FREEC
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Application data |
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| Biological application domain(s) | Copy number estimation |
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| Maintained? | Maybe |
| Input format(s) | SAM, BAM, Eland, BED, SOAP, psl, Bowtie, Arachne binary |
| Operating system(s) | Linux, Linux 64, Windows |
Summary: A tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies.
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Description
The tool deals with two frequent problems in analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC-content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-content normalization provides smooth profiles which can be further segmented and analyzed in order to predict copy number alterations.
There is a new version of the tool - Control-FREEC: http://bioinfo-out.curie.fr/projects/freec/
Control-FREEC which enables automatic calculation of copy number and allelic content profiles from next generation sequencing data, and consequently predicts regions of genomic alteration such as gains, losses, and loss of heterozygosity. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze over-diploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks.
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References
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