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      Comparing sequences without using alignments: application to HIV/SIV subtyping

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          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.

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          Most cited references 15

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          Profile hidden Markov models.

           Sean R. Eddy,  S Eddy (1997)
          The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
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            DIALIGN 2: improvement of the segment-to-segment approach to multiple sequence alignment.

            The performance and time complexity of an improved version of the segment-to-segment approach to multiple sequence alignment is discussed. In this approach, alignments are composed from gap-free segment pairs, and the score of an alignment is defined as the sum of so-called weights of these segment pairs. A modification of the weight function used in the original version of the alignment program DIALIGN has two important advantages: it can be applied to both globally and locally related sequence sets, and the running time of the program is considerably improved. The time complexity of the algorithm is discussed theoretically, and the program running time is reported for various test examples. The program is available on-line at the Bielefeld University Bioinformatics Server (BiBiServ) http://bibiserv.TechFak.Uni-Bielefeld.DE/dial ign/
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              PHYLIP (Phylogeny Inference Package) version 3.6


                Author and article information

                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                2 January 2007
                : 8
                : 1
                [1 ]Institut Mathématique de Luminy, UMR 6206, Campus de Luminy, Case 907, 13288 Marseille Cedex 9, France
                [2 ]Equipe Bioinfo, LIFL, USTL, cité scientifique, Batiment M3, 59655 Villeneuve d'Ascq, France
                [3 ]Department of Bioinformatics, Institute of Microbiology and Genetics, University of Goettingen. Goettingen 37077, Germany
                [4 ]Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
                [5 ]Laboratoire Statistique et Génome, UMR 8071, Tour Evry 2, 523 Place des Terrasses, 91034 Evry, France
                Copyright © 2007 Didier et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Research Article

                Bioinformatics & Computational biology


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