First off, I was very happy to see this automated QC tool published, and I now use it for the Strand-seq libraries we produce in Vancouver. By my own testing, it accords with my manual QC ~95% of the time, and it incorrectly marks libraries that I consider "good" as "poor" only ~0.5% of the time, which is great. Thank you!
With a recent round of libraries I noticed something a bit odd. We intentionally sequenced a mix of +BrdU and -BrdU cells, and I applied ASHLEYS to all of them. For some reason, ASHLEYS marked quite a few of the -BrdU cells as good-quality Strand-seq libraries, even though all chromosomes had WC strand state (see image for an example; each row is a cell). Even if Strand-seq libraries are made with BrdU, some cells may be sequenced that don't incorporate BrdU, and then this tool might call them good quality. Such libraries could be excluded using separate filters, but it would be nice if ASHLEYS did all the QC in one pass.

Here's the code I used to run ASHLEYS:
ashleys.py -j 12 features -f /path/to/dir/ -w 5000000 2000000 1000000 800000 600000 400000 200000 -o features.tsv
ashleys.py predict -p features.tsv -o quality.txt -m /path/to/ASHLEYS/models/svc_default.pkl
First off, I was very happy to see this automated QC tool published, and I now use it for the Strand-seq libraries we produce in Vancouver. By my own testing, it accords with my manual QC ~95% of the time, and it incorrectly marks libraries that I consider "good" as "poor" only ~0.5% of the time, which is great. Thank you!
With a recent round of libraries I noticed something a bit odd. We intentionally sequenced a mix of +BrdU and -BrdU cells, and I applied ASHLEYS to all of them. For some reason, ASHLEYS marked quite a few of the -BrdU cells as good-quality Strand-seq libraries, even though all chromosomes had WC strand state (see image for an example; each row is a cell). Even if Strand-seq libraries are made with BrdU, some cells may be sequenced that don't incorporate BrdU, and then this tool might call them good quality. Such libraries could be excluded using separate filters, but it would be nice if ASHLEYS did all the QC in one pass.
Here's the code I used to run ASHLEYS:
ashleys.py -j 12 features -f /path/to/dir/ -w 5000000 2000000 1000000 800000 600000 400000 200000 -o features.tsvashleys.py predict -p features.tsv -o quality.txt -m /path/to/ASHLEYS/models/svc_default.pkl