensive assessment of their dependence on the particulars of the model system.
We illustrate the impact of the weighting procedure on GM12878 and mESC CHi-C data by comparing the properties of the 100,000 top-scoring interactions, called either with or without weighting. The reproducibility of interaction calls decreases with bait–other end distance (Fig. 5a; Additional file 2: Figure S5a). As a result, the “weighted” significant interactions generally span a much shorter range than the unweighted ones (Fig. 5b; Additional file 2: Figure S5b). This is consistent with the biological expectation that promoter-interacting regions, such as enhancers, are enriched in the relative vicinity of their targets. Another consequence of the weighting procedure is that the average read count is much higher in the weighted calls (Fig. 5c; Additional file 2: Figure S5c). Strikingly, many of the unweighted calls are based on only one read pair per interaction. As the vast majority of fragment pairs attract no reads at all, low p values for single read-pair interactions are expected. However, due to the very large number of possible fragment pairs (approximately 18.5 billion in both the GM12878 and the mESC data), we still expect thousands of single read-count calls to be generated by technical noise. These spurious calls, the majority of which correspond to trans-chromosomal pairs (Fig. 5d; Additional file 2: Figure S5d), are generally non-reproducible and are therefore excluded by the weighting procedure.
In conclusion, the p value weighting procedure implemented in CHiCAGO provides a multiple testing treatment that accounts for the differences in true positive rates at different bait–other end distances, thus improving the reproducibility of interaction calls.
Promoter interactions detected by CHiCAGO: validation and key properties
The properties of CHiCAGO-detected interactions in human lymphoblastoid cell line GM12878 and mESCs
Number of captured baits
Total number of unique captured read pairs
Rep 1: 46,542,745
Rep 1: 59,963,697
Rep 2: 118,813,226
Rep 2: 82,026,534
Rep 3: 73,881,698
Number of significant interactions
Mean number of significant interactions per bait
Median distance of cis-chromosomal interactions
Enrichment for regulatory features
We first assessed the enrichment of promoter-interacting fragments for histone marks associated with active (H3K4me1, H3K4me3, H3K27ac) and repressed (H3K27me3, H3K9me3) chromatin, as well as for the binding sites of CTCF, a protein with a well-established role in shaping nuclear architecture . To this end, we compared the observed and expected numbers of promoter-interacting fragments overlapping with these features. To estimate the expected degree of overlap, we drew multiple permutations of the promoter–other end pairs not detected as interacting, such that the overall distribution of their spanned distances matched the distribution for the true interactions.
Assessing the enrichment of promoter-interacting fragments for known regulatory features can serve as a useful quality control for CHi-C samples. To this end, CHiCAGO automatically generates enrichment bar plots similar to Fig. 6 for each sample, integrating interaction calls with user-specified genomic annotations, such as ChIP-seq peaks.
Enrichment for genome-wide association study SNPs
The majority of disease-associated SNPs identified in genome-wide association studies (GWAS) localise to non-coding regulatory regions, away from annotated promoters, posing a significant challenge in identifying their putative target genes . We asked whether promoter-interacting regions detected by CHiCAGO in human cells are enriched for GWAS SNPs, which would potentially reflect their presence in long-range regulatory sequences and thus suggest a putative functional role in disease.
We assessed the enrichment of promoter-interacting regions in GM12878 cells for sets of GWAS catalogue SNPs from Maurano et al. . These sets reflect the grouping of GWAS traits into broader categories, such as autoimmune disease (AI), neurological/behavioural traits (NB) and kidney/liver/lung disorders (KLL). We used the software package GoShifter (Genomic Annotation Shifter) , which infers the significance of overlap by locally shifting genomic annotations (in our case, the “other ends” of CHiCAGO-detected promoter interactions), thus reducing the effect of genomic biases and linkage disequilibrium structure. We observed a significant enrichment of CHiCAGO “other ends” for SNPs associated with autoimmune diseases (GOShifter p = 0.001) but not with kidney/liver/lung disorders (p = 0.876) or neurological/behavioural traits (p = 0.742). This selective enrichment for autoimmune SNPs is consistent with GM12878 being a lymphocyte-derived cell line and replicates the original findings of Mifsud et al. .
