Question: cn.mops: copy number for low-coverage bam files?
0
gravatar for Paul Shannon
6.6 years ago by
Paul Shannon750
Paul Shannon750 wrote:
I'd like to take advantange of the segmentation algorightms in cn.mops, to estimate copy number from low-coverage BAM files. The average coverage in our tumor and normal DNA, is about 0.8 and 0.7 respectively, quite uniformly across the whole genome. The consistency of the reads, and their ratios, allow us to plausibly identify amplifications and deletions, either visually, or by simple calculations. Now it's time for more rigor, for which I turned to cn.mops. However, it appears that such low-coverage sequencing is apparently not well-suited to cn.mops. The man page says: minReadCount: If all samples are below this value the algorithm will, return the prior knowledge. This prevents that the algorithm, from being applied to segments with very low coverage. Default=1. Even if I set minReadCount to 0.2, no useful results are returned: Individual CNVs: GRanges with 2 ranges and 4 metadata columns: seqnames ranges strand | sampleName median mean CN <rle> <iranges> <rle> | <factor> <numeric> <numeric> <logical> [1] chr10 [ 42441001, 42553000] * | F7272LUNG.sorted.bam 0.5849625 0.5767351 <na> [2] chr10 [135478001, 135534746] * | F7272SCLC.sorted.bam 0.2196978 0.5403949 <na> Before I give up on using this impressive package, may I ask if there is indeed a way to use it with low-coverage but otherwise high-quality bam files? Thanks! - Paul
sequencing coverage cn.mops • 1.0k views
ADD COMMENTlink modified 6.6 years ago by Günter Klambauer540 • written 6.6 years ago by Paul Shannon750
Answer: cn.mops: copy number for low-coverage bam files?
0
gravatar for Günter Klambauer
6.6 years ago by
Austria
Günter Klambauer540 wrote:
Hello Paul, Thanks for your interest in cn.mops. It should be possible to identify CNVs quite reliably at this coverage, if you use the right parameters. The parameter "WL" in the function "getReadCountsFromBAM" is crucial for the analysis, because it determines the length of the segments, in which the reads are counted. If the coverage is low, the segments should be longer. Which "WL" (window length) did you use? The second important parameter that controls sensitivity and specificity is "priorImpact". I suppose you expect more CNVs in your case, therefore you should lower the parameter. The parameter "minReadCount" is not crucial for the algorithm, and should be left to default. Did you use the function "cn.mops" or "referencecn.mops"?? Do you have two samples (tumor and normal) or do you have more tumor samples and more normal samples. In the first case you should use "referencecn.mops" and in the second case "cn.mops". Please do not hesitate to ask, if you encounter any problems. Regards, G?nter On 04/30/2013 06:26 PM, Paul Shannon wrote: > I'd like to take advantange of the segmentation algorightms in cn.mops, to estimate copy number from low-coverage BAM files. The average coverage in our tumor and normal DNA, is about 0.8 and 0.7 respectively, quite uniformly across the whole genome. The consistency of the reads, and their ratios, allow us to plausibly identify amplifications and deletions, either visually, or by simple calculations. Now it's time for more rigor, for which I turned to cn.mops. > > However, it appears that such low-coverage sequencing is apparently not well-suited to cn.mops. > > The man page says: > > minReadCount: If all samples are below this value the algorithm will, return the prior knowledge. > This prevents that the algorithm, from being applied to segments with very low coverage. Default=1. > > Even if I set minReadCount to 0.2, no useful results are returned: > > Individual CNVs: > GRanges with 2 ranges and 4 metadata columns: > seqnames ranges strand | sampleName median mean CN > <rle> <iranges> <rle> | <factor> <numeric> <numeric> <logical> > [1] chr10 [ 42441001, 42553000] * | F7272LUNG.sorted.bam 0.5849625 0.5767351 <na> > [2] chr10 [135478001, 135534746] * | F7272SCLC.sorted.bam 0.2196978 0.5403949 <na> > > Before I give up on using this impressive package, may I ask if there is indeed a way to use it with low-coverage but otherwise high- quality bam files? > > Thanks! > > - Paul >
ADD COMMENTlink written 6.6 years ago by Günter Klambauer540
Hi Gunter, Thanks for this helpful reply. I am glad to learn that cn.mops will work with low coverage bam files -- very nice. It looks like I 1) changed a parameter I should have left at default ("minReadCount") 2) failed to change parameters which should be changed ("WL" and "priorImpact") BAMFiles <- list.files(pattern=".bam$") bamDataRanges <- getReadCountsFromBAM(BAMFiles, mode="unpaired") res <- cn.mops(bamDataRanges, minReadCount=0.2) Off-list you kindly offered to examine my bamDataRanges so that you could suggest the correct parameters. I will send you that Granges object, serialized (also off-list). After we figure this out, I will gladly summarize all this back to the list. Many thanks, - Paul On Apr 30, 2013, at 1:20 PM, G?nter Klambauer wrote: > Hello Paul, > > > Thanks for your interest in cn.mops. It should be possible to identify CNVs quite reliably at this coverage, if you use the right parameters. > The parameter "WL" in the function "getReadCountsFromBAM" is crucial for the analysis, because it determines the length of the segments, in which the reads are counted. If the coverage is low, the segments should be longer. Which "WL" (window length) did you use? > The second important parameter that controls sensitivity and specificity is "priorImpact". I suppose you expect more CNVs in your case, therefore you should lower the parameter. The parameter "minReadCount" is not crucial for the algorithm, and should be left to default. > > Did you use the function "cn.mops" or "referencecn.mops"?? Do you have two samples (tumor and normal) or do you have more tumor samples and more normal samples. In the first case you should use "referencecn.mops" and in the second case "cn.mops". > > Please do not hesitate to ask, if you encounter any problems. > > Regards, > G?nter > > > > > On 04/30/2013 06:26 PM, Paul Shannon wrote: >> I'd like to take advantange of the segmentation algorightms in cn.mops, to estimate copy number from low-coverage BAM files. The average coverage in our tumor and normal DNA, is about 0.8 and 0.7 respectively, quite uniformly across the whole genome. The consistency of the reads, and their ratios, allow us to plausibly identify amplifications and deletions, either visually, or by simple calculations. Now it's time for more rigor, for which I turned to cn.mops. >> >> However, it appears that such low-coverage sequencing is apparently not well-suited to cn.mops. >> >> The man page says: >> >> minReadCount: If all samples are below this value the algorithm will, return the prior knowledge. >> This prevents that the algorithm, from being applied to segments with very low coverage. Default=1. >> >> Even if I set minReadCount to 0.2, no useful results are returned: >> >> Individual CNVs: >> GRanges with 2 ranges and 4 metadata columns: >> seqnames ranges strand | sampleName median mean CN >> <rle> <iranges> <rle> | <factor> <numeric> <numeric> <logical> >> [1] chr10 [ 42441001, 42553000] * | F7272LUNG.sorted.bam 0.5849625 0.5767351 <na> >> [2] chr10 [135478001, 135534746] * | F7272SCLC.sorted.bam 0.2196978 0.5403949 <na> >> >> Before I give up on using this impressive package, may I ask if there is indeed a way to use it with low-coverage but otherwise high- quality bam files? >> >> Thanks! >> >> - Paul >> > > <klambauer.vcf>
ADD REPLYlink written 6.6 years ago by Paul Shannon750
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 177 users visited in the last hour