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# Highlight for Query ‹Cat fluscreening›

## Improved Algorithmic Complexity for the 3SEQ Recombination Detection Algorithm

Improved Complexity

The central feature of 3SEQ was a reduction of an O(2 m+n) space-complexity problem into an O(mn3) problem, for computing the probability xm, n, k that a HGRW with m up-steps and n down-steps achieves a maximum descent of size k exactly. The descent does not need to be k consecutive down steps. The computations were done via auxiliary variables ym, n, k, j: the probability that a HGRW with m up-steps and n down-steps achieves a maximum descent of size k exactly and the minimum value achieved by the random walk is exactly j units below the origin. The y-variables can be computed recursively (Boni et al. 2007) by building a table of size mn3. The x-variables are then computed as follows:

(1)xm,n,k=∑j=0kym,n,k,j.

By separating out the first and last term in the sum above, and using the y-variable recursions, a nearly direct recursion can be written for the x-variables:

(2)xm,n,k=mm+nxm−1,n,k+nm+nxm,n−1,k+nm+nym,n−1,k−1,k−1−nm+nym,n−1,k,k.

The P value for observing a maximum descent of size at least k is defined by

(3)pm,n,k=∑l=knxm,n,l,

and recursions for the p-variables reduce to:

(4)pm,n,k=mm+npm−1,n,k+nm+npm,n−1,k+nm+nym,n−1,k−1,k−1.

The y-variables in equation (2) above—since the last two indices are equal—can be computed recursively by building one table of size mn2. The p-variables can be recursively computed by building a second table of size mn2. This means that the entire computational procedure of P values can be done with space complexity O(mn2) instead of the original O(mn3) presented in Boni et al. (2007). All computations were verified against the original approach.

This new approach allows larger probability tables to be built more quickly. Using the 2007 recursions, a table of size 700 × 700 × 700 was built in 9 h and used 5.1-GB RAM (2.6 GHz processor; 16-GB RAM). Using the recursions above, a table of 1,600 × 1,600 × 1,600 was built in 1 h and 42 min in a 10.4-GB memory footprint. Two other noteworthy improvements were made to the algorithm: 1) faster breakpoint calculations by using polymorphic sites only in breakpoint searches, and 2) a repeated subsampling feature that allows for comparison of data sets of different sizes; with this feature one can randomly subsample M sequences from multiple databases or sequence collections, and repeat the process to see how often these subsets exhibit recombination. The new source code and manual can be downloaded from http://mol.ax/3seq. When a P value falls outside the bounds of the table being used, the software substitutes in Hogan–Siegmund approximations (Hogan and Siegmund 1986) for the queried P value.

New Applications

The 3SEQ maximum descent statistic describes clustering patterns in sequences of binary outcomes, and is therefore not confined to recombination analysis. The statistic can be viewed as a generalization of the Mann–Whitney U statistic, in the sense that outcomes of one type (of a binary outcome variable) do not necessarily have to cluster or rank at the beginning or end of a sequence of data points. The maximum descent of a HGRW can be used to describe the clustering of one particular binary outcome in the middle of a sequence of binary outcomes; in other words, it is a 1D nonparametric clustering statistic. In recombination analysis, this is the clustering of one kind of informative site among all the informative sites (Han et al. 2010). To make use of this statistic easier for those working outside the field of recombination, we developed a web calculator (fig. 2) that computes exact P values for clustering in a sequence of binary outcomes, available at http://mol.ax/delta. For example, the sequence “AAAAABBBBABBBABBBAAAA” can be typed in and the calculator reports that the clustering of Bs in the middle of the sequence is significant at P = 0.0055.

We list two practical example uses of our nonparametric clustering statistic. First, seasonality can be assessed nonparametrically. If a particular population behavior or climatic characteristic (e.g., rain or no rain) can be noted to occur or not occur every day, then an ordered sequence of the days in the year will show if the occurrence of one of the behaviors is clustered and thus if this feature was seasonal in that one year. As a second example, when a process is expected to behave at an intermediate range or when an observation is expected to be made at intermediate values only, this pattern can be tested for nonparametrically. Dengue virus does not cause severity for all ages equally. One’s first dengue infection, occurring during childhood, is typically nonsevere; secondary infections, seen in older children and teenagers, have a higher chance of severity, whereas tertiary and subsequent infections, those that would occur in older age groups, are thought to be rare and/or subclinical (Gubler 1998; Wikramaratna et al. 2010). Thus, disease severity in a surveillance system should be seen in the intermediate age ranges, and this can be tested for nonparametrically by noting if each age band is overrepresented or underrepresented in the pool of patients experiencing dengue-like severe disease in a hospital. In fact, since all that is required here is a symptoms description, the identification of a vulnerable age range can be done for any set of symptoms.

