## Abstract

Motivation: Periodic patterns in time series resulting from biological experiments are of great interest. The commonly used Fast Fourier Transform (FFT) algorithm is applicable only when data are evenly spaced and when no values are missing, which is not always the case in high-throughput measurements. The choice of statistic to evaluate the significance of the periodic patterns for unevenly spaced gene expression time series has not been well substantiated.

Methods: The Lomb–Scargle periodogram approach is used to search time series of gene expression to quantify the periodic behavior of every gene represented on the DNA array. The Lomb–Scargle periodogram analysis provides a direct method to treat missing values and unevenly spaced time points. We propose the combination of a Lomb–Scargle test statistic for periodicity and a multiple hypothesis testing procedure with controlled false discovery rate to detect significant periodic gene expression patterns.

Results: We analyzed the Plasmodium falciparum gene expression dataset. In the Quality Control Dataset of 5080 expression patterns, we found 4112 periodic probes. In addition, we identified 243 probes with periodic expression in the Complete Dataset, which could not be examined in the original study by the FFT analysis due to an excessive number of missing values. While most periodic genes had a period of 48 h, some had a period close to 24 h. Our approach should be applicable for detection and quantification of periodic patterns in any unevenly spaced gene expression time-series data.

Availability: The computations were performed in R. The R code is available from

Contact:chenj@umkc.edu

Supplementary information: The online supplement is available at

## INTRODUCTION

Rhythmic processes occur at all levels of biological organization with periods ranging from less than a second to years (Goldbeter, 2002). Time-series experiments are a common way to study rhythmic processes, and inherent periodicity in such data is indicative of the underlying ‘clocks’. Examples of biological rhythms include cell division (Mitchison, 2003), circadian rhythms (Crosthwaite, 2004; Prolo et al., 2005), morphogenesis of periodic structures, such as somites in vertebrates (Dale et al., 2003), complex life cycles of some microorganisms (Lakin-Thomas, 2004; Rovery et al., 2005) and many others.

With the help of gene expression technology, biologists can study the mechanisms that control a particular biological rhythm more closely. For instance, to study how the major oscillator in the suprachiasmatic nuclei (SCN) and in the liver regulates behavioral and physiological rhythms in the whole organism, Panda et al. (2002) used high-density oligonucleotide arrays to measure gene expression in the mouse tissue samples taken every 4 h during two complete circadian cycles and applied a cosine wave-fitting algorithm (Harmer et al., 2000) to identify clusters of circadian-regulated genes among more than 7000 genes. They found that about 650 cycling transcripts were under circadian regulation specific to either the SCN or the liver.

As with many other types of high-dimensional data, the choice of algorithm and statistic to identify significantly periodic patterns of gene expression is a challenge (Wichert et al., 2004). Zhao et al. (2001) used a single-pulse model (SPM) to identify periodic transcripts in the Saccharomyces cerevisiae yeast microarray data based on the assumption that the cell-cycle-regulated transcripts will peak only once per cycle. Bar-Joseph et al. (2003) developed an algorithm to represent time series of gene expression as continuous curves using a cubic spline method and used this algorithm to estimate missing values in time series. Langmead et al. (2003) proposed an algorithm that uses autocorrelation to perform linear-time search in frequency and phase, and then use the undirected Hausdroff distance as a similarity measure to cluster genes of similar cyclic patterns together. Johansson et al. (2003) used a multivariate partial least squares regression model to identify cell cycle-regulated genes in the S.cerevisiae yeast data. Luan and Li (2004) proposed to use the shape-invariant model of Lawton et al. (1972) and Wang and Brown (1996) combined with a B-spline estimation to model periodic gene expression profiles. Luan and Li (2004) also applied their approach to several publicly available microarray data including the S.cerevisiae yeast data.

A common computational technique to study periodic data is the Fast Fourier Transform (FFT) algorithm (Priestley, 1981), which finds periodicities by searching for sharp peaks in the ordinary periodograms calculated from the Fourier transform of the time series (Durbin, 1967). For example, Spellman et al. (1998) studied cell cycle-regulated genes in S.cerevisiae gene expression data using Fourier analysis and found about 800 periodic genes. Straume (2004) compared four algorithms used in circadian gene expression analysis and pointed out that FFT performs well when time points are evenly spaced. Wichert et al. (2004) defined an average periodogram based on the ordinary periodograms or classical periodograms (Priestley, 1981; Scargle, 1982) and used it as a graphical tool for finding possible periodic signals in yeast and human cells.

