 Research article
 Open Access
 Published:
MS4  MultiScale Selector of Sequence Signatures: An alignmentfree method for classification of biological sequences
BMC Bioinformatics volume 11, Article number: 406 (2010)
Abstract
Background
While multiple alignment is the first step of usual classification schemes for biological sequences, alignmentfree methods are being increasingly used as alternatives when multiple alignments fail. Subwordbased combinatorial methods are popular for their low algorithmic complexity (suffix trees ...) or exhaustivity (motif search), in general with fixed length word and/or number of mismatches. We developed previously a method to detect local similarities (the Nlocal decoding) based on the occurrences of repeated subwords of fixed length, which does not impose a fixed number of mismatches. The resulting similarities are, for some "good" values of N, sufficiently relevant to form the basis of a reliable alignmentfree classification. The aim of this paper is to develop a method that uses the similarities detected by Nlocal decoding while not imposing a fixed value of N. We present a procedure that selects for every position in the sequences an adaptive value of N, and we implement it as the MS4 classification tool.
Results
Among the equivalence classes produced by the Nlocal decodings for all N, we select a (relatively) small number of "relevant" classes corresponding to variable length subwords that carry enough information to perform the classification. The parameter N, for which correct values are datadependent and thus hard to guess, is here replaced by the average repetitivity κ of the sequences. We show that our approach yields classifications of several sets of HIV/SIV sequences that agree with the accepted taxonomy, even on usually discarded repetitive regions (like the noncoding part of LTR).
Conclusions
The method MS4 satisfactorily classifies a set of sequences that are notoriously hard to align. This suggests that our approach forms the basis of a reliable alignmentfree classification tool. The only parameter κ of MS4 seems to give reasonable results even for its default value, which can be a great advantage for sequence sets for which little information is available.
Background
The classification of biological sequences is one of the fundamental tasks of bioinformatics, and faces special challenges in the genomic and postgenomic era. While it is a classical paradigm to base it on an initial multiple alignment of the sequences, a current trend is to provide alignmentfree classification methods (subwordbased [1], kernelbased [2], composition vectorbased [3, 4]...), in order to tackle datasets that cannot be amenable to multiple sequence alignment (MSA) methods. Approaches based on kmers have also been used for more than a decade to detect anchoring zones for whole genome alignments [5–8].
In this paper, we describe a method for the alignmentfree classification of families of nucleic or protein sequences (composed of a few hundreds of members). Our aim is to rapidly detect similarity segments shared by these sequences without having to consider the order in which they occur inside the sequences. Our approach allows us to take into account shuffled domains as well as repeated segments.
The local similarity detection uses a previously described method called Nlocal decoding [9]. The basic principle of the Nlocal decoding is to rely on the occurrences of similar substrings in sequences to cluster together positions in the sequences. More precisely, two positions in the considered sequences (that we will call "sites" for short) are directly related when they occur at the same position in two equal substrings of fixed length N. The Nlocal decoding clusters together all indirectly related sites, that is, sites related by a chain of direct relations. This results in a partition of the set of sites. For each subset of clustered sites (an equivalence class or simply class), the segments of length 2N  1 which are centered on the sites exhibit local similarities. Although it is based on exact matches, the indirect relation scheme results in the inclusion of an a priori unknown number of mismatches.
We have previously used successfully this kmer based method for alignmentfree classification [10], without being able to solve the delicate problem of tuning the parameter N. In the present paper, we tackle this problem by developing a procedure to select among all the segments of similarity detected by Nlocal decoding for all N, a subset on which to base the classification. We call this alignmentfree classification method MS4, for MultiScale Selector of Sequence Signatures.
The Nlocal decoding has been efficiently implemented using suffix trees. Like in any kmer based approach, there is no sensible criterion to fix a value of the parameter N. Here, we follow how the partition of sites varies with the parameter N. When N increases, site classes tend to split into several subclasses, while for too low values of N, classes tend to group sites that do not share any detectable similarity. MS4 attempts to select among all these classes of sites those that correspond to relevant homologous segments. More precisely, MS4 selects for a given site the smallest N such that the average number of occurrences per sequence of the equivalence class of this site is smaller than a given threshold κ. The resulting values of N are different for different sites, and adapt to the context of appearence of the site among the studied set of sequences. The parameter κ, unlike N, has a sensible global interpretation, and can be tuned to a value reflecting the maximum number of repetitions in the sequences. Finally, the classes selected by MS4 are used to compute a dissimilarity matrix on which the classification is based (using the NeighborNet option of SplitsTree [11, 12]).
