Comparing sequences without using alignments: application to HIV/SIV subtyping
© Didier et al; licensee BioMed Central Ltd. 2007
Received: 26 July 2006
Accepted: 2 January 2007
Published: 2 January 2007
In general, the construction of trees is based on sequence alignments. This procedure, however, leads to loss of informationwhen parts of sequence alignments (for instance ambiguous regions) are deleted before tree building. To overcome this difficulty, one of us previously introduced a new and rapid algorithm that calculates dissimilarity matrices between sequences without preliminary alignment.
In this paper, HIV (Human Immunodeficiency Virus) and SIV (Simian Immunodeficiency Virus) sequence data are used to evaluate this method. The program produces tree topologies that are identical to those obtained by a combination of standard methods detailed in the HIV Sequence Compendium. Manual alignment editing is not necessary at any stage. Furthermore, only one user-specified parameter is needed for constructing trees.
The extensive tests on HIV/SIV subtyping showed that the virus classifications produced by our method are in good agreement with our best taxonomic knowledge, even in non-coding LTR (Long Terminal Repeat) regions that are not tractable by regular alignment methods due to frequent duplications/insertions/deletions. Our method, however, is not limited to the HIV/SIV subtyping. It provides an alternative tree construction without a time-consuming aligning procedure.
Almost all existing methods for comparing biological sequences are based on certain implicit (and necessary) assumptions about the kinds of changes sequences undergo during their putative evolution. Under these assumptions, some changes like permutations and inversions are often ignored (see however [1–3]) while other kinds of changes (insertions/deletions, for instance) are poorly evaluated because they do not follow regular evolutionary models. As a result, the regions in which these changes occur are often treated as ambiguous and are deleted from sequence alignments before further evolutionary analysis. In addition, as no single alignment method copes with tremendous variety of sequence data, expert editing (manual alignment) becomes often necessary. This is particularly important if the sequences have successive duplications/deletions [4, 5], and permutations.
To solve these problems, there has been work on methods of comparing sequences without alignments. An intuitive way of comparing sequences without alignments is to compare their compositions, i.e. the frequency of nucleotides or amino acids appearing in the sequences, as two sequences with significantly different compositions cannot be closely related. However, it is also true that two unrelated sequences can have very similar compositions. So the next step is to consider all N-words in a set of sequences (an N-word consists of N consecutive letters in a sequence) to calculate sequence dissimilarities . This can show some evolutionary relationships. The problem is that the only straightforward relationship between two words is that they are either identical or different. This can be compensated by considering «imperfectly matched words». The problem is then a combinatorial explosion: as we all know, there are too many ways of being imperfect.
To track the evolutionary information embedded in ambiguous regions of sequence alignments and to alleviate the burden of manual editing, one of us developed a method that allows comparison by considering «local decoding of order N» of sequences (also called «N-local decoding method»), and keeping information of each set of overlapping (step 1) N-words that cover each site in a sequence . The method is recapitulated in a recent work describing a new algorithm that computes the «local decoding of order N» of a sequence (or a set of sequences) with a complexity linear in sequence length, both in time and memory space .
The method was found useful for assessing a manually constructed HIV (Human Immunodeficiency Virus) LTR (long terminal repeat) multiple alignment, which is homology blocks based . «Related sites» were found to correspond to homology blocks between compared sequences, leading us to think that the comparison of the sequences «composition» in «locally decoded letters» could yield a new method for calculating dissimilarities between sequences without an alignment and consequently without taking into account the order of homology blocks in each sequence.
The new method has also been tested against 10 reference alignment-based dissimilarity matrices calculated from 10 multiple alignments of DNA or RNA sequences: it gives alignment-free dissimilarities in very good agreement with the reference alignment-based dissimilarity matrices (better than the correlation obtained when the alignment-free dissimilarity matrix was calculated from the comparison of sequence compositions in words of length N) .
