Closed mvelinder closed 4 years ago
Sorry this fell through the cracks!
How much memory does your machine have? One thing that may not be intuitive for a first-time user is that the -M
flag specifies the amount of memory to use for each sample. So if your machine doesn't have 240x3Gb of free memory then you would expect to run into problems. It appears that is what has happened here.
From your false positive rates, it looks like you should be fine reducing the memory quite a bit and still be fine. I usually aim for an FPR of 0.1-0.2 for the control/parent samples and 0.2-0.4 for the case/proband sample (high FPR in the proband can be compensated for at the kevlar filter
step). For 30-40x coverage data, this usually required 64-72Gb per sample. Doing error correction on the reads beforehand can reduce this drastically (the majority of unique k-mers span a sequencing error), but you might miss a few SNPs this way.
Hope this helps!
Also, I'd recommend running kevlar count
before kevlar novel
so you don't have to re-count k-mers every time you run the novel k-mer discovery step.
In fact, I'd recommend this Snakemake workflow for executing the entire pipeline. Please feel free to ask if anything is unclear.
Feel free to reopen this thread if you have any additional questions.
Trying to run kevlar... here's what's happening
Any help would be great. Thanks!