60ec88a5ecc6afec9f5b5007876eee53d0300c37 ccpowell Mon Oct 14 16:43:57 2019 -0700 Updating newsArch.html for GENCODE V32 and VM23 release, refs #24087 diff --git src/hg/htdocs/goldenPath/newsarch.html src/hg/htdocs/goldenPath/newsarch.html index 2d4dc40..068b37e 100755 --- src/hg/htdocs/goldenPath/newsarch.html +++ src/hg/htdocs/goldenPath/newsarch.html @@ -39,30 +39,60 @@
+ ++We are pleased to announce the release of the new GENCODE Gene annotation track for both +hg38/GRCh37 and +mm10/GRCm38 assemblies +corresponding to the + +Ensembl 98 release.
+ ++The following table provides statistics for the +human V32 and +mouse VM23 releases derived +from the GTF files that contains annotations only on the main chromosomes. More information on how +they were generated can be found in the +GENCODE site. +
Genes & transcripts | human V32 Release stats | mouse V23 Release stats |
---|---|---|
Protein-coding Genes | 19,965 | 21,849 |
Protein-coding transcripts | 83,986 | 59,188 |
Long non-coding RNA genes | 17,910 | 13,201 |
Small non-coding RNA genes | 7,576 | 6,108 |
+More details about the new GENCODE track can be found on the resepctive GENCODE description pages. +We would like to thank the GENCODE team and thank Brian Raney, Mark Diekhans, Jairo Navarro and +Conner Powell at UCSC for their work on this track.
+A new "group auto-scale" option is now available for signal tracks collected together in composites. The original auto-scale setting, which is still available, acts to auto-scale each track individually inside a composite group. With the new "group auto-scale" setting, all of the tracks within the composite will scale to the track with the highest auto-scale value viewed in the region.
For example, below is a side-by-side image of two views of the same data from a selection of cell lines within a composite of related RNA-seq experiments. On the left is the original "auto-scale to data view" setting, where each track is auto-scaled to appear at each track's highest value. And on the right is the new "group auto-scale" setting for the same RNA-seq data where all tracks are scaled against the one track in the region that has the highest value