One limitation of the FFT algorithm and the ordinary periodograms is that it requires evenly spaced time series (Priestley, 1981). Time series produced by biological experiments are, however, often unevenly spaced, for a variety of reasons, many of which have to do with the limitations of the instruments and with inherent experimental constraints on biological samples. Another limitation of the FFT algorithm is that it does not tolerate missing values. When missing values in the time series are present, data must be imputed prior to the application of the FFT algorithm. The effects of data imputation and the optimal way to do so, however, are generally not known. Moreover, FFT scores do not directly address the significance of the observed periodicity and have to be validated by a heuristic cutoff score or permutation studies.

These limitations can be overcome using the statistical approach first introduced in astrophysics, the Lomb–Scargle periodogram. When studying variable stars in astronomy, Lomb (1976) sought a way to find periodicities in unevenly spaced data. Astronomers could not always control viewing times, telescope availability and the position of an object in the sky—all of which is reminiscent of similar experimental problems in biology. In an attempt to find an alternative to imputing pseudo-data in sinusoidal models, Lomb (1976) proposed to use least-squares fits to sinusoidal curves. Scargle (1982) extended Lomb's work by defining the Lomb–Scargle periodogram and by deriving the null distribution for it. The p-value of the Lomb–Scargle periodogram can be obtained using these results. Horne and Baliunas (1986) noted a particular standardization of the periodogram that resulted in known statistical properties. Press and Rybicki (1989) proposed a practical mathematical formulation, later implemented in C (Press et al., 2002).

Ruf (1999) was one of the first to use Lomb–Scargle periodograms to analyze biological data. Ruf applied the technique to telemetric temperature data of an alpine marmot over more than 12 days prior to hibernation and detected a circadian rhythm with a period of 24 h. Van Dongen et al. (1999) analyzed the data of human oral temperatures in search of a circadian rhythm. Periodicity of 24 h was successfully identified in the unevenly spaced time series using the Lomb–Scargle method. In their attempt to detect rhythmic components in the circadian cycle of the Crassulacean acid metabolism plants, Bohn et al. (2003) used Lomb–Scargle method for periodogram estimation.

In this paper, we propose to use the Lomb–Scargle periodogram to search for periodic patterns in unevenly spaced time series that represent gene expression profiles. As gene expression data have typically large dimensionality (hundreds to thousands of profiles), searching for all periodic genes under such conditions requires statistical multiple hypothesis testing, which can be achieved using the multiple comparison procedure that controls the false discovery rate (FDR) (Benjamini and Hochberg, 1995). Our main example is gene expression in the malaria parasite, recorded by Bozdech et al. (2003). The techniques and the software that we developed, nevertheless, are readily applicable to periodic pattern discovery in any time series.

## METHODS

### Lomb–Scargle periodogram

For a gene g and expression level observed at time ti, we denote the time series by Yg(ti) for i = 1, … , N and g = 1, … , $G$. To model Yg(ti) for periodicity, we assume

$Yg(ti)=ηg(ti)+ɛg(ti),$
where ηg(ti) is a periodic function with a smallest positive period Tg for gene g, i.e. ηg(ti + Tg) = ηg(ti) for all ti; and ɛg(ti) is assumed to be a sequence of non-observable normal random errors with mean 0 and homogenous variance σ2 for all g and ti (Scargle, 1982). Let the expression of gene g at time ti be yg(ti), and the average gene expression for gene g be
$y¯g=1N∑i=1Nyg(ti),$
then the error variance can be estimated by the sample variance as follows:
$σ^2=1N−1∑i=1N[yg(ti)−y¯g]2.$
Unlike Fourier analysis, in which the Fourier frequencies are used, we assume that there are M test frequencies, f1, f2, … , fM and their corresponding angular frequencies are ωj = 2πfj, for j = 1, … , M.