In this paper, we describe the implementation of the MS4 classification tool, which is accessible via a Webbased interface. We also give a validation on some real biological data that are not so easy to classify: MS4 is illustrated on several families of HIV/SIV sequences. These sets have already been classified by us with the help of Nlocal decoding method [13], and it was shown that the Nlocal decoding classes correspond to segments of homology for these sequences [10]. The results obtained in [10] were in good agreement with the accepted classification [14, 15], for several values of N. These "good" values are however datadependent and hard to guess. The approach described in this paper replaces this parameter with the more intuitive parameter κ.
Our present results show that MS4 gives correct classifications on coding and noncoding regions of HIV/SIV. Moreover the results are robust with respect to the variations of the parameter κ. In fact, even on sequences containing repetitions (like the noncoding regions of the HIV/SIV LTR), the choice of κ = 1 gives satisfying results. Therefore, MS4 may be expected to give reasonable results for this default value for κ when no other information on the sequences is available.
Methods
As mentioned in the Background section, we use the Nlocal decoding (NLD) in order to produce partitions of the set of all sites in the sequences under study [9]. A short recapitulation of NLD is found here. The central part of this paper is the introduction of an object that describes the embedding of successive partitions as N increases. It turns out that this object is a tree. The tree structure is essential, because it provides a criterion for choosing "relevant" partitions of sites, which may occur at several values of N. We use the chosen classes to construct a dissimilarity matrix between sequences (taxa). This matrix becomes then the input for standard tree construction methods (SplitsTree4 [11, 12] in our case).
NLocal Decoding
We consider a collection S of sequences s over a finite alphabet . The site space consists of all pairs σ = (s, p) where s is a sequence, and p a position in it. This set is
where ℓ (s) is the length of sequence s. The NLD procedure starts with a collection of sequences and with an integer N ≥ 1. It consists of two steps:

1.
To every site σ in ∑, associate a neighborhood of length 2N  1, consisting of σ and of N  1 sites on each side of σ (neighborhoods that are too near the beginning or the end of a sequence are accordingly truncated, but this case will not be considered for simplicity's sake in the rest of the description). This neighborhood carries a word W of length 2N  1. We consider all subwords w of length N of this word W. They can be "identified" by their position relative to σ, i.e. the index of the beginning of w inside W. The subword w of W at relative position i will be denoted by w_{ i } . Given two sites σ and σ', we say that they are directly related if there exists an i such that the subword w_{ i } of W is identical to the subword of W' . If two sites σ, σ' are directly related, we write σ ≃_{ N } σ'.

2.
We define the equivalence relation ~_{ N }as the transitive closure of ≃_{ N }. In other words, we say that σ _{1} ~_{ N } σ _{2} if there is a chain of directly related sites connecting σ _{1} and σ _{2}.
We illustrate this on an example (Fig. 1). We consider here a set of protein sequences, and examine one of the equivalence classes obtained by Nlocal decoding with N = 7. This class consists of 6 sites. The first site is described by the pair (0,571): this means that it lies at position 571 of the sequence number "0", and similarly for the other five sites. Since N = 7, the neighborhoods around these sites are of length 2N  1 = 13. The words in these neighborhoods are shown on the picture, with the central letter displayed in red.
Directly related sites are connected by solid lines. For instance, the sites (0, 571); (3, 630) and (8, 614) share the word LREIDED starting at the third position of their environment. The sites that are related (but not directly related) are connected by broken lines. For instance, the sites (1, 580) and (5, 528) are connected by the chain (1, 580) → (0, 571) → (3, 630) → (5, 528). The fact that every site is connected to every other site means that this set of sites is a class.
