Hierarchical structure of cascade of primary and secondary periodicities in Fourier power spectrum of alphoid higher order repeats
 Vladimir Paar^{1}Email author,
 Nenad Pavin^{2},
 Ivan Basar^{1},
 Marija Rosandić^{3},
 Matko Glunčić^{1} and
 Nils Paar^{1}
DOI: 10.1186/147121059466
© Paar et al., licensee BioMed Central Ltd. 2008
Received: 12 March 2008
Accepted: 03 November 2008
Published: 03 November 2008
Abstract
Background
Identification of approximate tandem repeats is an important task of broad significance and still remains a challenging problem of computational genomics. Often there is no single best approach to periodicity detection and a combination of different methods may improve the prediction accuracy. Discrete Fourier transform (DFT) has been extensively used to study primary periodicities in DNA sequences. Here we investigate the application of DFT method to identify and study alphoid higher order repeats.
Results
We used method based on DFT with mapping of symbolic into numerical sequence to identify and study alphoid higher order repeats (HOR). For HORs the power spectrum shows equidistant frequency pattern, with characteristic twolevel hierarchical organization as signature of HOR. Our case study was the 16 mer HOR tandem in AC017075.8 from human chromosome 7. Very long array of equidistant peaks at multiple frequencies (more than a thousand higher harmonics) is based on fundamental frequency of 16 mer HOR. Pronounced subset of equidistant peaks is based on multiples of the fundamental HOR frequency (multiplication factor n for n mer) and higher harmonics. In general, n mer HORpattern contains equidistant secondary periodicity peaks, having a pronounced subset of equidistant primary periodicity peaks. This hierarchical pattern as signature for HOR detection is robust with respect to monomer insertions and deletions, random sequence insertions etc. For a monomeric alphoid sequence only primary periodicity peaks are present. The 1/f^{ β }– noise and periodicity three pattern are missing from power spectra in alphoid regions, in accordance with expectations.
Conclusion
DFT provides a robust detection method for higher order periodicity. Easily recognizable HOR power spectrum is characterized by hierarchical twolevel equidistant pattern: higher harmonics of the fundamental HORfrequency (secondary periodicity) and a subset of pronounced peaks corresponding to constituent monomers (primary periodicity). The number of lower frequency peaks (secondary periodicity) below the frequency of the first primary periodicity peak reveals the size of n mer HOR, i.e., the number n of monomers contained in consensus HOR.
Background
Introduction
Repeat sequences are a common feature of genomes [1–3]. The detection and study of periodicity in genomic sequences has been an area of increasing interest. Signal processing approaches to periodicity detection methods are attracting significant attention in genomic DNA investigations of approximate repeats because they are rather robust in the presence of substitutions, insertions and deletions and may identify approximate periodicities in DNA sequences. Different computational techniques have been used: Fourier spectral analysis [4–20], wavelet transform [21], DNA walk analysis [22–25], information theory measures [26–28], informational decomposition [29, 30], quaternionic periodicity transform [31], exactly periodic subspace decomposition [32, 33], portrait method [34], enhance algorithm for distance frequency distribution [35], etc.
Discrete Fourier transformation (DFT) based methods
Spectral analysis employing Discrete Fourier transform is used to reveal periodicity in symbolic sequences, like genomic and protein sequences [7, 9, 14, 16, 17, 20, 36–53], to investigate longrange correlations [4, 5, 54, 55] and to study the problem of sequence similarity [14, 56–62].
DFT identification of approximate repeats
A peak at a frequency f in Fourier power spectrum of base correlations of a given genomic sequence shows a kind of l = 1/f – base periodicity, exact or approximate [14–16, 63]. In the ideal case of perfect periodicity, where a fragment of the length l is exactly repeated N times, periodicity generates a series of l1 equidistant peaks in the power spectrum, at frequencies [14, 16]:
f_{1} = 1/l, f_{2} = 2/l, f_{3} = 3/l, ... f_{l1}= (l1)/l
Approximate repeats, modified by random insertions and/or deletions with respect to perfect repeats, typical for genomic sequences of higher organisms, can often be identified using Fourier transform [14, 16, 17]. This procedure results in a characteristic system of equidistant peaks. However, it was noted that a disadvantage of methods based on Fourier transform may be that in cases of more pronounced deletions or insertions the periodicity cannot be detected, while deletions and insertions are frequent mutational events in genomic sequences [29, 50].