The Lomb–Scargle periodogram is defined in Press and Rybicki (1989) as

(1)
$Pg(ωj)=12σ^2{(∑i=1N[yg(ti)−y¯g]cos[ωj(ti−τ)])2∑i=1Ncos2[ωj(ti−τ)]+(∑i=1N[yg(ti)−y¯g]sin[ωj(ti−τ)])2∑i=1Nsin2[ωj(ti−τ)]}$
for j = 1, … , M, where τ is defined by
$tan(2ωjτ)=∑i=1Nsin(2ωjti)∑i=1Ncos(2ωjti).$
The choice of M depends on the number of independent frequencies, N0 (Press et al., 2002). Horne and Baliunas (1986) performed extensive Monte Carlo simulations to investigate the relationship between M and N0. They gave a simple least squares formula to estimate the number of independent frequencies N0 from the number of observations, N, in a time series:
$N0≈−6.362+1.193N+0.00098N2.$
This empirical deterministic formula is adequate for most purposes of choosing M by actually taking M as N0 (Press et al., 2002).

### Statistical hypothesis testing for periodicity using Lomb–Scargle periodogram

Scargle (1982) showed that the null distribution of the Lomb–Scargle periodgram Zj = Pgj) at a given frequency ωj is exponentially distributed, i.e. the cumulative distribution function (CDF) of Zj is

(2)
$F(z)=Pr[Zj≤z]=1−e−z.$
To search for periodic gene expression, we test the null hypothesis that gene g is non-periodic versus the alternative that it is periodic. We calculate how likely it is for an observed peak in the Lomb–Scargle periodogram of gene g to occur by chance. If the peak in the Lomb–Scargle periodogram of gene g is attained at frequency ωk among M independent frequencies, we denote such a peak by Xg = maxjPgj) = Pgk). Then, for independently normally distributed noise ɛg, if there were M independent frequencies to test, the probability that the peak Lomb–Scargle periodogram Xg is smaller than xg is given by
$Pr[Xg≤xg]=Pr[Zj≤xg,j=1,…,M]=(1−e−xg)M,$
in view from Equation (2). Thus, the observed statistical significance level, the p-value, of testing the null hypothesis that such a peak in Lomb–Scargle periodogram of gene g is due to chance is calculated by
(3)
$pg=p-value=1−(1−e−xg)M,$
for gene g with g = 1, … , $G$.

In genome-wide datasets, $G$ ranges from 103 to 106, and a multiple testing approach must be employed to control the family-wise error rate when comparing $G$ profiles simultaneously. Dudoit et al. (2003) surveyed several different approaches to multiple hypothesis testing for finding differentially expressed genes in microarray experimental data. The well-known Bonferroni adjustment, when applied to gene expression analysis is somewhat conservative in the sense that too many genes will be rejected for periodicity (Tsai et al., 2003; Knudsen, 2004). Benjamini and Hochberg (1995) proposed another approach to multiple testing by controlling the FDR, the rate of expected proportion of errors among the rejected hypotheses. It is a step-down type of multiple testing procedures in combination with Bonferroni approach. The Benjamini and Hochberg FDR approach is less stringent than the family-wise error rate and more powerful than the methods proposed in Holm (1979), Hochberg (1988) and Hommel (1988). Storey and Tibshirani (2003) proposed an extension of the FDR, called q-value, to give each test its own individual measure of significance. For analyzing large-scale gene expression time-series data, we suggest to use the Benjamini and Hochberg FDR approach in combination with a periodicity-searching method.

The approach of searching for periodic genes proposed in this paper consists of the following steps:

1. For each gene expression time series, calculate the normalized Lomb–Scargle periodogram, Pgj), given by Equation (1), for j = 1, … , M, and g = 1, … , $G$; and find the peak Lomb–Scargle periodogram, Pgk), for each gene g.

2. For each of the Pgk) obtained in step 1 above, calculate the p-value, pg, according to Equation (3).

3. For the p-values obtained in step 2, obtain the ordered p-values: p(1)p(2) ≤ … ≤ p($G$). Then, find according to $k^$ according to

(4)
$k^=argmax1≤k≤G{k:p(k)≤qk/G}$
for a desired FDR level q.

4. Identify the genes whose p-values correspond to p(1), p(2), … , $p(k^)$; these genes can be claimed to show statistically significant periodic behavior for the given FDR level q according to Benjamini and Hochberg (1995).

## NUMERICAL EXPERIMENTS

Several numerical experiments were performed to assess the effectiveness of the Lomb–Scargle periodogram procedure. This section summarizes the main results of applying the Lomb–Scargle periodogram to search for periodicity in simulated signals taken on unevenly spaced time points; the details and the case of evenly spaced time points data are included in the online supplement ().

