 Methodology Article
 Open Access
Inferring 3D chromatin structure using a multiscale approach based on quaternions
 Claudia Caudai^{1},
 Emanuele Salerno^{1},
 Monica Zoppè^{2} and
 Anna Tonazzini^{1}Email author
 Received: 5 December 2014
 Accepted: 9 July 2015
 Published: 29 July 2015
Abstract
Background
The knowledge of the spatial organisation of the chromatin fibre in cell nuclei helps researchers to understand the nuclear machinery that regulates dna activity. Recent experimental techniques of the type Chromosome Conformation Capture (3c, or similar) provide highresolution, highthroughput data consisting in the number of times any possible pair of dna fragments is found to be in contact, in a certain population of cells. As these data carry information on the structure of the chromatin fibre, several attempts have been made to use them to obtain highresolution 3d reconstructions of entire chromosomes, or even an entire genome. The techniques proposed treat the data in different ways, possibly exploiting physicalgeometric chromatin models. One popular strategy is to transform contact data into Euclidean distances between pairs of fragments, and then solve a classical distancetogeometry problem.
Results
We developed and tested a reconstruction technique that does not require translating contacts into distances, thus avoiding a number of related drawbacks. Also, we introduce a geometrical chromatin chain model that allows us to include sound biochemical and biological constraints in the problem. This model can be scaled at different genomic resolutions, where the structures of the coarser models are influenced by the reconstructions at finer resolutions. The search in the solution space is then performed by a classical simulated annealing, where the model is evolved efficiently through quaternion operators. The presence of appropriate constraints permits the less reliable data to be overlooked, so the result is a set of plausible chromatin configurations compatible with both the data and the prior knowledge.
Conclusions
To test our method, we obtained a number of 3d chromatin configurations from hic data available in the literature for the long arm of human chromosome 1, and validated their features against known properties of gene density and transcriptional activity. Our results are compatible with biological features not introduced a priori in the problem: structurally different regions in our reconstructions highly correlate with functionally different regions as known from literature and genomic repositories.
Keywords
 3d chromatin structure
 Chromosome conformation capture
 Multiscale approach
 Quaternions
Background
The packing of DNA in living cells is obtained through several mechanisms, both general (due to general princples, irrespective of DNA sequence) and specific, i.e. mediated by proteins that recognise specific motifs and bring in close proximity parts of DNA that may be very distant in the genomic sequence. The first level, mediated by histone octamers, produces a fibre of about 11 nm. This fibre, in turn, is supposed to be organised into a 30 nmwide structure, whose existence, however, is still debated [1, 2]. Most current information on packaging is derived from data that are not necessarily consistent with a single conformation, because they are obtained from a pool of cells which are not synchronized, even if they are of the same kind. As a result of the activities involving DNA (transcription, replication, repair, silencing etc.), in different individual cells, DNA organization can be slightly different, while responding to the same general principles. It is also to be kept in mind that DNA is not a rigid entity, and its structure changes from moment to moment in the same cell, both to respond to external stimuli (allowing for either transcription regulation or DNA repairs, if necessary), and to allow for regular compaction, as clearly recognisable at large scale during mitosis. It is well established that, in interphase cells, most chromosomal DNA is organised in ‘chromosome territories’ [3], and it is increasingly apparent that chromosomal organisation is one of the factors involved in regulation of gene function.
A step ahead towards an understanding of this spatial organisation has been enabled by fluorescence insitu hybridisation techniques (FISH [4, 5]), which can be used to locate specific DNA sequences in the genome and measure the distances between pairs of fragments. More recently, Chromosome Conformation Capture (3C, [6]) and a number of related techniques (4C [7], 5C [8], HiC [9, 10]) fostered a major boost in chromatin studies, as they provide highthroughput, highresolution contact data for a full genome at a relatively low cost. The output of each such experiment is a matrix of contact frequencies between pairs of DNA fragments in a uniform population of cells. The average size of the individual fragments depends on the restriction enzymes used. For example, the fragment sizes in the data we use here, obtained by enzyme HindIII, are of about 4 kbp. The raw contact matrices can thus have a very high genomic resolution, but the data come from millions of cells, so stable results can only be obtained by binning the matrices to lower resolutions (typically, 100 kbp). A new experimental protocol [11] applied to individual cells confirms the validity of HiC results, pointing out that the intrachromosomal structures are substantially stable across different cells, whereas a marked variability of interchromosomal interactions has been revealed. Since Chromosome Conformation Capture data carry information about the 3D spatial configuration of the chromatin chain, many research groups in the last decade have been trying to develop specific reconstruction algorithms.