DFT identification of period three hidden periodicity
A sharp peak of period three was found in a search for periodic regularities on a sample set of human exons [5, 9, 10, 22, 54, 60, 64]. The threebase periodicity in exons is caused by unbalanced nucleotide distributions in the three coding positions, while in intron sequences the nucleotides distribute uniformly. The relative height of the corresponding peak in Fourier spectrum is a good discriminator of coding potential and has been used to detect coding regions [9, 14, 37, 45, 49, 65–75].
DFT identification of longrange correlations
Statistical studies of DNA sequences have been instigated by finding of the 1/f^{ β }longrange powerlaw correlations in human genomic sequences, indicating the presence of scale invariant structure [4, 5, 22], implying that the underlying system shows fractal properties [25, 76, 77]. The lack of long enough sequences and the use of different methods of estimating the correlations, leading to some results not strictly comparable to each other contributed to controversies regarding findings on longrange correlations, like the presence of these correlations only in noncoding or in all human genomic sequences, and their presence in other organisms [5, 6, 23, 36, 78–83]. Nonstationary analysis of DNA sequences has shown that both coding and noncoding sequences exhibit longrange correlations, with the average spectral exponent of noncoding segments being higher than its counterpart for coding segments [84]. With the availability of large sequences and extended statistical computations, showing powerlaw correlations over four or five orders of magnitude, with exponents which are consistent with previous results obtained analyzing short sequences, such correlations in human DNA, with fractallike scaling, are now commonly accepted [27, 28, 45]. It has been pointed out that the mosaic structure of genome is presumably responsible for longrange correlations [79, 85, 86]. At very low frequencies (for example, f < 10^{6}) the power spectrum flattens out [87–89]. It should be noted that the attribution of longrang correlations exclusively to largescale variations of nucleotide density responsible for 1/f^{ β }spectra is not quite correct. Generally, even largescale variations of nucleotide density may produce patterns different from 1/f^{ β }spectra.
DFT identification of alphoid higher order repeats (HOR)
Here we investigate the application of Fourier analysis to human alpha satellite tandem repeats and the associated higher order repeats (HORs). Alphoid arrays consist of tandem repeats of alpha satellite monomer unit of approximately 171 bp, which form chromosomespecific higher order repeats (HOR) or monomeric organization consisting of diverged monomers [90–104]. Alpha satellite monomers within HOR exhibit substantial mutual sequence divergence (20–40%), while HORs exhibit much lower mutual divergence (< 5%) [98]. Such a case is interesting for Fourier analysis because it has a twolevel hierarchy of approximate homology.
Alpha satellite DNA is characterized by many levels of hierarchical organization in genomes, from suprachromosomal families to chromosomespecific subsets, to polymorphic variation within these subsets [90–103]. The higher order repeat organization is consistent with linear sets of diverged monomers becoming the unit of crossingover during the process of sequence homogenization. The HOR units of alpha satellite monomers are organized in largely chromosome specific manner. The centromere of each human chromosome is characterized by one or more subsets of distinct alpha satellite HOR units. Analyses have revealed the presence of up to several thousand repeat units arranged in an apparently uninterrupted fashion in the centromere and forming arrays of several million base pairs. Alpha satellite HORs have been studied using restriction enzymes that cut higher order repeat unit [98, 101]. Recently, HORs and monomeric alpha satellites have been studied by computational analysis of genomic sequences from the NCBI genome assembly [104–108].
As a case study we consider a 16 mer HOR at the loci D7Z2 and D7Z1 in human chromosome 7 [94–97]. In [104] 16 mers were identified by DOTTER analysis; the presence of 16 mer was reported, but detailed HOR structure was not presented. In detailed computational studies of genomic sequence of the 193277bp clone AC017075.8 (contig NT_023603.5), the 46 complete and 14 incomplete copies of 16 mer alphoid HOR were identified in the central domain (positions 31338 to 177434, total length 148147 bp) [105–107]. Preliminary study of power spectra discussed the general pattern and the signaltonoise ratio [17]. These HOR copies are highly homologous (divergence from consensus less than 0.6% on the average), while divergence among monomers within each HOR copy is sizeable (20% on the average). (In accordance with common practice, monomer deletions or insertions, which appear in some HOR copies, are not taken into account in calculating divergence among HOR copies.) Such a long genomic sequence enables a highly precise determination of higher order periodicities. In the front domain of genomic sequence (31337 bp) and in the back domain (15843 bp), 199 alpha satellite monomers are present which are not organized into HORs and therefore are all mutually divergent by 20% or more. Only 29% of this bordering domain is not of alpha satellite type.