Results and Discussion

To illustrate improved runtimes and memory usage of the new 3SEQ algorithm, we searched for recombinants among large sequence data sets of dengue virus serotype 2, Ebola virus, the coronavirus responsible for Middle-East Respiratory Syndrome (MERS) and Zika virus; see table 1. Full-length Zika virus sequences were downloaded from the NCBI Viral Variation Resource (Brister et al. 2014) and aligned with Muscle v3.8 (Edgar 2004). Full-length sequences of Ebola virus, dengue virus serotype 2, and the coronavirus responsible for Middle-East Respiratory Syndrome (MERS) were downloaded from NCBI and aligned with the online NCBI alignment tools. Ebola virus sequences were restricted to human viruses sampled in Africa after December 1, 2013. Dengue virus serotype 2 was chosen to include a particularly large and polymorphic alignment. As negative controls, we considered segments PB2 and NS from avian influenza A virus, subtype H5N1, originally analyzed in Boni et al. (2010); only sequences from the Influenza Genome Sequencing Project were included (Ghedin et al. 2005) and identical sequences were removed (when identical sequences were not removed, results using the new version of 3SEQ were identical to the results in table 1 of Boni et al. 2010).

The new version of the software—run with a P value table of size 1,200 × 1,200 × 1,200—had faster computation times than the previous version and was able to comfortably accommodate alignments with thousands of polymorphic sites. Table 1 shows the results of all runs. Note that because 3SEQ evaluates all triplets in a data set, the run time of the algorithm scales as the cube of the number of sequences and linearly with the alignment length. As informative sites can sometimes be clustered in short regions of the genome, 3SEQ will report these short segments as recombinant. For this reason, an additional column is included in table 1 showing the number of sequences that were identified as recombinant with both inherited regions being longer than 500 nt; if one of the recombinant regions is very short, it is difficult to confirm the recombination results with a phylogenetic analysis of the two identified parental segments.

Starting with the analysis on the two negative control data sets, no recombinant segments longer than 500 nt were detected in either avian influenza alignment. Both of these runs took <30 s. The genomic alignments of MERS and Zika virus contained 1,150 and 2,792 polymorphic sites, respectively, and >99.9% triplets were able to be tested for mosaicism with exact P values. These runs took <2 min. As expected from a recent analysis by Dudas and Rambaut (Dudas and Rambaut 2015), the MERS sequence data set was highly recombinant, with 100 out of 164 sequences being identified as such. For Zika, 6 out of 157 virus sequences were identified as recombinant, consistent with earlier analyses supporting the presence of recombination in the evolutionary history of Zika (Faye et al. 2014; Zhu et al. 2016); details of the recombinants, parents, and breakpoints are included in the Supplementary Material online. The Ebola virus and dengue virus alignments each contained around 1,000 sequences. The Ebola virus data showed no evidence of recombination. The dengue alignment was the most diverse of all the tested data sets with 6,151 polymorphic sites; 99.4% of the triplets in this data set were able to be evaluated with exact P values. A total of 36 out of 1,108 dengue sequences were identified as recombinant (see Supplementary Material online). Several previous analyses of dengue virus have shown evidence for intraserotype recombination in dengue (Holmes et al. 1999; Worobey et al. 1999; Uzcategui et al. 2001; Aaskov et al. 2007; Waman et al. 2016, 2017). The results presented here, as well as those of Waman et al. (2017), suggest that recombination in dengue is infrequent.

In general, when recombinants are identified by a mosaicism statistic like the one used by 3SEQ, a phylogenetic analysis should be performed to ensure that the recombination signal is preserved when the entire evolutionary history of the sample is taken into account. The size of modern data sets presents two challenges here. First, as the number of available sequences increases, the choice for phylogenetic inference tools drifts to more approximate methods, as thorough explorations of tree space become computationally expensive for large numbers of sequences. This reduces our confidence in phylogenetic incongruence signals that we observe in these data. Second, genome-level analyses in highly recombining organisms are likely to result in a subdivision of the genome into many nonrecombinant blocks. Inferring phylogenies for all blocks individually will be computationally expensive, as will the subsequent analysis of identifying specific phylogenetic incongruences among the trees. The next generation of recombination detection methods should focus on these computational challenges.