### Single periodicity detection with unevenly spaced time points

A cosine curve is often used to model an ‘ideal’ periodic gene expression (Ueda, 2002). Since the Plasmodium falciparum dataset used in this study contained data from 48 hourly samples, a 48-point cosine signal mixed with normal noise (mean 0 and variance 1) was simulated. Figure 1a shows such a simulated expression profile for a gene that has a 48 h period with data values taken randomly in the 48 h interval. Figure 1b shows a peak near a frequency of 0.0208 per hour ≈1/48 per hour, or a period of 48 h in the Lomb–Scargle periodogram. A p-value curve (Fig. 1c) is obtained for different frequencies; the lowest p-value (highest significance) of 0.0211 is achieved at peak frequency 1/48 per hour.

Fig. 1

Simulated cosine signal taken on unevenly spaced time points mixed with Gaussian noise.

Fig. 1

Simulated cosine signal taken on unevenly spaced time points mixed with Gaussian noise.

The highest frequency for which the unevenly spaced data may be evaluated (Van Dongen et al., 1999) is called the Nyquist frequency. The Nyquist frequency for data spaced by an interval Δt, an approximation of the mean of an unevenly time interval, is

$fnyquist=1/(2Δt).$
Spurious peaks can be seen in a periodogram near or above the Nyquist limit. To avoid these problems near the Nyquist limit, and because longer-period biological signals may be of more interest, the frequency range for periodograms was restricted from just above 0 to 0.20 per hour.

A 48-point (unevenly spaced) cosine signal with the period of 24 h mixed with Gaussian noise was also simulated to study for a dominant frequency. The periodograms performed as expected with a single high peak and the p-value curve gave the lowest value of 0.000925 at frequency 1/23.4 per hour (Fig. 2).

Fig. 2

Simulated cosine signal taken on unevenly spaced time points (mixed with Gaussian noise) with single dominant period.

Fig. 2

Simulated cosine signal taken on unevenly spaced time points (mixed with Gaussian noise) with single dominant period.

### Multiple periodicities detection with unevenly spaced time points

Although the biology of the malaria parasite suggests one dominant frequency of periodic gene expression (Bozdech et al., 2003), we cannot exclude the possibility that different genes have different expression periodicity and that expression of some genes may reflect two or more periodic processes with different frequencies. Van Dongen et al. (1999) have proposed a procedure of multiple periods searching in an unequally spaced human oral temperature time-series data using Lomb–Scargle method.

A simulated profile (on unevenly spaced time points) with three periodicities of 8-, 24-, and 48 h (mixed with a Gaussian noise) was obtained (Fig. 3). All three periods had equal contributions in Figure 3 and the Lomb–Scargle periodogram p-value curve showed two low p-values that indicated at least two dominant frequencies in the signal. Additional results of the numerical experiments on multiple periodicities are in the online supplement.

Fig. 3

Simulated cosine signal taken on unevenly spaced time points (mixed with Gaussian noise) with multiple periods.

Fig. 3

Simulated cosine signal taken on unevenly spaced time points (mixed with Gaussian noise) with multiple periods.

When the simulated observations were purely Gaussian noise taken on unevenly spaced time points, the Lomb–Scargle periodogram showed no significant peak, and the corresponding p-value curve had a lowest p-value of 0.763 (Fig. 4) indicating no significant periodic signal.

Fig. 4

Simulated Gaussian noise taken at unevenly spaced time points with no periodicity.

Fig. 4

Simulated Gaussian noise taken at unevenly spaced time points with no periodicity.

## ANALYSIS OF THE PLASMODIUM FALCIPARUM INTRAERYTHROCYTIC GENE EXPRESSION

While studying the transcriptional program of the asexual intraerythrocytic developmental cycle (IDC) of malaria parasite P.falciparum, Bozdech et al. (2003) obtained expression profiles using a DNA microarray. The profiles represent the expression of nearly every gene in that species. Since the symptomology and pathogenesis of malaria are strongly periodic, the study attempted to identify genes that were also strongly periodic, which might be useful for understanding the transcriptional program of Plasmodium IDC and for drug intervention. The data (available at ) include three datasets: Complete, Quality Control, and Overview (see online supplement for more discussion). Gene expression was measured every hour throughout the 48 h IDC. The Complete dataset of 7091 probes had 18.23% missing values, including all time points at hour 23 and hour 29. The large number of missing values motivates us to use the Lomb–Scargle periodogram analysis for searching for periodic patterns in this same dataset.