The Partition Tree
A recurring problem of Nmerbased methods is that there does not seem to be a good criterion to tune this parameter N to an acceptable value. There is moreover no real reason to believe that a single "optimal" value will always be meaningful, since the similarity between sequences can depend very much on the position of neighborhoods in sequences.
In the case of Nlocal decoding, we combine the different equivalence classes for various values of N by introducing a new construction, the partition tree, which encodes the way in which equivalence classes for successive values of N are related. This tree will allow us to choose a set of "relevant" NLDclasses. Let ℰ ^{N} be the partition of ∑ induced by ~_{ N }.
Lemma 1. For all N ≥ 0, the partitions ℰ^{N}satisfy ℰ^{N+1}⊂ℰ^{N}.
Proof. Compare the partitions of ∑ produced by ~_{(N+1)}with the partitions produced by ~_{ N }. If any two sites σ_{1} and σ_{2} are ~_{(N+1)}equivalent, we have to show that they are ~_{ N }equivalent. Notice that σ_{(N+1)}equivalence is reduced to a set of direct ≃_{(N+1)}relations, and that σ_{1} ≃_{(N+1)}σ_{2} implies trivially σ_{1} ≃ _{ N }σ_{2}. If two neighborhoods share a word of length N + 1 at a given relative position, they also share words of length N at the same relative positions.
This simple lemma is crucial, and corresponds to the intuitive idea that it is harder to lump together big words than small words. We are now ready to define the partition tree.
Definition 1. For N > 0, denote by ℰ ^{N} the set of equivalence classes defined by the relation ~_{ N }. Letting ℰ ^{0} = {}(which will correspond to the root of the tree), we can encode the set V = ∪_{i ≥ 0}ℰ^{i}of equivalence classes for different values of N into the partition tree P = (V, E^{P}), defined by
In other words: the vertices of P are all the equivalence classes that correspond to ~ _{ N } for all values of N. The edges are drawn between pairs of classes that correspond to successive values of N and such that one is a subset of the other. By the above lemma, any two sites that are (N + 1)equivalent are also Nequivalent. On the other hand two sites that are Nequivalent are not necessarily (N + 1)equivalent. In other words, the Nclasses split as N increases. The edges are drawn precisely between any Nclass C and all the (N + 1)classes into which C splits. From this definition, it is clear that any vertex of P has at most one ancestor, i.e. that P is a tree. Finally, for memory saving purposes, all valency 2 nodes are suppressed from P (resulting in the compacted partition tree). Examples of partition trees are given in Fig. 2 and Fig. 3.
A choice of classes
When we examine Nequivalence classes for all possible N, we face a deluge of information, moreover altogether redundant. We shall now use the tree of partitions to alleviate this problem. Given any set C of sites, we can define the size of C as the number of sites in C and the spread of C as the number of sequences which contain at least one element of C. Define κ(C) as the ratio between the size and the spread of C as follows.
For a given value κ ≥ 1, the condition κ (C) ≤ κ means that the average number of occurrences of class C per sequence where it occurs is less or equal than κ. In particular, κ (C) = 1 means that no sequence contains more than one element of C (of course we take here C to be an NLDclass). We call the parameter κ the maximum average repetitivity. We use this parameter to select nodes in the partition tree that satisfy κ (C) ≤ κ.
This condition is not sufficient to make these classes relevant (see an example in Fig. 2). Indeed, the bottom of the partition tree is occupied by classes corresponding to large N, which occur in only one sequence. Such classes are of no interest. In order to find relevant classes, we have to "climb upward" (towards smaller values of N). Since any vertex of a tree has only one ancestor, the following definition does make sense.
Definition 2. An NLD class C will be called κ relevant, if it satisfies κ (C) ≤ κ, while its ancestor does not.
The MS4 method consists in choosing all relevant classes in a set of sequences, and ignoring the others. The algorithm describing the implementation of MS4 is given in section Appendix. An explicit toy example on which we can see both the Nlocal decoding and the selection of relevant classes at work for κ = 1 is shown in Fig. 3.