Taken together, these results demonstrate the power of using CHi-C data to link GWAS SNPs with their putative target genes in a cell type-specific and high-throughput manner. We expect this to be one of the key applications of CHi-C in future clinical studies.
Capability to drive transgene expression in vivo
TRIP (Thousands of Reporters Integrated in Parallel) is a novel experimental technique to assess the influence of local chromatin context on gene expression. In TRIP analysis, a barcoded transgene reporter is randomly integrated into thousands of genomic locations in parallel and the transcriptional activity at each location is then monitored. Here we integrated the published TRIP analysis dataset in mESCs  with the CHiCAGO mESC calls , comparing the transcriptional activity at promoter-interacting regions with the activity elsewhere, over a range of genomic distances.
Interactions where both fragment ends are baited (referred to as “bait-to-bait interactions”) represent contacts between gene promoters. These interactions are of special interest because they may help to identify sets of co-regulated genes recruited to either shared transcription factories  or repression networks such as those mediated by Polycomb proteins .
Extremely long-range promoter interactions map within broader Hi-C contact regions
In this paper, we present the CHiCAGO algorithm for Capture Hi-C analysis and demonstrate its efficacy in detecting interactions enriched for regulatory chromatin features and relevant GWAS SNPs.
Our approach is based on the assumption that “significant” interactions emerge as outliers on a distance-dependent local background profile. This assumption is shared by most other tools for interaction detection in 3C-like data and seems reasonable for the purposes of identifying regulatory interactions. Indeed, it can be expected that regulatory events such as transcription factor binding will stabilise the chromatin loop, leading to interaction frequencies or retention times beyond those generated by random collisions due to Brownian motion. This expectation is supported by the observation that CHiCAGO-detected interactions are selectively enriched for regulatory chromatin features, even when located in regions with high background interaction levels.
While the conceptual interpretation of “significant” interactions is shared between CHiCAGO and algorithms developed for other types of 4C and Hi-C data, there are key differences in terms of the underlying background model, the normalisation strategy and the multiple testing procedure.
Existing tools model Hi-C background with a broad range of distributions, both discrete (binomial [16, 29], negative binomial ) and continuous (Weibull [7, 9], normal ). In CHiCAGO, we instead opted for a two-component convolution model that incorporates two count distributions: a negative binomial and a Poisson. In doing so, we were motivated by the fact that distance-dependent Brownian collisions and technical variability are two distinct background count-generating processes whose properties are best learned separately on different subsets of data. Indeed, signals from Brownian collisions ostensibly dominate the background at short distances, to the extent that technical variability is barely detectable. In contrast, at large linear distances between fragments, Brownian collisions are too weak for their count distribution to be estimated directly. Thus, we infer this distribution by extrapolation.
Borrowing information across baits to learn the background model, as CHiCAGO does, requires careful normalisation across interactions. While Hi-C background depends on a number of known parameters, such as fragment length and GC content , we, along with others [7, 8, 30], have opted to avoid any specific assumptions about noise structure, particularly given the increased complexity and asymmetric nature of capture Hi-C noise compared with conventional Hi-C. Assuming that interactions are subject to multiplicative bait- and other-end-specific bias, as we did in learning the Brownian background component, parallels the assumptions of the Hi-C iterative correction approach by Imakaev et al.  and is generally consistent with data from molecular dynamics simulations of chromatin fibres . In modelling technical noise, we assumed it to be reflected in the numbers of trans-chromosomal interactions involving the same fragment. A similar strategy has been applied independently in a recently published Capture Hi-C study ; the same authors also proposed an iterative correction algorithm for Capture Hi-C data  (software not publicly released) that may complement the approaches taken here.