Instead of imputing the missing values, we treated any two points with a missing value in-between as two unevenly spaced time points. The Lomb–Scargle algorithm was implemented in ‘R’ (R Development Core Team, 2004, ), largely based on MatLab code by Glover (2000, ), with additional information from Horne and Baliunas (1986) and Press et al. (2002). Several extreme points in the Complete Bozdech dataset of 7091 profiles were identified and profiles with empty Oligo_ID were ignored (see supplement), resulting in a set of 6875 profiles. These 6875 profiles in the Complete dataset (with 15.66% missing values) were later used in our analysis of periodic patterns. More details about the data processing are provided in the online supplement.

### Discoveries

Of the 6875 probes in the Complete dataset we found that 4355 probes, or 63%, were considered to be significantly periodic with FDR level q = 1 × 10−4, and 2520 were not considered as significantly periodic. Figure 5 summarizes the results from the Lomb–Scargle algorithm for the Bozdech's Complete dataset of 6875 probes compared with the results of Bozdech et al. (2003) based on the Quality Control dataset.

Fig. 5

Periodic gene sets identified by the two methods.

Fig. 5

Periodic gene sets identified by the two methods.

Bozdech et al. (2003) did not analyze the Complete dataset for periodic genes, but rather a subset of the Complete dataset known as the Quality Control dataset. They found that 3719 profiles (the Overview dataset) in the Quality Control dataset of 5080 probes were periodic using a 70% FFT score cutoff and a 75% maximum frequency magnitude cutoff. Using the Lomb–Scargle method, we found that of the 5080 probes in the Quality Control dataset, 4112 were periodic.

One typical periodic gene (probe ID i3518_1) with its corresponding Lomb–Scargle periodogram and p-value curve is given in Figure 6. A non-periodic gene (probe ID j167_5) along with its corresponding Lomb–Scargle periodogram and p-value curve is given in Figure 7.

Fig. 6

Periodic gene expression pattern of i3518_1 in the P. falciparum gene expression dataset.

Fig. 6

Periodic gene expression pattern of i3518_1 in the P. falciparum gene expression dataset.

Fig. 7

Non-periodic gene expression pattern of j167_5 in the P. falciparum gene expression dataset.

Fig. 7

Non-periodic gene expression pattern of j167_5 in the P. falciparum gene expression dataset.

Of the 4355 probes that we selected as periodic from the Complete dataset, 744 now identified as periodic were not considered periodic by Bozdech et al. (2003). See the supplement for the gene list and the representative periodograms in this selected set. Among these 744 probes, 243 were processed by the Lomb–Scargle algorithm with a sample size less than 43. These 243 probes resolved to 151 distinct genes. The analysis of these 151 distinct genes presents a picture of sequence conservation generally similar to Plasmodium genome as a whole (Aravind et al., 2003), with 26% of proteins apparently unique to plasmodia, 36% shared only with other apicomplexan species, and the rest broadly conserved across the phylogeny. Predicted molecular functions of the latter group of genes, however, show uneven distribution of different functional classes of transcripts: the single largest category (13 genes, accounting for 9% of all newly identified periodic genes and for more than one-quarter of genes with predicted molecular function) consists of genes involved in ribosome assembly and RNA maturation (supplementary table). There were no other overrepresented functional categories in the set of 243. Some late trophozoite or early schizont-specific functions highlighted by Bozdech et al. (2003), e.g. proteasome subunits and TCA cycle enzymes, were missing from the newly recovered periodic gene set altogether. This suggests that the newly recovered 243 periodic genes perhaps mostly come from the ring or early trophozoite stage (compare Fig. 2 in Bozdech et al., 2003). The reasons for that are not clear at present, but most likely they include experimental artifacts and early phase shift for some genes (see supplement).

A histogram by period (see supplement) of the 4355 selected probes shows a dominant frequency at 48 h, as expected. A histogram of p-value shows two peaks and suggests a mixture of two distributions (see supplement).

Since the Overview dataset was the most restrictive Bozdech dataset, the 108 probes identified as non-periodic by the Lomb–Scargle algorithm were studied (see supplement). Most of these probes may be claimed as periodic by the Lomb–Scargle test with slightly more lenient selection criteria.

Fourier analysis directly provides an approximate value for the phase of a periodic curve. In the Lomb-Scargle computation, such a phase value is not provided directly, but it can be approximated by the location of the peak value in the expression profile after appropriate smoothing (see supplement).

