Volume 17 Supplement 14
Proceedings of the 14th Annual Research in Computational Molecular Biology (RECOMB) Comparative Genomics Satellite Workshop: bioinformatics
A continuous analog of run length distributions reflecting accumulated fractionation events
- Zhe Yu^{1} and
- David Sankoff^{1}Email author
https://doi.org/10.1186/s12859-016-1265-5
© The Author(s) 2016
Published: 11 November 2016
Abstract
Background
We propose a new, continuous model of the fractionation process (duplicate gene deletion after polyploidization) on the real line. The aim is to infer how much DNA is deleted at a time, based on segment lengths for alternating deleted (invisible) and undeleted (visible) regions.
Results
After deriving a number of analytical results for “one-sided” fractionation, we undertake a series of simulations that help us identify the distribution of segment lengths as a gamma with shape and rate parameters evolving over time. This leads to an inference procedure based on observed length distributions for visible and invisible segments.
Conclusions
We suggest extensions of this mathematical and simulation work to biologically realistic discrete models, including two-sided fractionation.
Keywords
Genomics Whole genome duplication Analysis of runs Probability modeling Duplicate gene deletionBackground
In the course of evolution, new genomes occasionally arise by duplication or triplication of an existing genome, so that there are two or three identical copies of each maternal and each paternal chromosome. After a (usually) transient period of polyploidy marked by unusual patterns of meiosis where more than just one maternal and paternal chromosome are aligned and recombine, processes of sequence divergence and chromosome rearrangement lead to more familiar diploid patterns. At the same time a process of fractionation eliminates some or most of the duplicate genes, some from each chromosomal copy, but in the simplest model, never all members of a duplicate pair or triple - for reasons of viability. Fractionation processes have been surveyed across evolutionarily diverse types of eukaryote organisms [1].
Since one copy of a duplicate pair of genes must be retained, we can identify not only the chromosomal regions that have been retained – by simple observation of the genome – but also each region that is now invisible – by reference to the duplicate chromosome that has necessarily retained a copy of this region. Thus, the data on which inferences about the deletion process can be made consist of alternating segments of deleted and undeleted genome of varying lengths.
Among the important questions about the nature of the deletion process, we can ask whether deletion proceeds one gene at a time or by larger chromosomal fragments. In this paper, we model the process as the deletion of segments from the real line, with a biologically realistic treatment afforded to overlapping deletions. Previous work focused on the difficult question of how many overlapping deletion events are responsible for each contiguous deleted region [2–4], but was not able to account analytically for the dynamics of the process.
In the present paper we attack and solve the inference problem of the size, form and spacing of deletion events, allowing for a number of sweeps over the genome as a way of accounting for overlapping deletions. We carry this out in a continuous analog of the original discrete gene-order context, and address the “one-sided” version of the problem, where all deletions occur on one of the duplicate chromosomes.
There has been a certain amount of work on the quantification of the fractionation process, starting in 2006 with [5], which claimed deletions involved one gene at a time, and [6], which treated the number of genes deleted in a single event as a random variable with mean greater than 1. Other work of this kind includes [1] and [7]. However, the modelling of fractionation where the whole genome evolves as a stochastic process began with [2]. The previously unstudied phenomenon taken into account in that work was the overlap of deletion events, something that assumes much importance soon after the fractionation process commences. Overlap must be handled differently if all deletions occur from one copy of the genome or in either copy. To isolate the most important aspect of overlap, [2] gave analytical results for the case where deletions all occurred on one copy (“one-sided” model). Then [3] extended this to the more realistic case where deletion could occur at different rates, or the same rate, from either copy of the genome (“two-sided” model). This analysis was more difficult and could not be taken as far as with the one-sided model.
For the one-sided model, a closed form solution of how many deletion events contribute to a deleted region after a single event (i.e., at a single step in the fractionation process) was obtained in [4].
Methods
The proposed model
We model the fractionation process in terms of a number of successive sweeps of a point process with parameter ν on the positive reals, i.e., ν∈R ^{+}, representing one copy of the genome. At the origin, we say that all points of this genome are “visible”. A deletion event, rendering a segment of exponentially (mean μ) distributed length “invisible”, occurs at each point determined by the point process. The second copy of the genome remains undisturbed throughout and retains a 1-to-1, length preserving, correspondence with the fractionating copy, without regard to any disruption caused by invisibility. In applications, the acceptance of the one-gene-at-a time theory of deletion depends on whether μ is below or above a certain absolute value, but the present work is part of the mathematical preliminaries to the practical questions. The eventual goal of this work is to determine the relative size of the “spacing” parameter ν and the deletion length parameter μ. The model innovation here is to introduce the parameter ν in the place of a rate parameter in previous work, which was awkward to work with.
with mean μ. Normally, ν≫μ, but this is not necessary to the analysis. The segment [x _{1},x _{1}+a _{1}) is “deleted", or is designated as invisible. The next deletion point x _{2} is chosen by sampling x2′ from the first exponential distribution (mean ν), so that x _{2}=x2′+x _{1}+a _{1}. Then the length a _{2} of the second deleted segment is determined by sampling from γ again. The process continues in this way to find x _{3},a _{3},… Concatenating only those segments that are still visible, we see that x _{1},x _{2},… are points determined by a point process with parameter ν. Associated with each of these points x is an “event counter" C(x). Initially, each C(x)=1. We define a function π _{ t }(i),i=1,… measuring the proportion of event counters registering i events at time t≥1. Thus π _{1}(1)=1 and π _{1}(j)=0, for all j>1.
