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Alright. So here is more background and my current solution.
Datasets:
two sets of HiFi reads, each ~250-300X (i.e. 500-600X total)
1 set of ONT ultra-long reads, ~120X
Hi-C reads
Solution:
I down-sampled to 100X of the longest HiFi reads, and optionally down to 100X of the longest ONT reads.
HiFiasm finished in a day with 100 GB memory allotted to it. This is in comparison to needing to allot 500-1500 GB memory and it not finishing even within 7 days.
Any further discussion on down-sampling would be appreciated.
Is down-sampling to 100X of the longest reads a recommended practice?
Would a higher or lower coverage target for down-sampling be more recommended than 100X?
Are there problems, other than memory and time, that arise when coverage is too high? For example, perhaps some errors begin to look like real variation?
And as for how this issue/insight could potentially enhance the user experience for HiFiasm:
Could there be a down-sampling option built into HiFiasm to tell it to just use 100X or 100X of longest reads? Or even down-sampling by read length minimum, i.e. reads > _ kb?
Hi,
As always, thank you for your tools, innovation, and help on issues.
I've launched a HiFiasm job twice now. Both times it failed to complete in 7 days.
The first time it did finish making the bin files and such.
So the second time it was able to start from there.
However, from what I can tell, there were no additional files written or any new landmarks for HiFiasm to start from.
So, I have requested a relaunch, but I am not hopeful it will finish. I think it will just be a repeat of the first re-launch.
Some details:
Is there a stepwise set of commands I can run to help break this job up into smaller pieces that can each finish in 7 days or less?
Any guidance would be much appreciated.
Best,
John
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