Difference between revisions of "Contra"
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− | CONTRA implements a method for CNV detection for targeted resequencing data, including whole-exome capture data. It calls copy number gains and losses for each target region based on normalized depth of coverage. Key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. | + | CONTRA implements a method for CNV detection for targeted resequencing data, including whole-exome capture data. It calls copy number gains and losses for each target region based on normalized depth of coverage. Key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. |
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+ | See publication: | ||
+ | Li, et al. Bioinformatics (2012) 28 (10): 1307-1313. [http://bioinformatics.oxfordjournals.org/content/28/10/1307.full FULL TEXT HERE] | ||
Revision as of 06:49, 30 May 2012
Application data |
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Created by | Jason Li |
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Biological application domain(s) | Next Generation Sequencing, Cancer biology, Genomics |
Principal bioinformatics method(s) | Copy number estimation, Copy number analysis, CNV |
Technology | Illumina |
Created at | Peter MacCallum Cancer Centre, East Melbourne VIC 3002, Australia |
Maintained? | Yes |
Input format(s) | BAM, BED |
Output format(s) | VCF, Tab-delimited |
Software features | Copy number analysis, baseline (pseudo-control) creation |
Programming language(s) | Python, R |
Software libraries | BEDtools, SAMtools |
Licence | GPL v3 |
Operating system(s) | Linux 64, Linux |
Summary: Copy number analysis for exome-sequencing / targeted-resequencing. Two methods of analysis available: Case vs Control, or Case vs Baseline. Function available for creating a baseline from multiple samples. INPUT=BAM files.
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Description
CONTRA implements a method for CNV detection for targeted resequencing data, including whole-exome capture data. It calls copy number gains and losses for each target region based on normalized depth of coverage. Key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation.
See publication:
Li, et al. Bioinformatics (2012) 28 (10): 1307-1313. FULL TEXT HERE
Links
References
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