The earliest attempts in this sense used constrained optimisation techniques, mostly looking for an explicit and deterministic relationship between the contact frequencies and the Euclidean distances between pairs of fragments in the 3D conformation [6, 12–14]. The intuitive strength of this choice is that pairs of fragments that are frequently in contact are likely to be spatially close, whatever their genomic distance; vice versa, pairs of fragments with a few contacts are assumed to be farther apart. In [6], a theoretical expression for wormlike chains [15] is adopted, whereas [12] and [13], among others, assume some negativepower relationship between the distances and the contact frequencies. Other approaches include fitting an empirical distancefrequency law to FISH experimental data [16], and using a goldensection search to choose among a parametric family of relationships [14]. In [17], it is proposed to correlate contact frequencies with the presence or absence of chromatin contacts rather than with average distances. Once the distances between all the possible pairs of loci have been determined, the optimisation approaches estimate the bestfit 3D structure from different models, such as piecewise linear curves [12] and beadchain models [13, 16, 18, 19], by also enforcing various constraints derived from known geometric and topological features of the chromatin fibre. In [13] and [16], the constraints are derived from polymer physics. Polymer models for the chromatin fibre have also been proposed in [20–24]. In [11], the 3D structure is obtained by restrained molecular dynamics simulations, at fine or coarse resolutions, where the restraints are flexible target distances derived from the HiC data. In [25–27], polymer models with no frequencydistance conversion are proposed, with different strategies to match the computed and measured contact frequencies.
Simple constrained optimisation in highdimensional applications suffers from known drawbacks, such as trapping in local modes and unaccountability of biases. Moreover, without an explicit probabilistic model accounting for noise, the estimated structures might not be representative of statistically significant conformational features. This motivated the proposal of a number of probabilistic approaches, ranging from Markov Chain Monte Carlo sampling on an unconstrained fragment distribution [28] to a Bayesian approach with Poisson likelihood and uniform prior, also including known biases into the solution model [29, 30]. Again, assuming a deterministic frequencydistance relationship is a popular choice in these approaches. However, [31] proposes a method where distances and contacts are related probabilistically, through a Poisson distribution.
In our view, there are a number of drawbacks that must be overcome to get accurate and reliable 3D reconstructions of the chromatin structure. First of all, we share the concerns about the use of deterministic relationships between contact frequencies and Euclidean distances. If the original contact matrix has null elements, infinite mutual distances can only be avoided if sets of mutually adjacent fragments are binned together until the related contact matrix has all nonzero entries. This sets the genomic resolution achievable well below its theoretical possibilities. Moreover, we checked the topological consistency of the structures obtained from real data through the most popular frequencydistance relationships found in the literature [32] and, as already observed in [33], we found that the distances inferred are often severely incompatible with the Euclidean geometry. Translating contacts into distances is not appropriate for one more reason: two fragments often found in contact are likely to be spatially close in nearly all the configurations assumed by the chromatin, but the converse does not need to be true. Nothing says that two DNA fragments that are seldom in contact are also far from each other.
A second aspect to be considered is the use of a suitable chromatin model to constrain the solution. Enforcing a data fit with no constraint on the mutual positions of the fragments increases tremendously the domain of the feasible solutions, thus decreasing one’s confidence in their plausibility. In [30, 34] no geometric constraint is imposed on the solutions, and yet biologically plausible conformations are found. The price to be paid for this result is the large number of parameters to be estimated and the multiple heuristic sampling processes involved.
The approach we propose in this paper includes a constrained modifiedbeadchain model and a Monte Carlo sampling on a likelihood function built directly from the contact data. This frees us from binning the matrix if not needed to stabilise the data, even though zerovalued entries are left, and avoids the solution of a distancetogeometry problem based on inconsistent data. By direct inspection of the data structure, or from knowledge of confined domains that do not interact with other segments of the genome [27, 35], we can partition the data matrix so that each such domain can be reconstructed separately and then, recursively, lower the resolution to find the spatial relationships between larger and larger chromatin segments with fixed internal configurations. At each resolution considered, the contact matrix must be partitioned by direct inspection or other relevant knowledge. The spatial structure at the finest resolutions is then reconstructed assuming that the structure of each subchain is not modified by its interactions with the other domains. This allows us to choose the most appropriate resolution for each segment, thus attaining an accurate reconstruction at both local and global levels. To sample the solution space, the chain configuration is evolved by quaternions [36], which offer advantages over the popular rotation matrices using Euler angles. Indeed, altering the bead positions by quaternions is independent of Cartesian coordinates, maintains topological constraints, and is less expensive computationally: it only involves generating planar and dihedral angles and interbead distances. The only constraint that needs to be checked is related to spatial interferences between beads.
