Detecting microsatellites within genomes: significant variation among algorithms
<p>Abstract</p> <p>Background</p> <p>Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algor...
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| Format: | Article |
| Language: | English |
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BMC
2007-04-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/8/125 |
| _version_ | 1818050429712334848 |
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| author | Rivals Eric Leclercq Sébastien Jarne Philippe |
| author_facet | Rivals Eric Leclercq Sébastien Jarne Philippe |
| author_sort | Rivals Eric |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker).</p> <p>Results</p> <p>Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (<it>Saccharomyces cerevisiae</it>, <it>Neurospora crassa </it>and <it>Drosophila melanogaster</it>) spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp), regardless of motif.</p> <p>Conclusion</p> <p>Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions.</p> |
| first_indexed | 2024-12-10T10:53:20Z |
| format | Article |
| id | doaj.art-18a589f67a6a49b09f3255c957cac5cf |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T10:53:20Z |
| publishDate | 2007-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-18a589f67a6a49b09f3255c957cac5cf2022-12-22T01:51:56ZengBMCBMC Bioinformatics1471-21052007-04-018112510.1186/1471-2105-8-125Detecting microsatellites within genomes: significant variation among algorithmsRivals EricLeclercq SébastienJarne Philippe<p>Abstract</p> <p>Background</p> <p>Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker).</p> <p>Results</p> <p>Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (<it>Saccharomyces cerevisiae</it>, <it>Neurospora crassa </it>and <it>Drosophila melanogaster</it>) spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp), regardless of motif.</p> <p>Conclusion</p> <p>Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions.</p>http://www.biomedcentral.com/1471-2105/8/125 |
| spellingShingle | Rivals Eric Leclercq Sébastien Jarne Philippe Detecting microsatellites within genomes: significant variation among algorithms BMC Bioinformatics |
| title | Detecting microsatellites within genomes: significant variation among algorithms |
| title_full | Detecting microsatellites within genomes: significant variation among algorithms |
| title_fullStr | Detecting microsatellites within genomes: significant variation among algorithms |
| title_full_unstemmed | Detecting microsatellites within genomes: significant variation among algorithms |
| title_short | Detecting microsatellites within genomes: significant variation among algorithms |
| title_sort | detecting microsatellites within genomes significant variation among algorithms |
| url | http://www.biomedcentral.com/1471-2105/8/125 |
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