The aims of this paper are: (A) to give a concise description of this new method (see the section Methods) (B) to apply it to a problem of biological interest, namely the classification of HIV/SIV, two of the most variable organisms known so far (see Results and Discussion).
The N-local decoding method on complete genome sequences – tests on HIV/SIV complete sequences
The computational program used to calculate the dissimilarity matrices from which the trees can be constructed has been tested for a wide range of values of N, which is the only user-specified parameter of the local decoding method. When N ranges from 13 to 35, there is no significant change in the tree. Furthermore, the topology of the tree obtained by N-local decoding (N = 13–29) of 46 HIV/SIV complete nucleotide sequences, is identical with the complete genome tree available from HIV/SIV sequence compendium 2000 (compare Figure "Comp2000_13_tree" corresponding to the file "Comp_2000" at  with the published tree at .
The N-local decoding method on short sequences – tests on HIV/SIV gag, pol, env and nef sequences
HIV/SIV exhibit great variety in different parts of their genomes, with env representing the most variable region. All 70 sequences described in the Methods section were used to test different sequence regions. For gag, 66 HIV/SIV sequences (only the gag region is used) and 4 HIV-2 sequences that cover partial gag (sequence length 771–781 nt, in contrast to 1473–1569 nt of the complete gag sequences) were tested. For other genome regions, pol (2993–3360 nt),env (2499–2658 nt) and nef (292–783 nt), only 66 sequences were selected because of unavailability of sequences in these regions from HIV-2 subtype C, D, E, F. The trees of these four regions, based on the N-local decoding calculated sequence distance matrices (see figures «gag18_tree», «pol22_tree», «env15_tree, and «nef13_tree» at ), also agree well with the established HIV/SIV phylogenetic trees in these regions [9–11, 14, 15] with the exception of nef. A few discrepancies exist in nef: in our method, HIV-1 group M sub-subtypes F1 and F2 mix together. Subtype K, depending on the chosen value of N, either is still close to F1/F2 as expected, or is isolated or loosely related to subtype J. SIV-CPZ-ANT is intermediary between HIV-1-M-N/SIV-CPZ-HIV-1-O and HIV-2/SIV-SMM. These discrepancies may simply reflect uneven sequence complexity in different genome regions, or just reflect the differences in treating ambiguous alignment regions: the N-local decoding method keeps all these regions while the traditional alignment-based methods have to delete those alignment regions in order to produce an unbiased tree.
The best orders N tested in these regions are: gag, N = 11 to 23; pol, N = 11 to 30; env, N = 12 to 24; and nef, N = 11 to 20.
The N-local decoding method on sequences that traditional alignment-based methods cannot deal with – tests on non-coding LTR sequences
Forty-three of the 70 sequences (retrieved from the HIV sequence database as described in Methods section) cover the non-coding part of LTR (complete non-coding LTR region or at least the 5' portion of this segment including the polyadenylation signal AATAAA). The length of this part ranges from 211 to 328 nt in the HIV-1/SIV-CPZ subset, and 433 to 508 nt in the HIV-2/SIV-SMM subset. These short non-coding LTR segments contain many duplications/insertions/deletions that make them difficult for traditional alignment-based phylogenic studies .
In our method, bootstrap values strongly support the clustering of HIV-2 (and their subtypes), SIV-SMM, HIV-1-M, HIV-1-N, SIV-CPZ, and HIV-1-O. However, in contrast to Figure 1, the values are low within HIV-1-M. This is not surprising since these sequences are short and very similar to each other. The parameter N tested here ranges from 10 to 21 in order to generate the most appropriate neighbor-joining trees for those 43 sequences. For various subtypes, however, this N sometimes falls into different ranges. For example, N = 9–14, 27–43 and even further make HIV-1 subtype C cluster clearly defined; N = 10–60 and beyond are needed for HIV-1 subtype G cluster; N = 8–60 and above are necessary to cluster two J sequences of HIV-1-group M. Finally, N = 9–17 are needed to clearly cluster HIV-1-M/B and D subtype, and N = 19–25 are necessary to distinguish HIV-1 subtype B and D.
