Genome Analysis Toolkit

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

MQRankSum Bug?



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    Bhanu Gandham

    Hi mk

    I checked with our dev team and this is what they said:

    When we calculate the z-score for any ranksum statistic (which is what is written out in the tag of the bam), we are comparing a Mann-Whitney test statistic to its expected distribution under the null hypothesis.  To get the expected distribution under the null hypothesis, we can either calculate it exactly by counting the number of permutation of ref and alt labels which would lead to a smaller test statistic, or use a normal approximation.  Our current implementation of the exact calculation is rather inefficient, and so can only be used when we have a small number (less than ~10 to 20) of total reads.  The problem is that the normal approximation is only good when there are both a large number of ref AND a large number of alt reads, so there are scenarios, such as when we have a large number of alt reads but a small number of ref reads, when we have to use the normal approximation even though it may be wildly inaccurate.  This appears to be exactly the situation you are observing.
    Fortunately, after searching through the literature, it appears that there are some much more efficient algorithms for performing the exact calculation. I’m working through some thoughts on how to implement one of them, so possible improvement incoming.


    This is something we are investigating. Unfortunately I cannot promise a timeline on when we will resolve this but rest assured we are looking into it.

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    Thank you.

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