CoNIFER

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Description

conifer website  

CoNIFER uses exome sequencing data to find copy number variants (CNVs) and genotype the copy-number of duplicated genes. As exome capture reactions are subject to strong and systematic capture biases between sample batches, we implemented singular value decomposition (SVD) to eliminate these biases in exome data. CoNIFER offers the ability to mix exome sequence from multiple experimental runs by eliminating batch biases. Together with a short read aligner such as mrsFAST which can align reads to multiple locations, CoNIFER can robustly detect rare CNVs and estimate the copy number of duplicated genes up to ~8 copies with current exome capture kits.

Required Modules

Serial

  • conifer

System Variables

  • HPC_{{#uppercase:conifer}}_DIR - installation directory

Additional Information

CoNIFER is a command-line python program (conifer.py). There are several sub-commands, each with a set of command line options. In general, the CoNIFER pipeline consists of the following steps:

1. Calculate RPKM values for all samples python conifer.py rpkm [...]

2. Analyze all RPKM values for all samples and create SVD-ZRPKM values. This steps create a single unified conifer data file which contains all data, probes and samples for downstream analysis. python conifer.py analyze [...]

3. View, make calls or export SVD-ZRPKM values for segmentation using other/existing algorithms python conifer.py export [...] python conifer.py call [...] python conifer.py plot [...] python conifer.py plotcalls [...]

Not all steps are required. For example, Step 1 is only needed if you wish to transform a set of .bam alignment files into RPKM files. If you have RPKM files created from another source, you can omit step one (bamToRPKM.py).

Help and available command line options for any of these steps can be listed using the --help or -h options, e.g.: python conifer.py analyze --help



Citation

If you publish research that uses conifer you have to cite it as follows:

O'Roak, BJ et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature (2012).doi:10.1038/nature10989

Krumm N, Sudmant PH, Ko A, O'Roak BJ, Malig M, Coe BP, NHLBI Exome Sequencing Project, Quinlan AR, Nickerson DA and Eichler EE, 2012. Copy number variation detection and genotyping from exome sequence data. Genome Research, doi:10.1101/gr.138115.112