Multiple testing issues are important in genomic analyses and, in attempting to address these issues, a number of bespoke approaches have been developed [20, 31]. The specific challenge of multiple testing in Hi-C data is that we expect the fractions of true positives to vary depending on the genomic distance between the fragments; in fact, the majority of tests are performed with interactions spanning large distances or spanning different chromosomes, where true positive signals are least expected. CHiCAGO’s multiple testing procedure is based on the p value weighting approach by Genovese et al. , which is a generalisation of a segment-wise weighting procedure by Sun et al. . These approaches have been used successfully to incorporate prior knowledge in GWAS [33–35] and are emerging in functional genomics analyses [36, 37]. In using the reproducibility of significant calls across replicates as an estimate of the relative true positive rate, we have taken inspiration from the irreproducible discovery rate (IDR) approach  used to determine peak signal thresholds in other types of genomics data, such as ChIP-seq.
Note that, in this setting, IDR cannot be used verbatim for choosing signal thresholds, as the relationship between Capture Hi-C signal and reproducibility does not satisfy IDR assumptions, likely because of undersampling issues (not shown). Importantly, conventional false discovery rate (FDR)-based approaches for multiple testing correction  are also unsuitable for these data. Indeed, CHi-C observations (read-pair counts) are discrete and many of them are equal to either zero or one. This leads to a highly non-uniform distribution of p values under the null, violating the basic assumption of conventional FDR approaches. The “soft-thresholding” approach used in CHiCAGO shifts the −log-weighted p values such that non-zero scores correspond to observations, where the evidence for an interaction exceeds that for a pair of near-adjacent fragments with no reads. More robust thresholds can then be chosen based on custom criteria, such as maximising enrichment of promoter-interacting fragments for chromatin features (Fig. 6; a user-friendly function for this analysis is provided as part of the Chicago R package—see the package vignette provided as Additional file 3). Based on this approach, we chose a signal threshold of 5 for our own analyses.
The undersampled nature of CHi-C data (particularly at longer distance ranges), although robustly handled by CHiCAGO, may lead to significant sensitivity issues when using thresholded interaction calls in comparative analyses. We therefore suggest performing comparisons based on the continuous score range. Potentially, differential analysis algorithms for sequencing data (such as DESeq2 ) may also be used to formally compare the enrichment at CHiCAGO-detected interactions between conditions at the count level, although power will generally be a limiting factor. As undersampling drives down the observed overlap of interactions called on different samples (Additional file 2: Figure S4c), methods such as [41, 42] may be considered for formally ascertaining the consistency between datasets. Additional filtering based on the mean number of reads per detected interaction (e.g., removing calls whose mean N is below 10 reads) will also reduce the impact of undersampling on the observed overlap, but at the cost of decreasing the power to detect longer-range interactions.
The p value weighting approach used here is similar in spirit to an empirical Bayesian treatment, with the p value weights related, but not identical, to prior probabilities. Bayesian approaches are widely used (including, recently, for signal detection in conventional Hi-C ) and the Bayes factors and posterior probabilities they generate are potentially more intuitive than weighted p values. However, the p value weighting approach used here has the advantage of not making any specific assumptions about the read distributions of “true interactions”, beyond their having a larger mean. Both approaches open the opportunity of incorporating prior knowledge, beyond the dependence of reproducibility on distance—for example, taking into account the boundaries of topologically associated domains (TADs) , higher-order contact domains and chromosomal territories. We choose not to do this currently because the exact relationship between these genomic properties and looping interactions still requires further investigation, and incorporating these relationships a priori prevents their investigation in post hoc analyses. Active research in this area makes it likely that much more will be known about the determinants of loop formation in the near future, enabling a more extensive use of prior knowledge in interaction detection, potentially with a formal Bayesian treatment.
