Control-FREEC: a tool for assessing copy number and allelic content using NGS data

[dmacmillan]

Thank you for the reply, that is helpful to know.

Another question,
How would one go about getting or creating a text file with SNPs such as the one you provide on the website? So as to use the BAF parameter.

[valeu]

Hi!

How does it use the normal sample if provided?

If you do not want to predict allelic status (the [BAF] group of parameters is empty), then the normal sample will be used instead of GC-content to normalize the read count in the tumor sample.
If you want to calculate BAF and allelic status, then the normalization is done using GC-content but CNVs will be annotated as somatic or germline using information from the normal sample.

If one provides a normal sample as well as chromosome fasta files, does it account for gc-content bias using the normal sample as well as the fasta files? Or just one of them.

No, if you only look for CNVs. However, it accounts for both of them if you look for allelic status.
In the first case, you can force GC-content normalization using option "forceGCcontentNormalization" (see http://bioinfo-out.curie.fr/projects...al.html#CONFIG)

Does Control-FREEC get its bin size from genomic coordinates, or does it use read density?

It uses genomic coordinates.
BTW, if you are not sure about a good value for window size use option "coefficientOfVariation" to evaluate it.

[dmacmillan]

I have a couple questions about Control-FREEC,

1. How does it use the normal sample if provided?
2. If one provides a normal sample as well as chromosome fasta files, does it account for gc-content bias using the normal sample as well as the fasta files? Or just one of them.
3. Does Control-FREEC get its bin size from genomic coordinates, or does it use read density?

[valeu]

Control-FREEC 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 (LOH).

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.

Publications:

Boeva V, Zinovyev A, Bleakley K, Vert JP, Janoueix-Lerosey I, Delattre O, Barillot E. (2011) Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics 2011; 27(2):268-9. PMID: 21081509.

Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G, Janoueix-Lerosey I, Delattre O, Barillot E. (2011) Control-FREEC: a tool for assessing copy number and allelic content using next generation sequencing data. Bioinformatics. 2011 Dec 6. [Epub ahead of print] PMID: 22155870.

Input for detection of copy number alterations (CNAs):

Aligned single-end, paired-end or mate-pair data in SAM, BAM, SAMtools pileup, Eland, BED, SOAP, arachne, psl (BLAT) and Bowtie formats. Control-FREEC accepts .GZ files.

Input for CNA+LOH detection:

Aligned reads in SAMtools pileup format. The file can be GZipped.

Output:

Regions of gains, lossed and LOH, copy number and BAF profiles.

Availability:

http://bioinfo.curie.fr/projects/freec/