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Main.HomePage History
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May 06, 2008, at 07:03 AM
by Marc Girondot -
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MacOS version - Universal binaries\\
May 06, 2008, at 07:01 AM
by Marc Girondot -
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http://www.bioinformatics.org/unidplot/public_ftp/UniDPlot_Win.zip
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http://www.bioinformatics.org/unidplot/software/UniDPlot_Win.zip
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http://www.bioinformatics.org/unidplot/public_ftp/UniDPlot_Mac.zip
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http://www.bioinformatics.org/unidplot/software/UniDPlot_Mac.zip
May 06, 2008, at 06:58 AM
by Marc Girondot -
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http://www.bioinformatics.org/unidplot/../../public_ftp/image/unidplot.png
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http://www.bioinformatics.org/unidplot/image/unidplot.png
May 06, 2008, at 06:57 AM
by Marc Girondot -
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http://www.bioinformatics.org/unidplot/../../public_ftp/image/unidplot.png
May 06, 2008, at 06:54 AM
by Marc Girondot -
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http://www.bioinformatics.org/unidplot/public_ftp/image/unidplot.png
May 06, 2008, at 06:52 AM
by Marc Girondot -
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http://www.bioinformatics.org/unidplot/public_ftp/UniDPlot.png
May 06, 2008, at 06:50 AM
by Marc Girondot -
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http://www.bioinformatics.org/image/UniDPlot.png
May 06, 2008, at 06:49 AM
by Marc Girondot -
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http://www.bioinformatics.org/unidplot/public_html/image/UniDPlot.png
May 06, 2008, at 06:49 AM
by Marc Girondot -
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http://www.bioinformatics.org/unidplot/public_html/image/unidplot.png
Current version 1.2.2
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http://www.bioinformatics.org/unidplot/public_html/UniDPlot_Win.zip
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http://www.bioinformatics.org/unidplot/public_ftp/UniDPlot_Win.zip
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http://www.bioinformatics.org/unidplot/public_html/UniDPlot_Mac.zip
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http://www.bioinformatics.org/unidplot/public_ftp/UniDPlot_Mac.zip
May 06, 2008, at 06:46 AM
by Marc Girondot -
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1 Laboratoire d’Écologie, Systématique et Évolution, UMR 8079 Centre National de la Recherche Scientifique, Université Paris Sud et ENGREF, 91405 Orsay cedex 05, France
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1 Laboratoire d’Écologie, Systématique et Évolution, UMR 8079 Centre National de la Recherche Scientifique, Université Paris Sud et ENGREF, 91405 Orsay cedex 05, France\\
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Search for DNA sequence similarity is a crucial step in many evolutionary analyses and several bioinformatic tools are available to fulfill this task. Basic Local Alignment Search Tool (BLAST) is the most commonly and highly efficient algorithm used. However, it often fails in identifying sequences showing very weak similarity. An alternative method is to use Dot Plot, but such a graphical method is not suitable for the analysis of large sequences (e.g. hundreds of kilobases) as this is now more often required in the context of genome sequencing programs.
to:
Search for DNA sequence similarity is a crucial step in many evolutionary analyses and several bioinformatic tools are available to fulfill this task. Basic Local Alignment Search Tool (BLAST) is the most commonly and highly efficient algorithm used. However, it often fails in identifying sequences showing very weak similarity. An alternative method is to use Dot Plot, but such a graphical method is not suitable for the analysis of large sequences (e.g. hundreds of kilobases) as this is now more often required in the context of genome sequencing programs.\\
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May 06, 2008, at 06:46 AM
by Marc Girondot -
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May 06, 2008, at 06:45 AM
by Marc Girondot -
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Current version 1.2.2
Windows version
http://www.bioinformatics.org/unidplot/public_html/UniDPlot_Win.zip
MacOS version
http://www.bioinformatics.org/unidplot/public_html/UniDPlot_Mac.zip
May 06, 2008, at 06:33 AM
by Marc Girondot -
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Marc Girondot 1,2 & Jean-Yves Sire 3
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Marc Girondot 1 & Jean-Yves Sire 2
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2 Département de Systématique et Evolution, Muséum National d’Histoire Naturelle de Paris, 25 rue Cuvier, 75005 Paris, France
3 Université Pierre & Marie Curie-Paris 6, UMR 7138 "Systématique, Adaptation, Evolution", 7 quai St-Bernard, 75005 Paris, France
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2 Université Pierre & Marie Curie-Paris 6, UMR 7138 "Systématique, Adaptation, Evolution", 7 quai St-Bernard, 75005 Paris, France
May 06, 2008, at 06:32 AM
by Marc Girondot -
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Background
Background. Search for DNA sequence similarity is a crucial step in many evolutionary analyses and several bioinformatic tools are available to fulfill this task. Basic Local Alignment Search Tool (BLAST) is the most commonly and highly efficient algorithm used. However, it often fails in identifying sequences showing very weak similarity. An alternative method is to use Dot Plot, but such a graphical method is not suitable for the analysis of large sequences (e.g. hundreds of kilobases) as this is now more often required in the context of genome sequencing programs.