In short, our HIV/SIV subtyping results confirmed the good performance of the N-local-decoding method, regardless the complexity of genome regions. The N values listed above not only are useful in making an N-parameter reference for future N-local-decoding method applications, but also imply different evolutionary pressures in various HIV/SIV clades.
Similarity blocks displayed by the N-local decoding method – an example of the HIV non-coding LTR sequences
In our previous study of HIV LTR , a sequence alignment had to be manually constructed in order to take into account the frequent duplications/insertions/deletions events. This strategy is similar to the N-local-decoding procedure described in this paper.
Here we use the HIV non-coding-LTR NFK B binding site (GGGACTTTCCA/G) (see ) and its flanking sequences obtained from 43 sequences (the same sequences used in non-coding HIV LTR Results section just above) as an example to show the relationship between the similarity blocks and the N-classes.
Similarity blocks in the NFK B binding sites reveal different duplication events of this site in various HIV clades. Each HIV-1 group M subtype has two NFK B site copies, with the exception of subtype C which exhibits an extra ≥ 10-letter long fragment (GGG(g)CgTTCCA) with 9 letters (here upper case letters) matching this NFK B site. Both HIV-1 group N sequences have two NFK B binding sites and one or two incomplete copy of such site (GGGACTTT), and these N-group sequences are more similar to SIV-CPZ and HIV-1-M than to other HIV clades. All HIV-1 group O sequences display two copies of a sequence fragment of 30 letters long (these two copies are written in two lines to show their similarity in Figure 5). Each fragment includes a NFK B binding site, a segment G2A3C2A4 similar to that in HIV-2 subtype B, and a segment C3T#G#C5 similar to SIV-CPZ-GAB. The differences in the similarity blocks in NFK B binding sites in HIV clades may indicate an important role the NFK B binding site played in different independent introductions of SIV-CPZ from chimpanzees into the human populations to establish HIV-1 M, N, O groups.
Similarity blocks in NFK B site flanking sequences may also provide helpful information in tracking evolutionary relationships and distances between HIV clades. One example is the pattern G#C5A1 before the NFK B site, that only exists in HIV-2 and SIV-SMM sequences. Possibly this well-preserved G#C5A1 participated actively in the cross-transmission from SMM to human to establish the HIV-2. The other example is A2 A# G# G# G# pattern (in blue) at the 3' end of HIV-2 subtype A flanking sequences. This pattern distinguishes HIV-2 subtype A from other HIV clades. This, again, may simply reflect different transmission influences in varied HIV clades.
The current HIV sequence analyses usually involve obtaining a gap-stripped multiple alignment to construct an unbiased phylogenetic tree. The alignment usually is first generated by HMMER ([20, 21] and/or other alignment softwares, followed by a manual editing . After deleting the ambiguously aligned positions from the alignment, the final alignment leaves about half the sequence length . This is a time-consuming procedure, and, most importantly, the alignment quite often underestimates the sequence variabilities, especially those embedded in the ambiguously aligned positions.
In this paper, we introduced a tree building method without a sequence alignment requirement. This N-local-decoding method calculates sequence dissimilarity matrices, based on re-writing, and re-classifies input sequences. Our HIV/SIV subtyping results showed that the classifications produced by this method agree very well with those obtained by a combination of standard methods. Thus comparing biological sequences without alignments appears to be an alternative to better explore sequence relationships.
However, there exist some discrepancies between our N-local-decoding-method-calculated trees and those obtained from standard methods. The differences may simply suggest that the N parameter used in N-local-decoding-method needs to be better defined, or they may be a consequence of the fact that we include ambiguous regions that are often ignored by traditional methods.