Our goal is to investigate the periodicities in the short, medium, and longrange order, related both to less homologous alphoid monomeric pattern and to more homologous alphoid higherorder repeats and to correlate the two levels of periodicity, primary (basic monomer periodicity) and secondary (HOR periodicity).
Results and discussion
DFT identification of HOR in AC017075.8 based on quartic mapping
The genomic sequence AC017075.8 (193277 bp) from chromosome 7 was transformed into numerical sequence using quartic mapping (Eq. 5) with parameters (Eq. 6) (see section Methods). The AC017075.8 sequence is used as a case study for the use of DFT method for interplay of monomeric and HOR repeats. In general, regions containing higher order repeat sequences can be located through the sliding window analysis, similarly as used in [16] for primary periodicity sequences. Analyzing complete nucleotide sequence we found domains having different repeat pattern, the central HOR domain and the bordering domains (front and back domains), in accordance with identifications obtained using other methods [94–97, 105–108].
f_{ n }= n·f_{1}, n = 1, 2, 3, ...
The fundamental frequency f_{1} corresponds to the 2734bp HOR. (Due to truncation of data set and the associated precision limit of 7.6·10^{6}, a more precise value can be deduced from the systematic of higher multiples).
These equidistant peaks are identified over a very broad interval, up to n ≈ 1000. In fact, all prominent peaks above the white noise background in the power spectrum are multiples of the fundamental frequency f_{1}. We note that such an extremely regular pattern can be rarely found even in the most regular dynamical systems in physics and engineering.
renormalized in order to increase the relative weight of lowfrequency with respect to high frequency peaks. The effective values S_{ eff }corresponding to frequencies f_{1}, f_{2} = 2f_{1}, f_{6} = 6f_{1}, and f_{16} = 16f_{1} are 2.025, 0.973, 0.895, and 6.584, respectively.
The prominent peak at the frequency f_{16} = 0.005852 bp^{1} corresponds to approximately 171 bp length. More precisely, 1/f_{16} = 170.88 bp. It corresponds to a set of alpha satellite monomers which constitute consensus HOR (nine 171bp, five 170bp, one 172bp, and one 173bp copy variants). Alternatively, the HOR period 1/f_{1} = 2734 bp could be also expressed as multiple of monomer period 1/f_{16} = 171 bp. The lowfrequency peaks at f_{1}, f_{2}, ..., f_{15} are subharmonics of the monomer frequency f_{16}.
In the frequency region above the monomer frequency f_{16} (Figure 1b), within the set of multiple frequencies nf_{1} (n > 16) we find a prominent subset of higher harmonics at frequencies that are multiples of the monomer frequency f_{16} : 2f_{16} , 3f_{16}, 4f_{16}, ... This subset with band head at the frequency f_{16} will be referred to as monomeric band.
Fourier analysis works well enough for studying relatively short periodicities while the statistical significance of longer periodicities will be decreased by the presence of shorter periodicities [29]. Thus, the statistical significance of longer periods was predicted to be a sort of smeared through statistical significance of shorter periods, i.e., for harmonics with longer periods the damping effect may be more pronounced [29]. We show here that the DFT method is applicable to alphoid HORs up to very long periodicities (up to several kilobases).
Generally, the fundamental frequency for equidistant pattern in the power spectrum corresponds to the periodicity of highest order in a given sequence, i.e., to the period of HOR (secondary periodicity). Specifically, in the HOR domain of AC017075.8 the fundamental frequency in power spectrum corresponds to the period of HOR consensus unit, 2734 bp.
Although the HOR copies are much more homologous to each other than the constituent alpha satellite monomers among themselves, the number of monomers corresponding to primary periodicity (at frequencies f_{16}, 2f_{16}, 3f_{16}, ...) is much higher than the number of HOR copies corresponding to secondary periodicity (at frequencies f_{1}, f_{2}, f_{3}, ...), and therefore the peaks of primary periodicity have higher spectral strengths.
Robustness of DFT results for hierarchical structure of cascade of primary and secondary periodicities using different genomic into numerical sequence mapping
The difficulty with DFT approach may be dependence on a particular labeling adopted. For example, some of the relevant harmonic structure can be hidden (or exposed) by the symbolictonumeric mapping [111]. To check the required mapping invariance, we investigate whether the hierarchical periodic pattern shown in this paper is robust with respect to a particular choice of procedure for transforming symbolic to numerical sequence.