The downstream analyses of CHiCAGO results provided in this paper confirm the enrichment of promoter-interacting regions for regulatory features and disease-associated variants. These results demonstrate the enormous potential of CHi-C for both functional genomics and population genetics, and this assay will likely be applied in multitudes of other cell types in the near future. Therefore, user-friendly, open-source software for robust signal detection in these challenging data will be a welcome addition to the toolkits of many bioinformaticians and experimentalists alike. We have developed CHiCAGO with the view of addressing this need. Furthermore, we expect the statistical foundations of CHiCAGO, particularly the convolution background model and the multiple testing procedure, to be potentially useful in a broader range of Hi-C-related assays.
The publicly available, open-source CHiCAGO pipeline presented here  produces robust and interpretable interaction calls in CHi-C data. Promoter-interacting fragments identified using this algorithm are enriched for active chromatin features, GWAS SNPs and regions capable of driving transgene expression, indicative of regulatory looping interactions. While developed specifically for CHi-C, the statistical principles of CHiCAGO are potentially applicable to other Hi-C-based methods.
The publicly available HiCUP pipeline [46, 47] was employed to process the raw sequencing reads. This pipeline was used to map the read pairs against the mouse (mm9) and human (hg19) genomes, to filter experimental artefacts (such as circularized reads and re-ligations) and to remove duplicate reads. For the CHi-C data, the resulting BAM files were processed into CHiCAGO input files, retaining only those read pairs that mapped, at least on one end, to a captured bait. The script bam2chicago.sh, used for this purpose, is available as part of the chicagoTools suite .
The CHiCAGO algorithm
Briefly, to combine replicates, a “reference” replicate is created by taking the geometric mean of each fragment pair’s count across samples. Sample size factors are calculated by taking the mean ratio to the “reference” replicate, in a manner similar to the sample normalisation strategy implemented in DESeq . Final counts are derived as the rounded weighted sum of counts across replicates, where the weights are the sample size factors.
Background from Brownian collisions is assumed to have negative binomial distribution, with mean s i s j f(d ij ) and dispersion r, where i indexes over other ends and j indexes over baits.
Estimation of s i , s j , f(d) and r is performed in “proximal bins”—by default, 20-kb bins that span the first 1.5 Mb around each bait.
For each bait, take all of the other ends in a distance bin to get a mean count for that bin.
f(d) is estimated in a distance bin by taking the geometric mean of the bin counts at that distance, across all baits.
To interpolate f(d) from these point estimates, we use a maximum likelihood cubic fit on a log–log scale.
Outside of this distance range, we extrapolate linearly, assuming continuity of f and its first derivative.
The bait-specific scaling factors, s j , are estimated by considering each mean bin count divided by f(d), then taking the median of this ratio, across all bins associated with a bait. The other end-specific scaling factors, s i , are estimated similarly but with the other ends pooled together (the pools are chosen such that their content ends have similar numbers of trans-chromosomal counts) so that there is enough information for a precise estimate. The dispersion, r, is estimated using standard maximum likelihood methods.
The technical noise is assumed to have Poisson distribution, with mean λ ij . λ ij is estimated from trans-chromosomal counts—again, first pooling fragments by the number of trans-chromosomal counts they exhibit. Specifically, to estimate the technical noise level for a putative interaction between a bait in pool A and an other end in pool B, we count the number of interactions that span between pools A and B and divide this by |A||B|, the total number of bait–other end fragment pairs from those pools.
P values are called with a Delaporte model, representing the sum of two variables: a negative binomial variable with mean s i s j f(d ij ) and dispersion r, and a Poisson variable with mean λ ij . A four-parameter bounded logistic regression model is assumed for p value weighting (see the next section and Additional file 1 for more information).
The final CHiCAGO score is obtained from soft-thresholding the −log(weighted p value). Specifically, the score is max(−log(p) + log(w) − log(w max ), 0), where w max is the maximum attainable weight, corresponding to zero distance. For the downstream analyses in this paper, interactions with CHiCAGO scores ≥5 were considered as “significant interactions”.