Results
As an alternative to the classical Dot Plot method, we designed UniDPlot, which permits to search for weak similarity either between two large sequences (e.g., genome regions, ...) or between one large sequence and a short one (e.g., exons, ...). UniDPlot methodology contracts the output of the Dot Plot similarity matrix along the length of the largest sequence, while defining statistical limits of significance using a bootstrap procedure. To illustrate the efficiency of this method, we used UniDPlot to search for the fate of the gene that encodes the major enamel protein, amelogenin, in chicken. Although we show that amelogenin was invalidated through a pseudogeneization process, we recovered the entire sequence in the chicken genome.
Conclusions
Using UniDPlot, we have identified a pseudogene, which was not detected by classical methods. UniDPlot can be used to search for missing genes, or motifs of various sizes in different genomic contexts.
to:
Search for DNA sequence similarity is a crucial step in many evolutionary analyses and several bioinformatic tools are available to fulfill this task. Basic Local Alignment Search Tool (BLAST) is the most commonly and highly efficient algorithm used. However, it often fails in identifying sequences showing very weak similarity. An alternative method is to use Dot Plot, but such a graphical method is not suitable for the analysis of large sequences (e.g. hundreds of kilobases) as this is now more often required in the context of genome sequencing programs.
As an alternative to the classical Dot Plot method, we designed UniDPlot, which permits to search for weak similarity either between two large sequences (e.g., genome regions, ...) or between one large sequence and a short one (e.g., exons, ...).
May 06, 2008, at 06:31 AM
by Marc Girondot - UniDPlot : A software to detect weak similarities between two DNA sequences
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UniDPlot : A software to detect weak similarities between two DNA sequences
Marc Girondot 1,2 & Jean-Yves Sire 3
1 Laboratoire d’Écologie, Systématique et Évolution, UMR 8079 Centre National de la Recherche Scientifique, Université Paris Sud et ENGREF, 91405 Orsay cedex 05, France
2 Département de Systématique et Evolution, Muséum National d’Histoire Naturelle de Paris, 25 rue Cuvier, 75005 Paris, France
3 Université Pierre & Marie Curie-Paris 6, UMR 7138 "Systématique, Adaptation, Evolution", 7 quai St-Bernard, 75005 Paris, France
Background
Background. Search for DNA sequence similarity is a crucial step in many evolutionary analyses and several bioinformatic tools are available to fulfill this task. Basic Local Alignment Search Tool (BLAST) is the most commonly and highly efficient algorithm used. However, it often fails in identifying sequences showing very weak similarity. An alternative method is to use Dot Plot, but such a graphical method is not suitable for the analysis of large sequences (e.g. hundreds of kilobases) as this is now more often required in the context of genome sequencing programs.
Results
As an alternative to the classical Dot Plot method, we designed UniDPlot, which permits to search for weak similarity either between two large sequences (e.g., genome regions, ...) or between one large sequence and a short one (e.g., exons, ...). UniDPlot methodology contracts the output of the Dot Plot similarity matrix along the length of the largest sequence, while defining statistical limits of significance using a bootstrap procedure. To illustrate the efficiency of this method, we used UniDPlot to search for the fate of the gene that encodes the major enamel protein, amelogenin, in chicken. Although we show that amelogenin was invalidated through a pseudogeneization process, we recovered the entire sequence in the chicken genome.
Conclusions
Using UniDPlot, we have identified a pseudogene, which was not detected by classical methods. UniDPlot can be used to search for missing genes, or motifs of various sizes in different genomic contexts.
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