Closed ndreey closed 1 year ago
Diluting the already mock data seems more efficient than having NGSNGS generate 10 samples using P_zinji ref + mock data reference genomes.
Mentioned this in a different issue. "I were to dilute a 4.5GB mock data file to reach 90% P. zijinensis the file would become ~45GB??."
This is not good..
From the paper:
This leaves me with these options..
A. use CAMISIM to generate mock data the same way CAMI did to generate the root rizhosphere data but instead change the abundance profiles so that 90% become P. zijinensis. Could also change so we get more fungal endophytic abundance than bacterial.
B. The A. thaliana reads are already removed from the mock data. Thus 90.5% of the data is bacterial and 9.5% is fungal. The idea now is to reduce this data by taking a subsample of this filtered mock data (1gb) and then dilute the subsample so i reach 90% P. zijinensis reads.
The most logical will be to run CAMISIM as it was designed to create benchmark datasets. I will therefore explore option A.
Does not have to be perfect, a simple script will suffice.
Number of reference reads / total number of reads = host contamination
I believe benchmarking of 0, 50, 80 and 95% host contamination will be statistically significant (maybe skip 80% depending on time, but would be nice for correlation).