The N-local-decoding-method is particularly useful in the analysis of sequence variety and in tracking the sequence evolutionary events when a good sequence alignment is not possible. Our N-local-decoding-method is meaningful from the evolutionary point of view. Its success in sequence subtyping relies on capturing (im)perfect repeats or conserved regions in sequences (similarity blocks that are either closely or remotely related, and the latter one is often undetected by traditional methods due to removal of ambiguous alignment regions). The similarity blocks include internal repeats in one sequence or conserved regions among sequences, and these blocks are not necessarily to appear in the same order in the original sequences. In our method, the inversion could be detected by including the reverse complementary sequences in the sequence set.
This method is also practically applicable in terms of computing time and convenience to use. All the calculations in this paper have been done within a few seconds on a regular PC. The quality of the algorithm  is responsible for this speed. This algorithm has a complexity linear in the total length of the set.
The N value, the only parameter used in this method, could be set empirically according to the N values listed in the Results section.
Our method thus provides an alternative way of constructing sequence trees. It is helpful in tracking sequence information embedded in ambiguous alignment regions. In addition, the possibility of comparing sequences of varied lengths also suggests its direct use in detecting sequence recombinants. Finally, the similarity blocks found by this method could also be used as the anchor points for those similarity-block-based alignment programs to refine the quality of the alignment.
It generates sequence dissimilarity matrices without requiring sequence alignments.
It has only one user-specified parameter (N: the order of the site neighborhoods).
It is fast.
Thus the N-local-decoding method provides a promising approach to the rapid construction of trees without a time-consuming step of aligning sequences.
HIV-1 group M (subtypes A-D, F-H, J, K; subtype A is further split into A1 and A2, and subtype F is divided into F1 and F2),
HIV-1 group N,
HIV-1 group O,
HIV-2 (subtypes A, B, G),
SIV-CPZ, and SIV-SMM
Four incomplete genome sequences are obtained from HIV-2 subtypes C, D, E, F, with lengths varying from 771 to 781 nucleotides, that cover about half of gag region. Thus those well-characterized 70 sequences, representing different HIV/SIV groups, sub-groups, subtypes, and sub-subtypes, are good samples of the genetic variation of the HIV/SIV world. These sequences can be retrieved from the Los Alamos HIV sequence database . Their accession numbers are given in Figure 1. The sequence files are also available from .
The sequence data described above were analysed using the method introduced in [7, 8], which we call «local decoding of order N» of a sequence. Basically, the aim of the method is to find local similarities between (and within) the sequences of an input set. The input consists of a set of non-aligned sequences or a single sequence. In the input, any site of a sequence is occupied by a letter (typically a base or an amino acid). In the output, every site is occupied by an identifier that carries information about the letter and its neighborhood. The details of this method are described below.
A sequence S consists of letters S1...Si... with i running from 1 to |S|, the length of the sequence. Let N be a chosen integer parameter, which determines the size of neighborhoods to be considered. Consider the set of all overlapping words of length N (step 1) in the sequence S. Given a site (i.e. an integer i with 1 ≤ i ≤ |S|), consider the set of words that cover this site. Two sites are said to be «directly related» if they appear at the same position in two occurrences of the same word of length N (see example below). It is easy to see that two directly related sites have to be occupied by the same letter. Note that two sites may not be directly related but each of them may be directly related to a third site. More generally, two sites are said to be «related» (not necessarily directly) if they are linked by a chain of direct relations. In mathematical terminology, we consider the transitive closure of the direct relation. One can show that any two (even not directly) related sites are also occupied by the same letter. Finally, one obtains a decomposition of the set of all sites into disjoint classes (so called «N-classes»), each class being defined to be a maximal subset of sites that are pairwise related. This decomposition is always finer than (or the same as) the decomposition of sites into classes of sites occupied by a same letter. In this sense, local decodings of sequences are more informative. This method can be extended to a set of sequences. A site is then labelled by a pair (sequence name, position).
