Difference between revisions of "CNVer"
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{{Bioinformatics application | {{Bioinformatics application | ||
|sw summary=CNVer is a method for CNV detection that supplements the depth-of-coverage with paired-end mapping information, where matepairs mapping discordantly to the reference serve to indicate the presence of variation. CNVer combines this information within a unified computational framework called the donor graph, allowing it to better mitigate the sequencing biases that cause uneven local coverage. CNVer can also reconstruct the absolute copy counts of segments of the donor genome, and work with low coverage datasets. | |sw summary=CNVer is a method for CNV detection that supplements the depth-of-coverage with paired-end mapping information, where matepairs mapping discordantly to the reference serve to indicate the presence of variation. CNVer combines this information within a unified computational framework called the donor graph, allowing it to better mitigate the sequencing biases that cause uneven local coverage. CNVer can also reconstruct the absolute copy counts of segments of the donor genome, and work with low coverage datasets. | ||
| − | |bio domain=Structural | + | |bio domain=Structural variation, Copy number estimation |
| + | |created at=University of Toronto, | ||
|maintained=Yes | |maintained=Yes | ||
| + | |input format=FASTA, | ||
| + | |language=Perl, C++, | ||
}} | }} | ||
== Description == | == Description == | ||
Latest revision as of 03:48, 13 July 2012
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Application data |
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| Biological application domain(s) | Structural variation, Copy number estimation |
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| Created at | University of Toronto |
| Maintained? | Yes |
| Input format(s) | FASTA |
| Programming language(s) | Perl, C++ |
Summary: CNVer is a method for CNV detection that supplements the depth-of-coverage with paired-end mapping information, where matepairs mapping discordantly to the reference serve to indicate the presence of variation. CNVer combines this information within a unified computational framework called the donor graph, allowing it to better mitigate the sequencing biases that cause uneven local coverage. CNVer can also reconstruct the absolute copy counts of segments of the donor genome, and work with low coverage datasets.
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