In what follows, we describe our approach, give details on our present algorithmic choices, and report on the results obtained from the data set provided in [9].
Methods
A multiscale modified beadchain chromatin model
To build our chromatin model, we exploit the fact that the DNA sequences in some genomic regions show many internal contacts and very weak interactions with the rest of the genome [35]. This entails a contact frequency matrix with a number of diagonal blocks with relatively large entries, associated to row and column ranges whose entries are much smaller almost anywhere else. Each such block lists the number of mutual contacts of the restriction fragments within one of the abovementioned regions (called topologically associating domains, or TADs), whose 3D configuration does not depend on the rest of the sequence, and can thus be reconstructed from the data in the related diagonal block alone. The spatial relationships among different TADs depend on the data outside the diagonal blocks. To account for such a lower resolution structure, we consider each TAD as a single locus, and bin the contact matrix so that it corresponds to a single entry. Then, a new block structure can be identified and estimated. This procedure can be repeated recursively until the lowest significant resolution is reached. The result is a chromatin model whose structure can be represented at multiple resolutions.
The advantages offered by this model consist in a better accuracy in the reconstruction of the chain at successive resolutions. The lengths of the bonds linking each bead to its immediate neighbours are such that the beads cannot penetrate their neighbours and cannot be too far apart from them. The angles between adjacent bonds are constrained so that the chain curvature cannot be higher than biologically/physically permitted. Finally, the overall size of the chain in its 3D configuration cannot exceed the value of the size of the nucleus (i.e, 5 to 10 μm). As opposed to what happens in [28] and [30], these constraints limit the feasible positions of any subset of loci, even though they do not affect the data fit term chosen to solve the reconstruction problem, as described in the next subsection.
Contact frequency fit
where, if x _{ i } and x _{ j } identify two bead centers, it is d _{ i,j }=x _{ i }−x _{ j }; note that Eq. (1) does not imply any restriction on the contact matrix. Accepting a contact frequency to vanish simply means that the corresponding pair does not affect the data fit. Of course, all the configurations with d _{ i,j } vanishing for each (i,j) in \(\mathcal {L}\) are unconstrained minimisers of (1). Each such configuration has all the pairs of loci in \(\mathcal {L}\) in contact, and all the others in arbitrary positions. This does not mean, however, that such configurations will all be reached: the geometrical constraints prevent the final structure from reaching all those minima, thus producing solutions that are consistent with both the data and our prior knowledge.
Estimation strategy
Monte Carlo sampling
Let \(\mathcal {C}\) be the configuration of a bead chain at any resolution. In our present implementation, we estimate it by sampling a probability density function \(p(\mathcal {C})\propto \ \text {exp}[\Phi (\mathcal {C})]\). The sampling is implemented by a Monte Carlo procedure with a classical annealing schedule [39, 40]. In synthesis, given the current chain configuration, a randomly altered configuration is proposed and included in the sample upon a probabilistic test. During the iteration, the data fit term \(\Phi (\mathcal {C})\) is modified by dividing it by a decreasing temperature parameter, to make the distribution more peaked around its maxima. When the temperature has reached its minimum value, the samples generated should be clustered around the set of absolute maxima of the distribution. In our case, we expect that different configurations match the data equally well, so the distribution function is not expected to show very definite maxima. Thus, the simulated annealing is not used as a global optimiser: various configurations can show similar (low) values of the data fit and can be assumed as highly plausible solutions.
