Copy number variation in cancer WGS

[lethalfang]

Hi guys,

I am looking at some whole genome sequencing (WGS) for tumor-normal pairs, and I want to find somatic copy number alteration in the tumors.
What tools do you guys recommend for these?

I have read about a few, e.g., BIC-Seq, OncoSNP-SEQ, CREST, etc., but have no experience. Any recommendations?

Thanks in advance.

[TiborNagy]

Control-FreeC is a good choice.
And an interesting article:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3875755/

[lethalfang]

Thanks. Yeah I've read over that article just recently, and I like to see what are people's experiences with different software.

Control-FREEC seems pretty good. What's your experience with it? I also want to try out BIC-Seq, OncoSNP-SEQ, and CNAnorm. I want to see how they perform. Does anyone have any experience with those?

[TiborNagy]

Control-FreeC is easy to use if you are working with human or mouse model. If you working with non model-organisms it is a bit tricky, because you need to generate the mappability files. Read carefully the documentation because there is a lots of settings. But the we were happy with the results.

[lethalfang]

Control-FreeC is easy to use if you are working with human or mouse model. If you working with non model-organisms it is a bit tricky, because you need to generate the mappability files. Read carefully the documentation because there is a lots of settings. But the we were happy with the results.

I'm working with some tumor-normal WGS pairs. Finished some runs, trying to make the plot........ which is taking some time.

I ran Control-FREEC with a pair of simulated tumor-normal data from this Syapse thing: https://www.synapse.org/#!Synapse:syn312572/wiki/60702

It took 3 hours to complete. The *_ratio.txt file is 113M. It took 3 minutes to produce the graph.
For the real tumor-normal pair (data file is slightly larger), it took 14 hours to complete. The The *_ratio.txt file is 169M. It took about an hour to finish plotting.

Why that discrepancy in time?

[lethalfang]

Just checking, it's a bad idea to split Copy Number Analysis jobs into chromosome by chromosome, right? It would be bad for the sample power.

[lethalfang]

I'm wondering for tumor-normal paired WGS, what are the commended settings for some of the following:

coefficientOfVariation:
The test file had 0.062. The manual has an "example" of 0.05. What's a good parameter from people's experience?

forceGCcontentNormalization:
Does GC content normalization improve the results for tumor-normal pairs? If so, is 1 or 2 better?
1: normalize GC content first, and then calculate sample/control ratio.
2: calculate sample/control ratio first, and then normalize GC content.

intercept:
1 - with GC content
0 - with a control dataset.
What if I have both?

minCNAlength:
What's a good setting based on people's experience?

Any other setting that people find particularly better for tumor-normal pairs?

Thanks.

[TiborNagy]

Why that discrepancy in time?

It can be anything: slow hard disk, old computer, etc.

Just checking, it's a bad idea to split Copy Number Analysis jobs into chromosome by chromosome, right?

I am not an expert, but I think it is not a bad idea.

[ffavero]

Hi guys,

I am looking at some whole genome sequencing (WGS) for tumor-normal pairs, and I want to find somatic copy number alteration in the tumors.
What tools do you guys recommend for these?

I have read about a few, e.g., BIC-Seq, OncoSNP-SEQ, CREST, etc., but have no experience. Any recommendations?

Thanks in advance.

Hi, you could try sequenza, is available on CRAN.

Is composed by a python script that generate a suitable file for the R-package, or alternatively VarScan2 output could be used.

It was developed for exome sequencing, but with whole-genome it works even better.

It's available from the institute page or from CRAN, the python script is bundled with the R package, as the documentation and example data.

As usual, higher depth, and higher tumor content are a goo thing, but I managed to analyse tumor sample with relatively low depth (10x) as well samples around ~20% of tumor content with satisfying results.

[lethalfang]

Hi, you could try sequenza, is available on CRAN.

Is composed by a python script that generate a suitable file for the R-package, or alternatively VarScan2 output could be used.

It was developed for exome sequencing, but with whole-genome it works even better.

It's available from the institute page or from CRAN, the python script is bundled with the R package, as the documentation and example data.

As usual, higher depth, and higher tumor content are a goo thing, but I managed to analyse tumor sample with relatively low depth (10x) as well samples around ~20% of tumor content with satisfying results.

Cool.
Is it possible to see the pre-print of your submitted paper, or do you have to wait until it's published?

[ffavero]

:-).
Well, I could but I'd like to see what the reviewers/editors have to say first.

We just describe the algorithm and compare the results runing on exome with respective SNP array from TCGA, as well compare the results of other similar algirithms.
Sequenza was, when not perfecly the same, was pretty close to the SNP array prediction...

[lethalfang]

:-).
Well, I could but I'd like to see what the reviewers/editors have to say first.

We just describe the algorithm and compare the results runing on exome with respective SNP array from TCGA, as well compare the results of other similar algirithms.
Sequenza was, when not perfecly the same, was pretty close to the SNP array prediction...

That sounds like OncoSNP-SEQ.

[ffavero]

That sounds like OncoSNP-SEQ.

Well oncoSNP-seq have a different inference implementation, plus it uses dbSNP to set heteozygous positions, while we use information from the germline.

It's written in MATLAB while we have implemented it in R, which should make it easier to use I suppose.

Anyway I've started the sofware from scratch , to have something working properly with exome, whitout borrow any concept from oncoSNP, they are ptetty different from each other, I would say.

I've tried to use oncoSNP-seq on exome, but it doesn't works well, as warned in the manual.
Although sequenza works pretty well with wgs.
If you want to perform some testing it it should be difficult to try them both and benchmark the difference.

[lethalfang]

Well oncoSNP-seq have a different inference implementation, plus it uses dbSNP to set heteozygous positions, while we use information from the germline.

It's written in MATLAB while we have implemented it in R, which should make it easier to use I suppose.

Anyway I've started the sofware from scratch , to have something working properly with exome, whitout borrow any concept from oncoSNP, they are ptetty different from each other, I would say.

I've tried to use oncoSNP-seq on exome, but it doesn't works well, as warned in the manual.
Although sequenza works pretty well with wgs.
If you want to perform some testing it it should be difficult to try them both and benchmark the difference.

Yeah installing MATLAB runtime was a pain.
It's much better to have a software without having to install a proprietary 3rd-party language.

[lethalfang]

Was trying

sequenza-utils.py pileup2seqz \
-gc ~/references/hg19.gc50Base.txt.gz \
-n ~/pileups_benchmark/wgs.normal.pileup.gz \
-t ~/pileups_benchmark/wgs.tumor.pileup.gz \
| bgzip > ~/out.seqz.gz

And I got an empty output file, with only the header. No error message.
The two pileups have chromosomes labeled as 1, 2, 3, ..., X, Y, M.

Any idea why?

Thanks.