At times t=1,2,…, the second, third, … sweeps begin, all independent of the first sweep and each other, and each applied to the concatenated visible segments only. We sample \(x_{1}^{(t)}\) and \(a_{1}^{(t)}\) in the same way as x _{1} and a _{1} according to ρ and γ, respectively, to determine a deletion interval \([x_{1}^{(t)}, x_{1}^{(t)}+a_{1}^{(t)})\).
If the interval \([x_{1}^{(t)}, x_{1}^{(t)}+a_{1}^{(t)})\) contains no previously defined deletion point, a new event counter at \(C(x_{1}^{(t)})\) is set at 1. If \([x_{1}^{(t)}, x_{1}^{(t)}+a_{1}^{(t)})\) already contains j>1 deletion points z _{1},…,z _{ j }, the event counter at \(C(x_{1}^{(t)})\) is set at \(1+\sum _{i=1}^{j} C(z_{i})\). The j deletion points z _{1},…,z _{ j } become invisible, along with the rest of the segment \([x_{1}^{(t)}, x_{1}^{(t)}+a_{1}^{(t)})\) that contains them.
We find the next deletion point by sampling \(x_{2}^{(t)'}\) from ρ, and setting \(x_{2}{(t)}=x_{1}^{(t)}+a_{1}^{(t)}+x_{2}^{(t)'}\). We continue the t sweep, adding visible deletion points and making others invisible. Some deletion points from the earlier sweep will remain unchanged, i.e. are still visible. The \(x_{i}^{(t)}\) by themselves define a point process with parameter ν on the concatenated visible segments. But the \(x_{i}^{(t)}\) and the additional deletion points remaining from the earlier sweep define a process with mean λ _{ t }, a parameter that decreases with t, as the undeleted segments are interrupted by more and more deletions. This parameter is important as it is directly inferable from the observed genome at time t.
More important, it is clear, that at each sweep, more and more of the genome becomes invisible. Since each concatenation of visible segments still extends to the positive reals, we cannot observe directly how much the genome has been reduced in absolute terms. But thanks to the length-preserving isomorphism between the second copy of the genome and the fractionating one, for any large finite interval we can observe the proportion of the genome that is left by time t and we can predict that it is approximately \((1-\frac {\mu }{\nu +\mu })^{t}\).
We will calculate λ, the number of deletion points in [x _{ i },x _{ i+1}), the distribution p(j),j=1,... of the number j of pre-existing deletion points in intervals deleted during each sweep, and discuss how to calculate π _{1}(j),j≥1, the proportion of event counters with C=j.
Results
The length of undeleted segments λ
The treatment of overlapping deletions
Thus we have the surprisingly uncomplicated result that the number q of pre-existing runs of single-copy regions overlapped by a new deletion event is geometrically distributed on q=0,1,… with parameter μ/(μ+λ).
The distribution of event counts π
Unfortunately, even knowing the dynamics of C does not help us with the inference problem, since the number of events associated with an invisible segment, is not directly associated with the total length of the segment. It is known that the overlapping gamma variables making up each segment are related in a complex way, and cannot simply be treated as the sum of gammas drawn a single population.
This leads us to the approach in the next two sections, where simulations strongly suggest the functional form of the distribution of invisible segment lengths, including shape and rate parameters that can be observed, leading to inference of the simulation parameters based on the observations.
Simulation
Our simulation experiments were based on initial visible segments of length 10,000, which is very long in comparison to the deletion lengths with μ≤10. In other words, we do not risk artificial effects, like a disappearing genome, after a few sweeps, t≤10. Moreover, after each sweep, if the total undeleted length =L, we add, to the end of the remaining visible portion, segments where the lengths of the visible portions total 10,000−L, copied from a replicate trial. The program, written in Java, was repeated 5 times for each configuration of the parameters μ,ν and t. Each set of 5 trials averaged a total of less than 3 min on a Lenovo Y50 laptop.
After each sweep, we calculated the distribution of segment lengths for both the invisible and visible parts of the model genome.
Parameter estimation
Simulated values of shape and rate when \(\frac {\mu }{\nu }=\frac {1}{3}\), for a range of values of μ, and t=2
μ | ν | shape α | rate β | 1/β |
---|---|---|---|---|
1 | 3 | 0.8994863 | 0.7801536 | 1.281798866 |
2 | 6 | 0.8713054 | 0.3645944 | 2.742773888 |
3 | 9 | 0.8943245 | 0.2557653 | 3.909834524 |
4 | 12 | 0.8551860 | 0.1863732 | 5.365578313 |
5 | 15 | 0.8479933 | 0.1504409 | 6.647128540 |
6 | 18 | 0.8673458 | 0.1250687 | 7.995605615 |
7 | 21 | 0.8793444 | 0.1044622 | 9.572840702 |
8 | 24 | 0.91907486 | 0.09607099 | 10.40896945 |
9 | 27 | 0.91503151 | 0.08817842 | 11.34064321 |
10 | 30 | 0.82931206 | 0.07483308 | 13.36307419 |
The observable quantities in our model are the distribution of visible segment lengths, predicted to be exponential with mean λ, and the shape and rate parameters α and β of the predicted gamma distribution of invisible segment lengths. These three observable quantities are related to the unknown model parameters μ,ν, and t through Eqs. (17), (18) and (19). With the given value of these parameters, we can estimate the values of μ,ν, and t.