Model evolution: Quaternions
To evolve our model, we use quaternions rather than Euler angles (see Additional file 1, or reference [36] for a more complete account). Quaternions can represent very well rotations in a 3D space, as they are a simple framework to understand and visualise rotations using an angle and a rotation axis. Furthermore, quaternions avoid several problems involving rotations, such as singularities and numerical instabilities related to orthonormal matrices (e.g., gimbal lock [41]). Finally, quaternions are less expensive than Euler angles, as they only need to store 4, as opposed to 9, real numbers, and composing two rotations needs 16 multiplications and 12 additions, as opposed to 27 multiplications and 18 additions.^{1}
Overall recursive procedure
The recursive procedure we propose is described in this pseudocode:
 a
Populate set \(\mathcal {L}\);
 b
Set the initial bead chain configuration \(\mathcal {C}_{0}\);
 c
Compute \(\Phi (\mathcal {C}_{0})\) as in Eq. (1);
 d
Iterate in i (assuming a cooling schedule T _{0}→…
T _{ n }→…)
Check stop criterion: if satisfied, save \(\mathcal {C}_{i}\) and leave

Generate \(\mathcal {C}^{*}\) by perturbing randomly the bond lengths, the planar and the dihedral angles of the current configuration \(\mathcal {C}_{i}\)

In the perturbed configuration, evaluate the distances between the beads belonging to the pairs in \(\mathcal {L}\);

Compute \(\Phi (\mathcal {C}^{*})\)

if {\(\Phi (\mathcal {C}^{*}) < \Phi (\mathcal {C}_{i})\) or \(\text {random}[0,1]< \mathrm {e}^{\left [\frac {\Phi (\mathcal {C}_{i})  \Phi (\mathcal {C}^{*})}{T_{i}}\right ]}\)}and constraints are satisfied
\(\mathcal {C}_{i+1} = \mathcal {C}^{*}\)
else
\(\mathcal {C}_{i+1} = \mathcal {C}_{i}\)

3) i f # o f d i a g o n a l b l o c k s=1
structure = \(\mathcal {C}\) (hierachical composition of all the saved configurations)
output structure
leave
4) constraints = geometrical features of all the subchains + parameters and constraints at the new resolution (Fig. 1 ad)5) cont.matr = bin(cont.matr) (binning in accordance to the current blocks)6) structure= procedure(cont.matr, constraints)
We wrote Python 2.7.2 procedures implementing this recursion for two hierarchical levels (see Additional files 2, 3 and 4). At the highest resolution, we used external information on possible TADs to extract the diagonal blocks; further binnings should be based on the values assumed by the matrix elements, possibly using some appropriately chosen threshold. Note that step 2) can be performed in parallel for all the extracted blocks. This means that possible parallel computing capabilities can fully be exploited. Note also that this procedure produces one overall structure, at maximum resolution, per run. As per the remarks in the previous section, different runs normally produce different structures. Another way to proceed, for each data and parameter set, is to save all the stable subchain configurations at any resolution, and then sample each such set to produce the structures at the subsequent resolution. This strategy allows us to produce a potentially very large set of solutions, while saving much computation time. This is what we have done with the experiments reported below.
Results and discussion
Conclusion
We propose a new approach to estimate chromatin configurations from contact frequency data. The novelties introduced are a modified beadchain model evolved by quaternion operators, and a datafit function that does not require to translate frequencies into distances. The 3D structure can be estimated by applying our algorithm recursively at different resolutions. In order to keep the model compliant with known physical and biological features, any prior information available must be translated into geometrical constraints.
Our first results from real HiC data show that the configurations obtained are compatible with biological information that has not been introduced in the problem. Indeed, the geometrical constraints we introduce are uniform along the chain, so the structural differences only depend on data. Thus, we demonstrated that structurally different regions in our reconstructions highly correlate with functionally different regions as known from literature and genomic repositories.
Besides extending the experimentation to further data and target features, our future activity will deal with the optimisation of our code, in order to help the choice of the most appropriate parameters, include an explicit treatment of data biases, along with all the available biological knowledge, and allow structure estimation for larger and larger genomic regions.
Endnotes
^{1} http://www.geometrictools.com/Documentation/RotationIssues.pdf (last additions. accessed: 2015, May 5^{ t h }).
^{2} http://genome.ucsc.edu/ENCODE/ (last accessed: 2015, April 28^{ t h }).
Declarations
Acknowledgements
The authors are indebted to Luigi Bedini and Aurora Savino for helpful discussions. This work has been funded by the Italian Ministry of Education, University and Research, and by the National Research Council of Italy, Flagship Project InterOmics, PB.P05 (http://www.interomics.eu).
Authors’ Affiliations
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