We then investigate how build impacts detection of outliers. Most outlier status is consistent between builds, but we do see that the amount gene’s quantification changes between builds is correlated with the average z-score change. (4/8)
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Finally, we look into the impact on clinically-relevant genes. We identified 341 rare disease & 68 cancer genes with changes between builds. In the top transcriptome-guided candidate lists we found annotation-specific genes and solved genes with rank differences between builds. (5/8)
This work was done extremely closely with Pagé Goddard, with huge help from Tanner Jensen and Fabien Degalez and Stephen Montgomery, along with the a wonderful team including Devon Bonner, Kevin Smith, Jon Bernstein, & @mwheelermd.bsky.social (7/8)
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