Test computation for quartic mapping deduced from systematic of purine/pyrimidine and strong/weak bond characteristics
Test computation for quartic mapping deduced from reduced dimensionality of frequency spectrum in symmetric manner
Test computation by summing the squares of Fourier transforms of indicator sequences
Hierarchical primary and secondary periodicity pattern in perfect HOR sequence
Robustness of power spectrum for hierarchical structure of cascade of primary and secondary periodicities for imperfect HORs
Noisy power spectrum of a random artificial sequence
Power spectrum pattern of monomeric alphoid domain in AC017075.8
Absence of lowfrequency 1/f^{ β }noise in DFT power spectrum
The 1/f^{ β }– noise is absent in the lowfrequency region of power spectrum of AC017075.8, both in the central HOR domain (Figure 1a) and in the monomeric front and backdomains (Figure 8a). This result is in accordance with expectations, because the sequence mainly consists of approximate repeats, without sizeable sequencewide base composition fluctuations. Previously, some cases of absence of long range correlations in repeat sequences have been found. For example, in a sequence for beta globin on human chromosome 11 (HUMHBB, 73326 bp) two 6kb segments without longrange correlations were identified, both including stretches of repetitive DNA [76].
Rank ordering for harmonics in alpha monomeric spectrum
In the power spectrum of our case study for genomic sequence in HOR domain the equidistant peaks corresponding to multiples of monomer frequency f_{16} are sizably stronger than the other peaks (Figure 1b). Among the lowfrequency peaks in Figure 1b) the most pronounced peaks are 10f_{16}, 12f_{16}, and 14f_{16}. The corresponding lengths are approximately 17 bp, 14 bp, and 12 bp, respectively. The 14bp length may be related to the highest frequency of appearance of the 6bp key string TTTTGA at the distance of 14 bp between two neighboring key strings. However, in general, the chosen mapping may influence the rank ordering of harmonics, as seen by comparing their relative heights in Figures 1, 2, 3, 4, 5. Thus, the effect of parameter choice for symbolictonumeric transformation may overshadow the effect of hidden genomic substructure.
Absence of periodicity three in power spectrum of HORs
Use of power spectrum for identification of hierarchical primary and secondary periodicity pattern in chromosome 1
Conclusion
We have demonstrated that DFT is a robust and efficient method to identify alphoid HORs in alpha satellite domain of genomic sequence. In the case of n mer HOR the lowest peak is at the fundamental frequency 1/(171n bp), which will be referred to as HORfrequency. It is a head of band of equidistant peaks at frequencies equal to consecutive multiples of HORfrequency, i.e., 1/(171n bp), 2/(171n bp), 3/(171n bp), ... This band is referred to as the HORband. Some peaks within the HORband form a strongspectralpower subset with band head at monomerfrequency 1/(171 bp). This subset forms a band of equidistant peaks at frequencies which are multiples of monomerfrequency 1/(171 bp), i.e., it corresponds to the peaks at frequencies 1/(171 bp), 2/(171 bp), 3/(171 bp), .... This subband is referred to as the monomericband.
In the case of monomeric alpha satellites (not organized into HOR) the lowest peak is at the monomerfrequency 1/(171 bp). It is a head of monomericband built from peaks at frequencies 1/(171 bp), 2/(171 bp), 3/(171 bp), ....
DFT was applied here in the case study of genomic sequence AC017075.8 (193277 bp) from centromeric region in human chromosome 7. The central domain of AC017075.8 consists of 16mer alphoid HOR copies. Thus the frequency of the lowest peak in the power spectrum (HORfrequency) is 1/(171·16 bp). We identified in the power spectrum as many as one thousand peaks at frequencies equal to multiples of HORfrequency, forming a HORband. Among these peaks in the HORband a subset of peaks at frequencies 1/(171 bp), 2/(171 bp), 3/(171 bp), .... is characterized by pronounced spectral power and represents the monomericband. This reveals hidden periodicities in the 171bp monomer, i.e., a hierarchy of periodicities within the monomer sequence. Power spectra of both the HOR region and of the monomeric region show this pattern of hidden higher frequencies.
The case study shows that DFT is robust in detecting approximate HORs, even in the presence of substantial sequence insertions and deletions.
Additionally, the applicability of DFT method was shown for chromosome 1, where a hierarchical pattern of 11 mer HOR is present.
Computing DFT power spectra for anonymous genomic sequence using sliding windows for bins of about 50 kb and step size of about 10 kb provides an easily recognizable hierarchical twolevel equidistant pattern in the power spectrum as signature of presence of HOR and gives a simple method to determine the size of HOR.