### Missing value tolerance

As pointed out earlier, the P.falciparum gene expression Complete dataset of 6875 probes contains 15.66% missing values. In Bozdech et al. (2003), missing values in the profiles were imputed using LOESS smoothing before the FFT algorithm was applied. Bozdech et al. (2003) concluded that 79.5% of the profiles in the Quality Control set of 5080 probes, or 54.10% of profiles in the Complete set of 6875 probes, were periodic, where periodicity was defined as the FFT score of the profile being higher than a heuristic 70% cutoff score and the profile being in the top 75% of the maximum frequency magnitudes. Heuristic rules have been used to reject certain profiles from analysis if they contained too many missing values and the LOESS smoothing failed to provide good imputation for those missing values. Therefore, Bozdech et al. (2003) only analyzed gene expression time series with at most five missing values. The Lomb–Scargle method does not need missing value imputation and just treats a series with missing value as unevenly spaced. Our above analysis illustrated the effectiveness of Lomb–Scargle method in treating missing values.

We further examined extreme cases when many missing values are consecutive in the time series (see supplement). As the original data had missing values for all profiles at hour 23 and hour 29, we deleted all 7 consecutive time points from hour 23 to hour 29 in the Complete dataset of 6875 probes (with missing value rate increased from 15.66 to 26.08% in the dataset), and found that at the FDR level of q = 1 × 10−4, the Lomb–Scargle method identified 3617 probes as periodic (53% periodic versus 54.10% in original analysis). Of the 3617 identified, 3609 were in the original set of 4355 identified periodic genes. When we deleted 11 consecutive time points (with missing value rate increased from 15.66 to 34.41%) from hour 21 to hour 31, the Lomb–Scargle method can still identify 2506 (or 36%) probes as periodic; and of the 2506 identified periodic probes, 2502 were in the original set of 4355 identified periodic genes. These studies indicated that the Lomb–Sargle method has good ‘tolerance’ toward missing values.

## DISCUSSION

The Lomb–Scargle periodogram is a promising technique of searching for time series with periodic patterns. It requires no special treatment of missing values and can be used in data taken on unevenly spaced time points. Under the normal random noise assumption, the p-value for labeling each gene as periodic is easily calculated. There is no need for an ad hoc scoring system of power in peaks or for use of random permutations to assess significance of a peak. Weighting of data occurs on a ‘per point’ basis instead of on a ‘per frequency interval’ basis (Press and Rybicki, 1989). As all other methods in the search for periodic signals, the Lomb–Scargle periodogram requires the assumption of Gaussian noise on the error term. When the Gaussian noise assumption is invalid, the conclusion of Lomb–Scargle method might be misleading. Schimmel (2001a,b) also discussed the limitation of the Lomb–Scargle method when the signal is periodic with non-sinusoidal shapes or with outliers. These issues become topics of our further investigation. Our current studies indicate that for the P.falciparum dataset the Lomb–Scargle periodogram method is more appropriate, as it is better suited to handle unevenly spaced time series.

The question of how many time points should be planned to observe a particular periodical expression pattern might be raised at the planning stage of the experiment. We provide a simple guideline for estimating the sample size N (number of time points) of one profile for a given p-value as follows:

(5)
$N≈5[1−log10(p-value)].$
The derivation of Equation (5) is through a simple regression curve and is given in the online supplement. This equation only provides a guideline on how many data points should be planned if one given profile will be viewed as significant at the given p-value. After a large number of profiles are obtained, a given FDR level should be used to identify actually how many profiles are significantly periodic.

## CONCLUSION

The Lomb–Scargle periodogram algorithm is an effective tool for finding periodic gene expression profiles in microarray data, especially when data may be collected at arbitrary time points or when a significant proportion of data is missing.

We thank two anonymous referees whose comments greatly improved the manuscript. We appreciate discussions with Galina Glazko, Chris Seidel, Mary-Lee Dequeant, Oliver Pourquie and Mike Melko. J.C. is supported by the NSF grant DMS-0426148 for this research and is a visiting scientist at the Bioinformatics Center of the Stowers Institute for Medical Research while on leave from the Department of Mathematics and Statistics of the University of Missouri-Kansas City.

Conflict of Interest: none declared.

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## Author notes

Associate Editor: Joaquin Dopazo