198c9b8daecc44fbda6a6494c566c723920f030a lrnassar Wed Mar 11 18:25:21 2026 -0700 Fixing a few hundred clear typos with the help of Claude. Some are less important in code comments, but majority of them are in user-facing places. I manually approved 60%+ of the changes and didn't see any that were an incorrect suggestion, at worst it was potentially uncessesary, like a code comment having cant instead of can't. No RM. diff --git src/hg/makeDb/trackDb/human/hg38/gnomadPLI.html src/hg/makeDb/trackDb/human/hg38/gnomadPLI.html index b1f4a41885c..3a3581d274f 100644 --- src/hg/makeDb/trackDb/human/hg38/gnomadPLI.html +++ src/hg/makeDb/trackDb/human/hg38/gnomadPLI.html @@ -77,31 +77,31 @@ <p> When evaluating how constrained a gene is, <b>it is essential to consider the CI when using O/E</b>. In research and clinical interpretation of Mendelian cases, <b>pLI > 0.9</b> has been widely used for filtering. Accordingly, the Gnomad team suggests using the upper bound of the O/E confidence interval <b>LOEUF < 0.35</b> as a threshold if needed. <p> Please see the Methods section below for more information about how the scores were calculated. </p> <h3>pLI and Z-scores</h3> <p> The pLI and Z-scores of the deviation of observed variant counts relative to the expected number are intended to measure how constrained or intolerant a gene or transcript is to a specific type of variation. Genes or transcripts that are particularly depleted of a specific class of variation (as observed in the gnomAD data set) are considered intolerant of that specific type of variation. -Z-scores are available for the missense and synonynmous categories and pLI scores are available for +Z-scores are available for the missense and synonymous categories and pLI scores are available for the loss-of-function variation. </p> <p> <em>Missense and Synonymous</em>: Positive Z-scores indicate more constraint (fewer observed variants than expected), and negative scores indicate less constraint (more observed variants than expected). <b>A greater Z-score indicates more intolerance to the class of variation.</b> Z-scores were generated by a sequence-context-based mutational model that predicted the number of expected rare (< 1% MAF) variants per transcript. The square root of the chi-squared value of the deviation of observed counts from expected counts was multiplied by -1 if the observed count was greater than the expected and vice versa. For the synonymous score, each Z-score was corrected by dividing by the standard deviation of all synonymous Z-scores between -5 and 5. For the missense scores, a mirrored distribution of all Z-scores between -5 and 0 was created, and then all missense Z-scores were corrected by dividing by the standard deviation of the Z-score of the mirror distribution. </p>