Using this method, we have re-written a set of HIV sequences in a larger alphabet. A «letter» of the new alphabet at a given site of the sequences, can be for instance «A215» where «A» is the letter in the input sequence at this site, and where A215 is the N-class identifier used to distinguish «related sites» (i.e. all sites carrying the symbol «A215») from non-related sites. This re-writing depends only on the choice of the integer parameter N. In addition, the re-writing of a set of sequences depends also on this sequence set itself, and could be changed if sequences are added or removed. In other words, «relatedness» is not an intrinsic property of sites.
Figure 6b shows all classes of sites containing at least two members, and Figure 6c shows the output of the program based on the algorithm described in . Each site in the original sequences is replaced by the identifier of the N-class to which the site belongs. As mentioned above, the identifier consists of the letter occupying the site in the original sequence, followed by a number.
Similarity and dissimilarity between sequences
Among many possibilities of defining a similarity between sequences, we choose the one described in . In the program output (Fig. 6c), every site in the compared sequences gets occupied by an identifier. We need an extra notation here and denote by |w|x the number of occurrences of an identifier x in any sequence (w). Let us first introduce a similarity score between two sequences u and v. Each identifier x could occur in any alignment (alignments are not used by our method) of u and v in at most min(|u|x,|v|x), that is the smallest number of occurrences of x in the two sequences u and v. Adding up this number over all identifiers gives an upper bound of the maximum number of possible matches between u and v (Fig. 6d). Finally, we normalize the obtained similarity by dividing it by the length of the shorter sequence (this makes it possible to subtype short sequence parts together with complete sequences, see Results first section). It is then straightforward to obtain a dissimilarity score, that is 1 minus the normalized similarity (Fig. 6e). Figure 6d–e outlines the method for calculating the dissimilarity matrix between the three compared sequences re-written in Figure 6c, by using the N-local decoding method. For instance, when comparing seq1 and seq2, the sum of all the min(|u|x,|v|x) equals 5, so that the corresponding dissimilarity is 1 - (5/20) = 0.75 (Fig. 6e). Here, the identifiers that correspond to classes containing only one site have been omitted, since they cannot contribute to the similarity (Fig. 6c).
Computational programs used in this study
A program implementing the N-local decoding method was used for constructing HIV/SIV trees. It reads a set of FASTA input sequences, and generates a tree based on the dissimilarity matrix described above. This program is available from . A more recent program  written in C, and the supplementary material are available upon request from GD (address in the title page).
Other programs used in this paper are: CLUSTAL-W (version 1.8 June 1999) , DIALIGN-2.2.1  and PHYLIP . Some of these programs have been used to compute the alignment-based dissimilarity matrices from which the trees have been constructed for Figure 3 (CLUSTAL-W) and for Figure 4 (DIALIGN-2.2.1).
Computing bootstrap values over trees
To assess the reliability of the trees computed from the dissimilarity matrices, we use a bootstrapping procedure similar to the one proposed in  which proceeds by resampling the columns of an alignment. Though we do not deal with an alignment but just a set of local decoded sequences, a very similar procedure can be applied here. Practically, from the local decoded set of sequences, we resample a given number of bootstrap replicates. These replicates are the sets in which each sequence of the initial local decoded set is replaced by a sequence of the same length. This sequence is generated by concatenating randomly selected sites in the sequence of the initial set (some positions can be selected more than once, others never selected). For all the resampled sets of sequences we compute dissimilarities and associated neighbor-joining trees. Those trees are finally combined using the «consense» program of PHYLIP  to give a final tree and related bootstrap values.
We thank Alain Hénaut, Bernard Prum and Jean-Loup Risler for helpful discussions. In particular, we owe the initial idea of using the N-local-decoding method for a classification without alignment to Alain Hénaut. This work was supported by the «programme inter-EPST Bio-informatique 2001».
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