The Dissimilarity matrix
At the end of the MS4 procedure, each sequence can be rewritten, by replacing the letter originally found at a given site by the identifier of the relevant MS4class to which the site belongs (e.g. Fig. 4). We use the number of MS4 classes shared by 2 sequences to define a similarity index in a similar way as described in [10]. This measure is closely related to the percentage of identity classically used for sequence comparison.
Given any two sequences seq_{ i } and seq_{ j } , we compute a number d_{ ij } as follows. For a class c, let n_{ i } (c) be the number of occurrences of c in seq_{ i } . Denote by C_{ ij } the set of relevant classes that have representatives both in seq_{ i } and seq_{ j } . Since the two sequences can contain a different number of occurrences, we put . Let ℓ be the minimum of the lengths of seq_{ i } and seq_{ j } . We define then a dissimilarity d_{ ij } by
In fact, n_{ ij } is the sum of local similarities shared by 2 sequences. Any exact common word of length M corresponds to M common MS4 classes (e.g. Fig. 4).
When κ = 1, n_{ ij } is simply the number of relevant classes having representatives in both seq_{ i } and seq_{ j } . This dissimilarity matrix is used as input in NeighborNet of SplitsTree4 [11, 12] to produce the split networks displayed in Fig. 5 and Fig. 6.
Results and Discussion
MS4classification of complete HIV/SIV genomes
We have applied the MS4method, followed by a computation of the dissimilarity matrix (see section Methods), and the construction of a splitnetwork (with the option NeighborNet [12] of SplitsTree4 [11]) to a family of 70 HIV/SIV genomes. The input for the calculation of the dissimilarity matrix consists of the classes selected by MS4 with κ = 1, for values of N between 2 and 60. We use here the same 70 nonrecombinant HIV (Human immunodeficiency virus)/SIV (Simian immunodeficiency virus) nucleotide sequences that we studied previously in [10] by using the Nlocal decoding method. These sequences include four incomplete (gag) sequences (HIV2 subtype C, D, E, F). These short sequences are subtyped in the sequence databases, so they appear to have kept subtyping signals that are in the complete genome sequences. The 66 complete sequences range in length from 8555 to 11443 nucleotides. All these sequences can be retrieved from the Los Alamos HIV sequence database [16] (their accession numbers are given in Fig. 5). The accepted groups are as follows:

1.
HIV1 group M (subtypes AD, FH, J, K; A is split into A1 and A2, and F is divided into F1 and F2),

2.
HIV1 group N,

3.
HIV1 group O,

4.
HIV2 groups A, B, G,

5.
SIVCPZ (chimpanzee)

6.
SIVSMM (sooty mangabey)
We produce a network by application of SplitsTree4 on the basis of a dissimilarity matrix given by the MS4 method. Fig. 5 shows the network obtained by our calculation. The network is quite treelike. The two types of HIV are clearly distinguished: HIV1 is closer to SIVCPZ and HIV2 is closer to SIVSMM. The HIV1 group M, on the left, is clearly separated from the rest. The nine subtypes of HIV1 group M (major) cluster distinctly, with subsubtypes significantly more closely related to each other (A1 and A2, F1 and F2, B and D that should be regarded as subsubtypes [14, 15]). Subtype K is more distant from subsubtypes F1 and F2 than these are from each other, but closer to them that to other subtypes. The HIV1 group N intercalates between HIV1M and SIVCPZ (CAM3, CAM5, GAB, and US). The HIV1 group O is intercalated between these CPZ and CPZANT that is the borderline in the HIV1/SIVCPZ lineages. HIV2 groups also form clear clusters, respectively, including C, D, E, and F that cover about half of the gag region.
Within the HIV2 viruses, notice that the HIV2 area, with the exception of the groups A and G, is less treelike than the rest. From the aspect of the network, it seems that HIV2C tends to cluster both with HIV2B and with SIVSMM. Another example is SIVSMMMAC which tend to group with both HIV2F and with HIV2D. Notice that the sequences HIV2C, HIV2D and HIV2F are short.
These groupings, which were obtained without alignments and without parameters, agree with accepted classifications.
