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      TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations

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          Abstract

          We present TranslatorX, a web server designed to align protein-coding nucleotide sequences based on their corresponding amino acid translations. Many comparisons between biological sequences (nucleic acids and proteins) involve the construction of multiple alignments. Alignments represent a statement regarding the homology between individual nucleotides or amino acids within homologous genes. As protein-coding DNA sequences evolve as triplets of nucleotides (codons) and it is known that sequence similarity degrades more rapidly at the DNA than at the amino acid level, alignments are generally more accurate when based on amino acids than on their corresponding nucleotides. TranslatorX novelties include: (i) use of all documented genetic codes and the possibility of assigning different genetic codes for each sequence; (ii) a battery of different multiple alignment programs; (iii) translation of ambiguous codons when possible; (iv) an innovative criterion to clean nucleotide alignments with GBlocks based on protein information; and (v) a rich output, including Jalview-powered graphical visualization of the alignments, codon-based alignments coloured according to the corresponding amino acids, measures of compositional bias and first, second and third codon position specific alignments. The TranslatorX server is freely available at http://translatorx.co.uk.

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          Most cited references 13

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          An algorithm for progressive multiple alignment of sequences with insertions.

          Dynamic programming algorithms guarantee to find the optimal alignment between two sequences. For more than a few sequences, exact algorithms become computationally impractical, and progressive algorithms iterating pairwise alignments are widely used. These heuristic methods have a serious drawback because pairwise algorithms do not differentiate insertions from deletions and end up penalizing single insertion events multiple times. Such an unrealistically high penalty for insertions typically results in overmatching of sequences and an underestimation of the number of insertion events. We describe a modification of the traditional alignment algorithm that can distinguish insertion from deletion and avoid repeated penalization of insertions and illustrate this method with a pair hidden Markov model that uses an evolutionary scoring function. In comparison with a traditional progressive alignment method, our algorithm infers a greater number of insertion events and creates gaps that are phylogenetically consistent but spatially less concentrated. Our results suggest that some insertion/deletion "hot spots" may actually be artifacts of traditional alignment algorithms.
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            RevTrans: Multiple alignment of coding DNA from aligned amino acid sequences.

            The simple fact that proteins are built from 20 amino acids while DNA only contains four different bases, means that the 'signal-to-noise ratio' in protein sequence alignments is much better than in alignments of DNA. Besides this information-theoretical advantage, protein alignments also benefit from the information that is implicit in empirical substitution matrices such as BLOSUM-62. Taken together with the generally higher rate of synonymous mutations over non-synonymous ones, this means that the phylogenetic signal disappears much more rapidly from DNA sequences than from the encoded proteins. It is therefore preferable to align coding DNA at the amino acid level and it is for this purpose we have constructed the program RevTrans. RevTrans constructs a multiple DNA alignment by: (i) translating the DNA; (ii) aligning the resulting peptide sequences; and (iii) building a multiple DNA alignment by 'reverse translation' of the aligned protein sequences. In the resulting DNA alignment, gaps occur in groups of three corresponding to entire codons, and analogous codon positions are therefore always lined up. These features are useful when constructing multiple DNA alignments for phylogenetic analysis. RevTrans also accepts user-provided protein alignments for greater control of the alignment process. The RevTrans web server is freely available at http://www.cbs.dtu.dk/services/RevTrans/.
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              Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites.

               Q. Z. Yang (1993)
              Felsenstein's maximum-likelihood approach for inferring phylogeny from DNA sequences assumes that the rate of nucleotide substitution is constant over different nucleotide sites. This assumption is sometimes unrealistic, as has been revealed by analysis of real sequence data. In the present paper Felsenstein's method is extended to the case where substitution rates over sites are described by the gamma distribution. A numerical example is presented to show that the method fits the data better than do previous models.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                1 July 2010
                30 April 2010
                30 April 2010
                : 38
                : Web Server issue
                : W7-W13
                Affiliations
                1Departamento de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, José Gutiérrez Abascal, 2, 28006 Madrid, Spain and 2Department of Genetics, Evolution and Environment, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK
                Author notes
                *To whom correspondence should be addressed. Tel: +44-(0)20-7679-2554; Fax: +44-(0)20-7679-7096; Email: m.telford@ 123456ucl.ac.uk
                Article
                gkq291
                10.1093/nar/gkq291
                2896173
                20435676
                © The Author(s) 2010. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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