P value weighting parameter estimation
The p value weighting function has four parameters: α, β, γ and δ (full details are given in Additional file 1). We can estimate these parameters from a candidate data set provided that it has multiple biological replicates, as follows. We split the data into subsets that contain approximately equal numbers of baits (by default, five subsets are used.) The reproducible interactions are defined as those where the stringent threshold of log(p) < −10 is passed in all biological replicates. Now, for each subset, we take a series of genomic distance bins (with the default breaks occurring at 0, 31.25 kb, 62.5 kb, 125 kb, 250 kb, 500 kb, 1 Mb, 2 Mb, 3 Mb, 4 Mb, …, 16 Mb), and we calculate the proportion of reproducible interactions out of the total number of possible interactions. The maximum likelihood estimates are calculated for each model parameter using standard optimization methods . Final parameter estimates are obtained by taking the median across the estimates from each subset. The two replicates of mESC data  were used for estimating weights. For GM12878 , the first replicate was not used for weight estimation as it led to unstable estimation. This was likely due to the poorer quality of this replicate compared with the other two, consistent with its higher cis/trans-chromosomal count ratios (data not shown). Recommendations on diagnosing unstable estimates are provided in the R package vignette (Additional file 3).
The Chicago R package
CHiCAGO was implemented as a package for the statistical environment R  taking advantage of the data.table objects  to optimise for both speed and memory. The fully documented R package “Chicago” and the tutorial data package “PCHiCdata” are publicly available  under Artistic Licence 2.0 and are part of Bioconductor release 3.3+ [52, 53]. A documented set of supplementary scripts (chicagoTools) for data pre- and post-processing and running Chicago in batch mode is also publicly available . Chicago v1.0.1 was used in this paper.
A typical Chicago job for two biological replicates of CHi-C data takes 2–3 h wall-clock time (including sample pre-processing from bam files using chicagoTools) and uses 50 GB RAM. An example workflow in the form of an R package vignette is provided as Additional file 3. The description of free parameters and rationale for their settings is given in Additional file 2: Table S1.
Assessment of feature enrichment
Enrichment for chromatin features at CHi-C interacting regions was assessed with respect to random HindIII fragments drawn in such a way as to match the distribution of the observed interaction distances. A 95 % confidence interval for the expected overlap was obtained from 100 random draws. SNP enrichment at promoter interacting fragments was assessed using GoShifter .
HOMER  was used to compute binned coverage- and distance-related background in the Hi-C data and call significantly interacting bin pairs. Short-range cis-chromosomal interactions (<1 Mb) were detected in 25-kb bins; long-range cis-chromosomal (>1 Mb) and trans-chromosomal interactions were detected in 1-Mb bins. Bin pairs with FDR-adjusted p < 0.05 were considered significant. The significance of overlap between CHi-C promoter-interacting regions identified by CHiCAGO and the HOMER-detected interacting bin pairs in the Hi-C data was ascertained by permutation, while preserving the structural features of the data, as follows. Cis-chromosomal interactions were permuted across the baits while preserving the interaction distances. Trans-chromosomal interactions were permuted across chromosomes while preserving the relative chromosomal position of the interacting fragments.
Raw CHi-C, Hi-C and random ligation control data used in this study are available in ArrayExpress [54, 55] under accession numbers E-MTAB-2323 (GM12878) and E-MTAB-2414 (mESC), respectively. CHiCAGO experiment design files and output files produced with default package settings for GM12878 and mESCs are available through the Open Science Framework . The interaction calls and raw reads for both cell types (score ≥5) have also been submitted to the NCBI Gene Expression Omnibus under accession number GSE81503 .