In our previous paper, we varied the parameter N and we selected values of N that agree with existing knowledge; it turned out that correct tree topologies were found for N in the range from 13 to 35. The fact that the same groupings were found by the MS4 method with no other input than the sequences themselves gives us some confidence in the validity of this approach.
HIV/SIV sequences from the Compendium 2000
We have also calculated a split network from the 46 HIV/SIV complete nucleotide sequences of the Compendium 2000 (HIV1/HIV2/SIV Complete Genomes), and compared it with a tree available at [17]. The result of our calculation is treelike, and agrees with the topology of the Compendium tree (Additional File 1).
Major genes of HIV/SIV
The major genes (gag, pol, env) of the HIV/SIV sequences (see above) were also tested.

1.
For gag we have 70 sequences: 66 complete sequences (1473 to 1569 nucleotides in length) and 4 partial sequences covering about half the gag regions (771781 nt).

2.
For pol : we have 66 complete sequences (29933360 nt).

3.
For env : we have 66 complete sequences (24992658 nt).
The regions pol and env were unavailable for the 4 HIV2 groups CF. The trees obtained for gag, pol and env give a good classification and the same description can be done for them as that detailed above for the 70 complete sequences (Additional Files 2, 3, 4).
MS4classification of short sequences: nef and noncoding LTR sequences
Noncoding LTR
In order to test our method, we have also looked at parts of the HIV/SIV genomes that are notoriously hard to align due to inner repetitions in the sequences. One of them (retrieved from 43 of the 70 sequences) covers the noncoding part of long terminal repeat (complete noncoding LTR region or at least its portion including the polyadenylation signal AATAAA). The lengths of this part range from 211 to 328 nt in the HIV1/SIVCPZ subset, and 433 to 508 nt in the HIV2/SIVSMM subset. These short noncoding segments contain many duplications/insertions/deletions that make them difficult for traditional alignmentbased phylogenic studies.
The network obtained (Fig. 6) shows again a clear separation between HIV1 and HIV2, even though it was constructed with short and "difficult" subsequences. It is less treelike than the network obtained from the complete sequences, which is not surprising. The comparison between Fig. 5 and Fig. 6 show several features which may require further investigation: While the complete genomes produce very strong grouping of the subtypes HIV1M, the noncoding LTR show several discrepancies for these subtypes. The clustering of HIV2 (and their groups), SIVSMM, HIV1O, SIVCPZ and HIV1M is correct. The network (Fig. 6) is similar to the tree in our previous paper [10].
It is interesting to notice that the two HIV1N are not very clearly grouped together. The sequence AJ271370_HIV1N is grouped both with the chimpanzee group (SIVCPZ) and with AJ006022_HIV1N. On the other hand, AJ006022_HIV1N tends to group both with the other HIV1N and with AF061640_HIV1MG (but less clearly). In the Neighbor Joining tree of [10], the two HIV1N are grouped together with a bootstrap value of 95% and connected with the group SIVCPZ with bootstrap value of only 55%.
Even though our results show the difficulties of treating the noncoding part of LTR, it should be stressed that our method says something about these sequences. By contrast, these sequences are not tractable by standard alignmentbased methods [10].
The featured sequences are reputedly hard to align, because they exhibit several repeated segments. MS4, used together with SplitsTree4, gives relevant results on these data that are usually set aside for the typing and subtyping of HIVSIV, for lack of sufficient phylogenetic signal. This observation was already present in our previous study which used only the Nlocal decoding method. In this previous study, we proceeded to the careful  and tedious  scrutiny of several trees, resulting from the NLD method for various values of the parameter N. We showed that, for the non coding LTR sequences, the best tree (best fitting the reference classification) was obtained for the value N = 11. The splits networks that are obtained by MS4, or by NLD for N = 11 (Additional File 5), are similar and yield correct groupings of the noncoding LTR. One only notes a discrepancy inside group M, NLD giving a better clustering of the A subtypes, while MS4 groups H subtypes better.
It should be noticed that when we have here varied the maximum average repetitivity κ from 1.0 to 10.0 (by step of 0.5), the obtained classifications turned out to be remarkably robust to this variation (e.g. Additional Files 6 and 7).
