9bfd58221b1539193cb7f0a317b4e959c1c7e49a max Thu May 21 01:00:45 2026 -0700 varFreqs: AI generated text sounds bad, hard to read, so remove typical AI language. "humanizer" pass on all 31 varFreqs description pages — cut em dashes, copula avoidance ("serves as", "stands as"), "-ing" puffery, and boilerplate filler ("We provide documentation that indicates how..."). Title-case headings and meaningful emphasis preserved. No facts/URLs/counts/versions changed. tpmi.html added as a new file (was previously uncommitted). refs #36642 Co-Authored-By: Claude Sonnet 4.6 diff --git src/hg/makeDb/trackDb/human/gasp.html src/hg/makeDb/trackDb/human/gasp.html index 9a47fc8317c..62ee1ecc359 100644 --- src/hg/makeDb/trackDb/human/gasp.html +++ src/hg/makeDb/trackDb/human/gasp.html @@ -29,28 +29,28 @@ 2×100 bp or 2×150 bp paired-end reads at an average depth of 36x. Reads were aligned to GRCh37 using BWA-MEM. Duplicate reads were marked with SAMBLASTER and sorted with Sambamba. Per-sample variant calling was performed with GATK HaplotypeCaller in GVCF mode, followed by joint genotyping with GenotypeGVCFs. Variant quality score recalibration (VQSR) was applied at a 99% sensitivity tranche for both SNPs and indels. Sample-level QC included contamination checks with verifyBamID and sex concordance verification. The final callset contains ∼65 million variants across 1,739 individuals from 219 populations.

The upstream callset is on GRCh37. We lifted it to hg38 using CrossMap and the UCSC hg19ToHg38 chain file. After lifting, variants that landed on alt, random, fix, or unplaced contigs were dropped, and the result was sorted and indexed with tabix.

-We provide documentation that indicates how all source files of the varFreqs track were converted in the makeDoc file of the track. -For some tracks, python scripts were necessary and are also available from GitHub. +The makeDoc file documents how all source files of the varFreqs track were converted. +For some tracks, python scripts were needed and are also available from GitHub.

References

GenomeAsia100K Consortium. The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature. 2019 Dec;576(7785):106-111. PMID: 31802016; PMC: PMC7054211