The authors would like to thank Simon Andrews, Chris Wallace, Oliver Burren and all members of the Spivakov, Fraser and Babraham Bioinformatics groups for helpful discussions. We are grateful to all our “wet-lab” collaborators (in particular, Mayra Furlan-Magaril, Mattia Frontini, Peter Rugg-Gunn and Willem Ouwehand) for using and testing CHiCAGO. This work has been funded by the Biotechnology and Biological Sciences Research Council and the Medical Research Council of the UK; DZ is funded by the European Molecular Biology Laboratory. Finally, we thank Laura Biggins for disambiguating the last two letters of CHiCAGO.
JC, PFP and MS designed the CHiCAGO algorithm; VP and DZ contributed statistical advice; JC, PFP, SWW and MS implemented the algorithm. SS, CO, BMJ and PF generated Capture Hi-C data and advised on their biological properties. PFP, CV, AD, JC and MS performed downstream validation analyses. JC, PFP and MS wrote the paper with critical input from all authors. MS supervised the work. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Ethics approval and consent to participate
Ethics approval was not required for this study.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Dekker J, Marti-Renom MA, Mirny LA. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet. 2013;14:390–403.View ArticlePubMedPubMed CentralGoogle Scholar
- van Steensel B, Dekker J. Genomics tools for unraveling chromosome architecture. Nat Biotechnol. 2010;28:1089–95.View ArticlePubMedPubMed CentralGoogle Scholar
- Mifsud B, Tavares-Cadete F, Young AN, Sugar R, Schoenfelder S, Ferreira L, et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat Genet. 2015;47:598–606.View ArticlePubMedGoogle Scholar
- Schoenfelder S, Furlan-Magaril M, Mifsud B, Tavares-Cadete F, Sugar R, Javierre BM, et al. The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements. Genome Res. 2015;25:582–97.View ArticlePubMedPubMed CentralGoogle Scholar
- Schoenfelder S, Sugar R, Dimond A, Javierre BM, Armstrong H, Mifsud B, et al. Polycomb repressive complex PRC1 spatially constrains the mouse embryonic stem cell genome. Nat Genet. 2015;47:1179–86.View ArticlePubMedPubMed CentralGoogle Scholar
- Dryden NH, Broome LR, Dudbridge F, Johnson N, Orr N, Schoenfelder S, et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C. Genome Res. 2014;24:1854–68.View ArticlePubMedPubMed CentralGoogle Scholar
- Jager R, Migliorini G, Henrion M, Kandaswamy R, Speedy HE, Heindl A, et al. Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci. Nat Commun. 2015;6:6178.View ArticlePubMedPubMed CentralGoogle Scholar
- Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods. 2012;9:999–1003.View ArticlePubMedPubMed CentralGoogle Scholar
- Sanyal A, Lajoie BR, Jain G, Dekker J. The long-range interaction landscape of gene promoters. Nature. 2012;489:109–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Yaffe E, Tanay A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat Genet. 2011;43:1059–65.View ArticlePubMedGoogle Scholar
- Sahlen P, Abdullayev I, Ramskold D, Matskova L, Rilakovic N, Lotstedt B, et al. Genome-wide mapping of promoter-anchored interactions with close to single-enhancer resolution. Genome Biol. 2015;16:156.View ArticlePubMedPubMed CentralGoogle Scholar
- van de Werken HJ, Landan G, Holwerda SJ, Hoichman M, Klous P, Chachik R, et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat Methods. 2012;9:969–72.View ArticlePubMedGoogle Scholar
- Klein FA, Pakozdi T, Anders S, Ghavi-Helm Y, Furlong EE, Huber W. FourCSeq: analysis of 4C sequencing data. Bioinformatics. 2015;31:3085–91.View ArticlePubMedPubMed CentralGoogle Scholar
- Raviram R, Rocha PP, Müller CL, Miraldi ER, Badri S, Fu Y, et al. 4C-ker: a method to reproducibly identify genome-wide interactions captured by 4C-Seq experiments. PLoS Comput Biol. 2016;12:e1004780.View ArticlePubMedPubMed CentralGoogle Scholar