NFkB region
We focus now on the noncoding region of LTR, to show how MS4 deals with repetitions in the sequences. The fig. 4 and the figures in Additional Files 8 and 9, show the binding site of the transcription factor NFκ B and its flanking regions [10]. This site is characterised by the signature GGGACTTTCC[AG], which is present one or two times in the noncoding region of the LTR of HIV/SIV genomes (one or two additional imperfect copies may exist).
It clearly appears that, although the parameter κ is here set to 1, this zone contains relevant classes over the whole repeated region. Each repeated motif of the NFκ B pattern is identified by a different set of MS4classes corresponding to N larger than the length of the repeated motif. Fig. 4 illustrates how the MS4classes on this repetitive region participate to the overall MS4 classification. We clearly distinguish the HIV1N group which has some similarity with SIVCPZ, the group HIV1O, and the group HIV1M in which we can distinguish e.g. the subtypes HIV1M/G, C and J. The HIV2 sequences are clearly separated into three groups A, B and G which show similarities with SIVMM. This example illustrates the facts that (a) Repeated segments are taken into account by the MS4 method, even for κ = 1 (which corresponds to one repetition of a class per sequence) and (b) each repeated segment participates in the classification of our set of sequences. Fig. 4 also illustrates the way that the rewriting of sequences in terms of MS4classes defines the dissimilarity between sequences (See Eq.3). For instance, in the sequences HIV1M/J, a class, such as 'A49', corresponds to an exact word of length 49 shared by the two sequences. These classes correspond to the value N = 49 when the similarity concerns only 2 sequences (this is a straightforward exact match) but a smaller N when it is shared by more than 2 sequences (most often N = 18 for the binding site of NFκ B).
The nef sequences
We have also studied the 66 nef sequences (292783 nt). The classification by MS4 is correct except for a few discrepancies (that have already been described in [10]): in the group HIV1M, subsubtypes F1 and F2 mix together, and the position of subtype K is uncertain between F1/F2 and J (Additional File 10). In both cases (non coding LTR and nef) that we just saw, it is obvious that a full classification is not possible due to conflicting signals, and it is necessary to find homologous sites on a multiple alignment (as we did for LTR with Nlocal decoding in [18]).
Here we examine more precisely nef, a sequence which is important for the virulence of the virus. We show a multiple alignment of the 66 sequences (Fig. 7). The Dialign [19] multiple alignment has been manually edited by putting in the same column the sites corresponding to one MS4 class (See section Methods). The results have been visualized with the help of Jalview [20] which is a multiple alignment editor which allows the user to define, for each color, the set of sites that carry that color. The fig. 7 shows an unambiguous sector of this alignment. The identifiers of the classes are not shown on the figure, but Jalview fortunately allows the user to click on a letter and recover this information. Identical letters (A, C, G or T) that are on the same column and with the same colour belong to the same class. We clearly see on Fig. 7 that there are classes that appear only in HIV1, classes that appear only in HIV2, and classes that appear in both. The fact that sequences can be correctly classified by MS4, suggests that the majority of sites regrouped in one class correspond to blocks of homology between sequences.
Conclusions
This paper gives a description of the MultiScale Selector of Sequence Signatures (MS4) method and uses it for an alignmentfree classification (virtually parameterfree) of a family of sequences. The core of the method consists in the selection of "relevant" classes of segments, which are assumed to carry similarity information, although the criterion for grouping them together is purely combinatorial (classification by context [9]). The point of our method is that it does not require the specification of a word length parameter and it does not consider only exact words.
The user may choose a parameter κ which reflects the average repetitivity of the set of sequences under consideration. The default value κ = 1 yields satisfying results in the examples we have considered so far. MS4 sets automatically a local length parameter N which depends on the starting set of sequences and local similarities between sequences.
In this paper, we test the method on a set of wellstudied HIV/SIV sequences [10, 14, 16] on which one of us is an expert [10, 18]. The results obtained are in excellent agreement with the accepted knowledge. The MS4 method has also been applied to other data (not shown here). It should be noted that it is not accurate on too small datasets. In our experience, this program can be applied in its present state to sets composed from a dozen to a few hundreds of sequences (datasets consisting of a few Mb). Note also that MS4 works for protein data as well as genes (e.g. Additional File 11, and [21]).
