Hi All I need small help I have microarray affymetrix data cytoscan 750K. How can we detect breakpoint by cytoscan 750K microarray.
For that microarray technology and for what you want to achieve, I would use the tools from the Aroma Project. In particular, you should follow the vignette for CRMA (v2): Estimation of total copy numbers using the CRMA v2 method. This consists of CRMA normalisation followed by circular binary segmentation (CBS) on the derived estimated copy number at each site:
In following the vignette, for the annotation loading section, you will want to run this for your chip type:
> cdf <- AffymetrixCdfFile$byChipType("CytoScan750K_Array")
> cdf
AffymetrixCdfFile:
Path: annotationData/chipTypes/CytoScan750K_Array
Filename: CytoScan750K_Array.cdf
Filesize: 161.10MB
Chip type: CytoScan750K_Array
RAM: 0.00MB
File format: v4 (binary; XDA)
Dimension: 2166x2166
Number of cells: 4691556
Number of units: 750865
Cells per unit: 6.25
Number of QC units: 4
[source: http://www.aroma-project.org/chipTypes/CytoScan750K_Array/]
Finally, this is implemented in the aroma.affymetrix R package.
Kevin
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Hi Kevin But this will not show me whether there is unbalanced or balanced translocations
Okay, in any case, use the method to at least derive the copy number, and then use those values with another program to get exactly what you want.
Can you elaborate on 'balanced' and 'unbalanced' translocations? - you originally only mentioned that you had wanted to identify breakpoints.
I have POC sample product of conception (Aborted child). Both parents are showing LOH and child is showing gain loss and LOH all three. What we are suspecting that death of child was due to unbalanced translocation of chromosome because have balanced translocation.
So i am given the task for detection of breakpoints and unbalanced region from Cytoscan 750K in child
Sounds very interesting and important. You would have to therefore derive allele-specific copy number, and there appears to be a previous publication for that involving the CytoScan 750K: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086940/
What is the difference between the R package you provided earlier and this rawcopy?
They can both derive allele-specific copy number, from what I can see. However, I favour the second one that I have found because it seems easier to implement (I am not sure of your level of bioinformatics, and I am aware that Aroma can be difficult to follow), and it was also specifically designed for the high-density Affymetrix arrays (SNP 6.0 and CytoScan).
Okay Kevin ,
Thanks for the help.
Hi Kevin This script is not working for me cdf <- AffymetrixCdfFile$byChipType("CytoScan750K_Array")
I am unbale to find CDF file for Cytoscan750_Array in Bioconductor. If it is not available there can you please tell me where will i get that file .
You will have to download the CDF for the CytoScan 750K, and then place i in a directory called ./annotationData/chipTypes/CytoScan750K_Array/
The file should be saved as:
CytoScan750K_Array.cdfIt seems that Aroma and the CytoScan are not well supported anymore, but I think that you can download the CDF from here (bundled with other files): https://bitbucket.org/brge/affy2sv/wiki/CytoScan
If you cannot get it working, then the other program / R package (rawcopy) seems promising.
Kevin Just want to confirm I am in correct path breakpoint will loss or gain at particular position of any arms right. So for example I have Loss in chr12 with position showing 12q31(10-100) X3 So loss is deletion at position 12q31 start coordinate 10 and end coordinate 100. This will be my breakpoint
Okay, that would be a small region, though, with just 90bp. It may be more appropriate to just refer to it as a copy number alteration (CNA). For breakpoints, we usually refer to larger section of DNA, i.e., larger genomic aberrations.
How did you derive the copy number? - rawcopy?
When we derive copy number for each probe region, we then typically agglomerate the copy number values into larger regions, such that we can identify things like this:
Those are both from my PhD (8 years ago) and using Affymetrix SNP 6.0. Back then, algorithms were not great for copy number. In the first, there is a possible breakpoint at 4q22.2. The second figure is indicative of iso-cromoome 8 (deletion of 1 arm; amplification of the other).
So, have you already derived copy number from the samples? Can you share some code? rawcopy should have some plotting or agglomeration function. You can open a new question if you want to get more fresh ideas. If you have already derived copy number, then that is a good starting point for diverse types of analyses. I was worried that the CytoScan 750K was no longer supported, so, sucess in that sense.
Kevin
This is the result i got from Rawcopy. https://ibb.co/c02TYn
How should i interpret it now also how will i be able to whether its unbalanced or balanced translocations
There does not appear to be any copy number aberrations...
Okay. But can i detect unbalanced translocations using Cytoscan750K microarray and using R for cytoscan 750K
Possibly not - why did you choose the CytoScan 750k? You can certainly identify breakpoints with CytoScan 750K, which was your original question.
To detect de novo translocations, you would have to do whole genome sequencing and preferably phased whole genome sequencing, which is very expensive.
If not de novo identification, I presume that you have a target region of interest?
no with this breakpoint i need to make probes for fish. But my customer is saying to me that it is possible with microarray to check for unbalanced translocations.
I don't recall seeing translocation information from microarray, but I do not doubt that it is technically feasible.
Have you come across the 'CytoScan Cytogenetics / Optima Suite'? - take a look Here. In addition, you may be interested in Chromosome Analysis Suite.
Apologies for sending you on wild goose chases, but the CytoScan never really took off and is not supported that much, as I mentioned. I attended a meeting with the Affy CEO a few years ago, in fact, and have his business card here.