- Genovese CR, Roeder K, Wasserman L. False discovery control with p-value weighting. Biometrika. 2006;93:509–24.View ArticleGoogle Scholar
- Ay F, Bailey TL, Noble WS. Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Res. 2014;24:999–1011.View ArticlePubMedPubMed CentralGoogle Scholar
- Thongjuea S, Stadhouders R, Grosveld FG, Soler E, Lenhard B. r3Cseq: an R/Bioconductor package for the discovery of long-range genomic interactions from chromosome conformation capture and next-generation sequencing data. Nucleic Acids Res. 2013;41:e132.View ArticlePubMedPubMed CentralGoogle Scholar
- Rosa A, Becker NB, Everaers R. Looping probabilities in model interphase chromosomes. Biophys J. 2010;98:2410–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Bohn M, Heermann DW. Diffusion-driven looping provides a consistent framework for chromatin organization. PLoS One. 2010;5:e12218.View ArticlePubMedPubMed CentralGoogle Scholar
- Gui J, Tosteson T, Borsuk M. Weighted multiple testing procedures for genomic studies. BioData Mining. 2012;5:4.View ArticlePubMedPubMed CentralGoogle Scholar
- Ong C-T, Corces VG. CTCF: an architectural protein bridging genome topology and function. Nat Rev Genet. 2014;15:234–46.View ArticlePubMedPubMed CentralGoogle Scholar
- Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Trynka G, Westra H-J, Slowikowski K, Hu X, Xu H, Stranger Barbara E, et al. Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex-trait loci. Am J Hum Genet. 2015;97:139–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Akhtar W, de Jong J, Pindyurin AV, Pagie L, Meuleman W, de Ridder J, et al. Chromatin position effects assayed by thousands of reporters integrated in parallel. Cell. 2013;154:914–27.View ArticlePubMedGoogle Scholar
- Sexton T, Umlauf D, Kurukuti S, Fraser P. The role of transcription factories in large-scale structure and dynamics of interphase chromatin. Semin Cell Dev Biol. 2007;18:691–7.View ArticlePubMedGoogle Scholar
- Ma T, Van Tine BA, Wei Y, Garrett MD, Nelson D, Adams PD, et al. Cell cycle-regulated phosphorylation of p220(NPAT) by cyclin E/Cdk2 in Cajal bodies promotes histone gene transcription. Genes Dev. 2000;14:2298–313.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang Q, Sawyer IA, Sung M-H, Sturgill D, Shevtsov SP, Pegoraro G, et al. Cajal bodies are linked to genome conformation. Nat Commun. 2016;7:10966.View ArticlePubMedPubMed CentralGoogle Scholar
- Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell. 38:576–89.Google Scholar
- Mifsud B, Martincorena I, Darbo E, Sugar R, Schoenfelder S, Fraser P, Luscombe N. GOTHiC, a simple probabilistic model to resolve complex biases and to identify real interactions in Hi-C data. biorXiv preprint. 2015;10.1101/023317.
- Hu M, Deng K, Selvaraj S, Qin Z, Ren B, Liu JS. HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics. 2012;28:3131–3.View ArticlePubMedPubMed CentralGoogle Scholar
- Dudoit S, van der Laan MJ. Multiple testing procedures with applications to genomics. New York: Springer; 2008.View ArticleGoogle Scholar
- Sun L, Craiu RV, Paterson AD, Bull SB. Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies. Genet Epidemiol. 2006;30:519–30.View ArticlePubMedGoogle Scholar
- Lin WY, Lee WC. Improving power of genome-wide association studies with weighted false discovery rate control and prioritized subset analysis. PLoS One. 2012;7:e33716.View ArticlePubMedPubMed CentralGoogle Scholar
- Roeder K, Wasserman L. Genome-wide significance levels and weighted hypothesis testing. Stat Sci. 2009;24:398–413.View ArticlePubMedPubMed CentralGoogle Scholar
- Li L, Kabesch M, Bouzigon E, Demenais F, Farrall M, Moffatt MF, et al. Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma. Front Genet. 2013;4:103.PubMedPubMed CentralGoogle Scholar
- Ignatiadis N, Klaus B, Zaugg JB, Huber W. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat. Methods. 2016; doi:10.1038/nmeth.3885.