As N decreases, the Nlocal decoding method detects weaker similarities, before being flooded by spurious ones [13]. Concerning the selection of equivalence classes, our aim is to select as many nonredundant homologous segments as possible, while keeping the background noise at a low level. Our default criterion for "relevant" classes locally sets N above this level, at the cost of losing some occurrences of repeated similar segments. By tuning the parameter κ, it is possible to accept a maximal average quantity of repetitions below a given threshold. When κ is set too high, the result of the classification can degenerate, and tends towards the mere lettercomposition criterion as κ tends to infinity. By default, we exclude repetitions of any given class in the same sequence. However, even for this value, the repeated segments are not lost altogether. When the value of N becomes larger than the size of the repetition, the MS4 classes only change (as subsets of sites) up to the value where different repetitions are assigned a different MS4 class. This can indeed result in a clearer identification of the distinct homologous repetitions. This phenomenon is illustrated on the well known repetitive NFκ B binding regions of noncoding LTR (see Fig. 4 and Section Results subsection NFκ B region). Although our current criterion can be tuned to take repetitivity into account, the classifications of the HIV/SIV sequences turn out to be remarkably robust to the variations of the parameter κ (for example see in additional files 6 and 7 the resulting SplitsTree from non coding part of LTR sequences for κ = 5 and 10). Nevertheless, it seems desirable to get a more significant criterion, statisticalbased, to prune the tree formed by the whole set of embedded partitions (See section Methods subsection Partition Tree and Choice of classes). The last step concerns the computation of the similarity matrix. Our similarity is straightforward: it consists in counting the number of MS4 classes that are shared by 2 sequences. This corresponds to a usual basic scheme for the comparison of two nucleic sequences (% identity). We group together similar sites (according to MS4) as equivalence classes. As a result, a segment of identity of length N between sequences will result in N MS4 classes (Additional Files 8 and 9). Each MS4 class has an equal weight in our dissimilarity computation (See Eq.3). In the case of an exact repeated subword of length N between two sequences, the contribution of this subword to the dissimilarity is exactly N.
However, it could be also possible in the future to obtain a SplitsTree by constructing directly the splits themselves on the basis of the selected segment classes, and to avoid the computation of the matrix. The presence of incompatible signals (resulting in parallelograms) in the network constructed by SplitsTree4 [11] from MS4 similarity matrices for short sequences, shows, as otherwise expected, that this method must usually be completed by visual expertise. This can be achieved by coupling MS4 with multiple alignment editor like Jalview [22] (See Fig. 6 and Fig. 7). Therefore, the classes detected by MS4 can be used to help the manual editing of a multiple alignment. We also use them to determine anchor points for the multiple alignment programs [21].
Availability
A userfriendly Webinterface is available at http://stat.genopole.cnrs.fr/ms4/. It takes as input a file with sequences in fasta format and gives the dissimilarity matrix in nexus format to run the option NeighborNet of SplitsTree4. The allowed parameters are κ (default value 1) and the range of N for computing the partition tree (default values: from 2 to N_{max} which is the size of the maximal repeated word shared by two sequences in the dataset). The Python code is avalaible in Additional File 12 and upon request from the corresponding author (for some implementation details see the algorithm in section Appendix).
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Acknowledgements
We thank M. Pupin, M. Nadal and A. Grossmann for helpful discussions, M. Baudry for assistance with the code, and B. Prum and J.L. Risler for useful suggestions about this manuscript. EC was supported by Genopole and by the Deutsche Forschungsgemeinschaft under reference DFG Project MO 1048/61. We thank anonymous referees for their comments.
