Difference between revisions of "Contra"
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{{Bioinformatics application | {{Bioinformatics application | ||
|sw 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. | |sw 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. | ||
| − | |bio domain= | + | |bio domain=Sequencing, Cancer biology, Genomics, Copy number estimation, |
|bio tech=Illumina | |bio tech=Illumina | ||
|created by=Jason Li | |created by=Jason Li | ||
| Line 11: | Line 11: | ||
|language=Python, R | |language=Python, R | ||
|library=BEDtools, SAMtools | |library=BEDtools, SAMtools | ||
| − | |licence= | + | |licence=GPLv3, |
|os=Linux 64, Linux, | |os=Linux 64, Linux, | ||
}} | }} | ||
Latest revision as of 00:07, 20 December 2015
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Application data |
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| Created by | Jason Li |
|---|---|
| Biological application domain(s) | Sequencing, Cancer biology, Genomics, Copy number estimation |
| 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 | GPLv3 |
| 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.
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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
none specified
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