- Zhao H, Fung WK. A powerful FDR control procedure for multiple hypotheses. Comput Stat Data Anal. 2016;98:60–70.View ArticleGoogle Scholar
- Li Q, Brown JB, Huang H, Bickel PJ. Measuring reproducibility of high-throughput. Ann Appl Stat. 2011;5:1752–79.View ArticleGoogle Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.Google Scholar
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.View ArticleGoogle Scholar
- Jeffries CD, Ward WO, Perkins DO, Wright FA. Discovering collectively informative descriptors from high-throughput experiments. BMC Bioinformatics. 2009;10:431.View ArticlePubMedPubMed CentralGoogle Scholar
- Blangiardo M, Cassese A, Richardson S. sdef: an R package to synthesize lists of significant features in related experiments. BMC Bioinformatics. 2010;11:1–10.View ArticleGoogle Scholar
- Xu Z, Zhang G, Jin F, Chen M, Furey TS, Sullivan PF, et al. A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data. Bioinformatics. 2016;32:650–6.View ArticlePubMedGoogle Scholar
- Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485:376–80.View ArticlePubMedPubMed CentralGoogle Scholar
- The CHiCAGO home page. http://www.regulatorygenomicsgroup.org/chicago.Accessed 25 May 2016.
- HiCUP. http://www.bioinformatics.babraham.ac.uk/projects/hicup/overview/. Accessed 25 May 2016.
- Wingett S, Ewels P, Furlan-Magaril M, Nagano T, Schoenfelder S, Fraser P, et al. HiCUP: pipeline for mapping and processing Hi-C data. F1000Res. 2015;4:1310.Google Scholar
- Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.View ArticlePubMedPubMed CentralGoogle Scholar
- Nelder JA, Mead R. A simplex method for function minimization. Comput J. 1965;7:308–13.View ArticleGoogle Scholar
- R Development Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2015.Google Scholar
- data.table: Extension of data.frame. http://cran.r-project.org/package=data.table. Accessed 25 May 2016.
- Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–21.View ArticlePubMedPubMed CentralGoogle Scholar
- The Chicago R package on Bioconductor. http://bioconductor.org/packages/release/bioc/html/Chicago.html. Accesseed 25 May 2016.
- ArrayExpress—functional genomics data. https://www.ebi.ac.uk/arrayexpress/. Accessed 25 May 2016.
- Kolesnikov N, Hastings E, Keays M, Melnichuk O, Tang YA, Williams E, et al. ArrayExpress update—simplifying data submissions. Nucleic Acids Res. 2015;43:D1113–6.View ArticlePubMedGoogle Scholar
- Analysis of Promoter Capture Hi-C data for GM12878 and mouse ES cells using the CHiCAGO pipeline. http://osf.io/nemc6. Accessed 18 May 2016.
- NCBI Gene Expression Omnibus. http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/geo/query/acc.cgi?acc=GSE81503. Accessed 25 May 2016.
- WashU Epigenome Browser. http://epigenomegateway.wustl.edu. Accessed 25 May 2016.
- Zhou X, Lowdon RF, Li D, Lawson HA, Madden PA, Costello JF, et al. Exploring long-range genome interactions using the WashU Epigenome Browser. Nat Methods. 2013;10:375–6.View ArticlePubMedGoogle Scholar
- Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods. 2012;9:215–6.View ArticlePubMedPubMed CentralGoogle Scholar
- ENCODE Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.View ArticleGoogle Scholar
- Yue F, Cheng Y, Breschi A, Vierstra J, Wu W, Ryba T, et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature. 2014;515:355–64.View ArticlePubMedPubMed CentralGoogle Scholar