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Variant Discovery in High-Throughput Sequencing Data

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Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. Learn more

Broken Links to Jupyter Notebooks in the "After gCNV calling considerations" Article



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    gamze maden

    I also have the same problem. Could you please fix the links or share with us the notebooks? My email is

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    Gökalp Çelik

    Hi gamze maden & Fatma Zehra Sarı

    We do not have any strict practice for any such notebooks in publishing, so the sentence about using Jupyter Notebooks to interact with the data is an open suggestion who would like to see how the results are in a meta-analysis stage. The article originally did not include any such links to notebooks. From the looks of it hyperlinks to the images Derek and the team created got nuked and will be fixed shortly. 

    As a general recommendation, there are many possible ways to interact with GCNV data and the most likely place to start is the denoised copy ratios and GCNV segment files emitted by the PostProcessGermlineCNVCalls. Combining all this data with allelic balances and plotting using various tools available in R or python or elsewhere can get you a better picture of how all your calls look like. As all of these steps can be done interactively under a Jupyter Notebook we suggested it in the article as a generic advice. 

    We will modify the article to make it clear for anyone who reads it in the future along with fixes to those beautiful visuals that our team created. 

    I hope this clarifies the issue. 


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