KaryoStudio FAQs

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  • Questions & Answers


  • You must load your data into the GenomeStudio Genotyping Module.

    You can export data displayed in the Found Regions table as either a single row or the entire table.

    • To export a single row: right-click in the Found Regions table and select Copy Row to Clipboard.
    • To export the whole table: right-click in the Found Regions table and select Copy All to Clipboard
    • Paste the data into an Excel file or import it into a third-party application. You can also export data as a cytogenetics report.

    Depending upon the number of division cycles, a tumor sample typically has a large number of aberrations across the length of the whole genome. You will see many regions of loss and gain in copy number. cnvPartition will identify many found regions in these samples. Genotyping call rates in these samples may be as low as 60%. The percentage of defects in these samples can be as high as 50% (or even higher). Congenital samples tend to have fewer aberrations and may only have <10 large deletions and duplications. Genotyping call rates in these samples should be above 95% more likely to be 98% or greater. The percentage of defects in these samples should be less than 1%.

    There currently is no limit on the number of samples that can be loaded into KaryoStudio.

    Refer to Appendix A of the KaryoStudio Software User Guide or see the KaryoStudio Benchmark Performance Technical Note for more information about KaryoStudio software performance.

    Click the Size column header to sort the data in the Found Regions table.

    Some settings for cnvPartition can be adjusted with a supplied configuration file. For information on adjusting the configuration file, refer to the algorithm release notes. For more information on this algorithm, see the CNV Algorithms Technical Note.

    Yes, this can be adjusted using Microsoft Excel. Information in any of the rows can be edited or deleted, or new rows can be added; however, no columns can be deleted. You can also create an entirely new known regions file and load it using the Filter Table interface. For more information about adjusting or creating new Known Regions lists, see the user guide.

    Yes, you can adjust the start and stop positions of a found region by right-clicking the found region and adjusting the genomic positions. You may need to use the zooming and panning functions to determine the exact position to which you would like to adjust the found region.

    An *.idat file is an intensity data file. It contains statistics for every bead type on your BeadChip. The statistics in an *.idat file include the number of beads, the mean, and the standard deviation for each color sample. There is one *.idat file per sample per channel.

    The minimum aberration size varies depending on the type of cytogenetics lab where the samples are analyzed. For example, some cytogenetics labs examine all aberrations > 250 kb in size. Others look at aberrations > 100 kb.

    You might want to set a threshold for the minimum number of SNPs per region, such as 50. This information is in the # SNPs column of the Found Regions table. When you allow for regions of a smaller size, more aberrations appear in the Found Regions table.

    In these cases, many regions might not yet be linked to a specific phenotype. Try beginning with the largest regions that are most likely to be linked to a phenotype already. Depending on the product, you might find aberrations as small as 1 kb.

    In the Found Regions table, click the # SNPs column header to sort the number of SNPs found in each region.

    Genotype information is not provided by KaryoStudio. To obtain the SNP genotypes from your data, you must load your data into the GenomeStudio Genotyping Module. For more information, refer to the GenomeStudio Genotyping Module v1 .0 User Guide.

    Updated versions are available from GenomeStudio downloads.

    In the Found Regions table, click the Clear filters button to allow aberrations as small as 1 kb to appear in your data. Then sort the data by clicking the Size column header.

    Gene information is preloaded into KaryoStudio and displayed in the IGV or in the Genes column of the Found Regions table. In addition, you can use the link to the UCSC Genome Browser, the Database of Genomic Variants, and DECIPHER; all of which provide additional gene information.

    With Infinium products, the two main parameters for copy number are the B Allele Frequency (based on genotypes) and the Log R Ratio (based on intensities). The Log R Ratio is the log (base two) of the "observed intensity" divided by the "expected intensity". The "expected" intensity is generated from the cluster file.

    Because of this direct comparison, accurately measuring the sample input amount is vital. Essentially, your input amount should match the recommended value (400 ng for Infinium HD Duo; 200 ng for Infinium HD Quad and Infinium HD 12-sample products). If this is this case, your Log R Ratio signal will tend to have low noise.

    When the DNA samples are inaccurately quantified, you may see an "undulation" pattern (this looks like waves) in the log R ratio. This tends to be in GC-rich regions of the genome. This wavy pattern makes it difficult to do CNV analysis as the waves themselves look like copy number changes. This tends to confuse algorithms and confound analysis. On the other hand, call rates tend to be only slightly affected by this change (but this varies).

    Also called a SNP manifest, a beadpool manifest is a file containing the SNP-to-beadtype mapping and all SNP annotations.

    For the Infinium assay, the beadpool manifest is a BPM file in binary format.

    You can zoom into a specific region of interest by clicking on the specific aberration that is shown within the Found Regions table. The chromosome viewer then displays a closer view of the aberration of interest.

    Alternately, use the zoom functions available in the toolbar or drag and stretch the red box on the ideogram to zoom in for a closer view of your data.

    KaryoStudio provides several items for you to examine when you are QCing data. The LogRDev metric provides a measure of noise in the intensity data, and is essentially a measure of standard deviation of Log R values across the autosomes. The "percent aberration" metric is a sum of all of the found regions in a sample divided by the entire length of the genome. In a blood sample, where you expect to have little to no aberrations, you will see a very small (<1%) measure for % aberration. In cases where you have a higher number, it may indicate a sample processing issue. Both of these metrics can be impacted by real biological variation in samples, so they should be examined holistically while taking into account the data viewed in the IGV.

    For specific troubleshooting issues and access to the controls dashboard, load your data into GenomeStudio, if you have access to that software. Otherwise, contact technical support with any additional questions on troubleshooting your data.

    After you set how many aberrations should go into the report, KaryoStudio shows the aberrations based upon descending size. Only found regions with a checkmark in the Found Regions Table are displayed in the report.

    Yes, it is possible to study copy-neutral LOH with KaryoStudio and cnvPartition. To ensure that you have the latest version of the cnvPartition algorithm, contact Illumina Technical Support. Note that the amount of copy-neutral LOH present across a typical genome can be quite large. Illumina recommends setting the filter to a large size to limit the number of regions found by the algorithm.

    The cluster file contains the mean (R) and standard deviation (theta) of the cluster positions in normalized coordinates for every genotype for every SNP. The cluster file also includes cluster score information and the allele frequencies from the training set used to generate the cluster file.

    KaryoStudio requires a cluster file. Illumina provides a standard cluster file for each product. Alternatively, you can generate your own cluster file.