Methods
Discrete Fourier transform of genomic sequence
To apply DFT, one should first represent genomic sequence, a symbol sequence over the alphabet {A, T, G, C}, as a numerical sequence reflecting the characteristics of the symbol sequence. Several approaches have been used for solving the problem of transformation of a symbol sequence to numerical sequence.
A common mapping scheme is to decompose genomic sequence into four component indicator sequences. These binary indicator sequences, u_{A(m)}, u_{T(m)}, u_{C(m)}, and u_{G(m)}, take the value of either 1 or 0 at position m depending on whether the corresponding character is present or absent at that location, respectively. These indicator sequences were analyzed by respective Fourier transforms [5, 9, 39, 40, 55]. For pure DNA character strings (i.e., without assigning numerical values), to the binary indicator sequences u_{A(m)}, u_{T(m)}, u_{C(m)}, and u_{G(m)} correspond the DFT sequencesu_{A(k)}, u_{T(k)}, u_{C(k)}, and u_{G(k)}
respectively, providing a fourdimensional representation of the frequency spectrum of the character string. The quantity obtained by summing the squares of the Fourier transform of indicator sequences:S(k) = u_{ A }(k)^{2} + u_{ T }(k)^{2} + u_{ C }(k)^{2} + u_{ G }(k)^{2}
is used as a measure of the total spectral content of DNA character string at frequency k [9, 37, 39, 40, 111].
Fourier transform of a nucleotide sequence was represented also by sum of pure sequences (Eq. 3) or by their product [15, 109]. A single binary sequence was used by mapping genomic sequence into purine/pyrimidine representation [22], or into weak bond/strong bond representation [109]. Alternatively, mapping of DNA symbolic sequence into a set of quaternions could be utilized via the use of quaternionic Fourier transform [31].
A quartic mapping of genomic into numerical sequence of length N was performed by mapping each symbol to a number [17, 37, 111]:
x(m) = au_{A(m)} + tu_{T(m)} + cu_{C(m)} + gu_{G(m)}, m = 0, 1, 2, ..., N1 (5)
where a, t, c, and g are numerical values assigned to the characters A, T, C, and G, respectively.
We define the quartic map by ordering numbers of nucleotides with increasing frequency in the sequence AC017075.8 (corresponding to the orientation ) in chromosome 7, which is used for our case study:
a = 4, t = 3, c = 2, g = 1 (6)
These values are in accordance with ordering of nucleotides with decreasing frequencies in the HOR region, and therefore they are biased in favor of A and T.
When using sequences from the Build 36.2 assembly (corresponding to the orientation +), the corresponding parametrization for quartic mapping is complement to (Eq. 6):
t = 4, a = 3, g = 2, c = 1 (7)
In [113] the purine/pyrimidine and strong/weak bond properties of the four kinds of nucleotides were considered. The point (1,1) was used to represent nucleotide C corresponding to its pyrimidine and strong bond properties; the point (1,1) to represent nucleotide G corresponding to its purine and strong bond properties; the point (1,1) to represent nucleotide A corresponding to its purine and weak bond properties; and the point (1,1) to represent nucleotide T corresponding to its pyrimidine and weak bond properties. Then the vectors connecting the origin to the four points (1,1), (1,1), (1,1) and (1,1) have the rotational angles π/4, 3π/4, 5π/4,7π/4 with the xaxis and correspondingly the map defined [113]:
a = 7, t = 5, c = 3, g = 1 (8)
These quartic map parameters are linearly related to parameters in (Eq. 6), b = 2b'1 (b and b' stand for the corresponding nucleotides in (Eq. 8) and (Eq. 6), respectively).
from which the DFTs are calculated.
In all computations the DFT was computed using Fast Fourier Transform (FFT) computer program [115] with the 1/$\sqrt{N}$ normalization.
A search for regions of higher order repeats in anonymous sequence, without prior knowledge on its structure, can be performed by sliding window analysis, similarly as used in Spectral Repeat Finder [16]. Once a region of HOR structure is detected, a more precise edge detection of HOR region can be determined by performing more precise local search using smaller step size.
Abbreviations
 HOR:

Higher Order Repeat
 KSA:

Key String Algorithm
 DFT:

Discrete Fourier Transform.
Declarations
Acknowledgements
This work was supported by Ministry of Science, Education and Sports of Croatia. The authors express special thanks to anonymous reviewers for their valuable discussions and comments on this manuscript.
Authors’ Affiliations
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