Lacking a closed form solution for μ,ν, and t in terms of λ,α and β, we use the following procedure. Since t must be an integer, we can find values of ν _{ t } and μ _{ t } for each t=1,2,… with Eqs. (17) and (18). Then we can solve Eq. (19) to find β _{ t }.
μ=1,ν=3,t=5,λ ^{−1}=0.16656,α=0.6711,β=0.3504
par ∖time t | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|
μ | 1.231661635 | 1.063543459 | 1.021578166 | 1.018615845 | 1.033112259 |
ν | 1.801158145 | 2.401544193 | 3.001930241 | 3.602316289 | 4.202702338 |
β _{ t } | 0.30056014 | 0.338494894 | 0.338654644 | 0.325314125 | 0.306788889 |
100δ | 14.23403354 | 3.409208693 | 3.363623543 | 7.170390806 | 12.4566366 |
μ = 6,ν = 12,t = 2,λ ^{−1}=0.17,α=0.8488,β=0.12063
par ∖time t | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|
μ | 5.7892 | 4.7235 | 4.7286 | 4.9648 | 5.2990 | 5.6560 |
ν | 12 | 18 | 24 | 30 | 36 | 42 |
β _{ t } | 0.1195 | 0.1406 | 0.1342 | 0.1220 | 0.1094 | 0.0976 |
100δ | 0.9107 | 16.5215 | 11.2325 | 1.1655 | 9.3410 | 19.0791 |
μ=1,ν=3,t=3,λ ^{−1}=1.017737,α=0.7977859,β=0.5649623
par ∖time t | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|
μ | 1.4006 | 1.0332 | 0.9909 | 1.0102 | 1.0523 |
ν | 1.9651 | 2.9477 | 3.9303 | 4.9129 | 5.8954 |
β _{ t } | 0.4271 | 0.5606 | 0.5596 | 0.5246 | 0.4810 |
100δ | 24.39 | 0.7780 | 0.9497 | 7.15 | 14.87 |
μ=5,ν=15,t=8,λ ^{−1}=0.532632,α=0.53869147,β=0.03107084
par ∖time t | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|
μ | 6.2529 | 5.4956 | 5.2256 | 5.1247 | 5.1051 | 5.1313 | 5.1861 |
ν | 7.5099 | 9.3873 | 11.2648 | 13.1423 | 15.0198 | 16.8972 | 18.7747 |
β _{ t } | 0.0281 | 0.0317 | 0.0324 | 0.0318 | 0.0306 | 0.0292 | 0.0277 |
100δ | 9.43 | 2.18 | 4.22 | 2.33 | 1.39 | 6.00 | 10.97 |
It can be seen, at least in these diverse examples, that the inference procedure generally identifies the correct value of t, and good estimates of μ and ν.
Discussion
The introduction of sweeps consisting of alternating jumps and deletions, with time-invariant parameters ν and μ, provide us with an improved possibility of solving the fractionation model completely. We do announce such a solution, though it has much room for improvement. Though the exponential distribution of visible segment lengths should be easy to establish analytically, it is also possible that the gamma distribution of invisible segment lengths could be proved, including the α and β parameters as a function of the number of sweeps. Depending on the functional form of such a solution, the inference of t,μ and ν might be amenable through closed form formulae rather than the quadratic modeling. Nevertheless, we have succeeded for the first time in inferring the parameters of a fractionation model, albeit a “one-sided” model and a continuous analog of more realistic discrete fractionation models.
Conclusions
Aside from theoretical improvements, the first priority for this work should be the return to a discrete gene-order model of fractionation with the insights gained in the current report. This should be extended to, or at least tested on simulations of, two-sided fractionation models with subgenome dominance (higher deletion rates on one copy of the genome than the other).
Declarations
Acknowledgements
Research supported in part by grants from the Natural Sciences and Engineering Research Council of Canada. DS holds the Canada Research Chair in Mathematical Genomics.
Declarations
The publication charges for this article were funded by the Natural Sciences and Engineering Research Council of Canada Discovery Grant RGPIN-2016-05585.
This article has been published as part of BMC Bioinformatics Vol 17 Suppl 14, 2016: Proceedings of the 14th Annual Research in Computational Molecular Biology (RECOMB) Comparative Genomics Satellite Workshop: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-17-supplement-14.
Availability of data and material
Not applicable.
Authors’ contributions
The study was planned by DS and ZY, who also wrote the paper. The research was carried out by ZY. Both authors read and approved the paper.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
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Authors’ Affiliations
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