Appendix
Algorithm 1 Main steps to select relevant classes in the partition tree
Input: All equivalence classes E ∈ ℰ ^{n} , for n ∈ {n_{ min } , ..., n_{ max } }
Input: ∑ set of all sites
1:
2: // Initialize the MS4 equivalence classes
3: RelevantECList←{∅}
4:
5: // Initialize the partition tree P: add leaves (∈ℰ^{∞}) in P
6: for each site σ ∈ ∑, ∑ set of sites do
7: // Add a new node in the partition tree P and initialize κ
8: addNode(P, σ)
9: κ (σ)←1
10: end
11:
12: // Main loop
13: n ← n_{max}
14: while n ≥ n_{ min }do
15: for each equivalence class E ∈ ℰ ^{n}do
16: // Build A, the highest ancestor set of E in P
17: ← {∅}
18: for each site σ ∈ E do
19: ← ∪ getHighestAncestor(P, σ)
20: end
21: // Compact the partition tree if only one ancestor is found
22: if card() > 1 then
23: // Add a new node in the partition tree P
24: addNode (P, E)
25: // Compute κ(E): (E) is the number of sequences where equivalence class E appears
26: κ (E) ← card(E)/(E)
27: for each equivalence class A ∈ do
28: // Set inclusion relation A ⊂ E in the partition tree P
29: addEdge(P, (E, A)).
30: if κ (E) <κ and κ(A) ≥ κ then
31: RelevantECList ← RelevantECList ∪{A}
32: end
33: end
34: end
35: end
36: n ← n  1
37: end while
38:
39: // Create root node (i.e. ℰ^{0}), connect it to the highest ancestors in P
40: // Same as above
41:
42: return RelevantECList
Author information
Additional information
Authors' contributions
EC and FP conceived the method and wrote part of the code, GG made the code available and implemented the Web interface, IL gave the original idea for the biological application and expertised the results, GD wrote part of the code, CD and EC drafted the manuscript, CD produced the results, expertised them, and supervised this work. All authors read and approved the final manuscript.
Electronic supplementary material
Additional file 5: Network for LTR sequences obtained with NLD. The SplitsTree4 network for noncoding LTR sequences computed with the NLD method for a fixed word length of N = 11. NLD method is described in [10], it uses a similar similarity index but with a fixed length word. In [10] we used Neighbor Joining instead of Splits Networks. (PNG 67 KB)
Additional file 8: Similarity blocks found by MS4 in non coding LTR sequences. Superposition of MS4 classes on a manually expertised alignment of the non coding part of 43 HIVSIV LTR sequences focused on NFκ B region. This is a nucleotide sequences alignment of the 43 noncoding LTR sequences. Apart from minor modifications the alignment is the same as that in Fig. 5 in [10]. The alignment is focused on the transcription factor NFκ B binding site (GGGACTTTCC[AG]) and its flanking regions. The names of sequences are indicated with their accession number in Los Alamos HIV sequence databank. The sequence are regrouped according to their phylogeny. The letters are rewritten by applying the MS4 method to the whole non coding LTR sequences. The MS4 identifier is constructed as follows: e.g. C24_8 (class C24 for a N value of 8). Identical recoded letters that are in the same columns are displayed in the same colour. When they are not all aligned on the same column no colour is used (as well as when they are unique in this part of the alignment). The repeated motifs inside one sequence are put one under the other. Therefore the sequences are often written on several lines to highlight similarities between sequences and inside sequences. Most often the similarity blocks are aligned and the great majority of identical indexed letters are on only one column. (XLS 54 KB)
Additional file 9: Region of NF κ B fixation site. The complete alignment, part of which is featured in Fig. 4. This figure corresponds to the figure in Additional File 8. The colours are the same as in the figure in Additional File 8 but in this figure the MS4 identifier has been simplified as follows: we have just indicated the letter and the value of N. Therefore it can be that two different MS4 classes that lie on the same column, with the same letter and the same N value are only distinguished by their colour (e.g. A18 and also T18 HIV1M/G, that are red or green). (PDF 32 KB)
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Corel, E., Pitschi, F., Laprevotte, I. et al. MS4  MultiScale Selector of Sequence Signatures: An alignmentfree method for classification of biological sequences. BMC Bioinformatics 11, 406 (2010). https://doi.org/10.1186/1471210511406
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Keywords
 Equivalence Class
 Dissimilarity Matrix
 Suffix Tree
 Partition Tree
 Relevant Classis