KatharinaHoff
commented
1 year ago Dear Alex,
in the Supplementary of the BRAKER2 paper is a Figure S3 that answers the question of how much "incremental improvement' you get by going beyond 1000 training genes:
This is data from A. thaliana, we did the same experiment with other species. It look similar (not identical) across different species. Accuracy improvements are possible if you go beyond 1000 training genes. We used to make that claim ourselves, but it was years ago, and my perspective is that we had limited data when we came up with the idea that 1000 genes were "usually enough".
optimize_augustus.pl splits the training genes into buckets for k-fold cross validation. If you have a large number of training genes, it is usually ok to split into more than 8 buckets. GALBA ensures that k is set to a value such that each bucket has a minimum of 200 genes. It will thus create at most 40 buckets/occupy 40 threads if you have 8000 training genes. I routinely run GALBA (and BRAKER) with 48 threads (because that's our infrastructure on the small nodes, and going to e.g. 256 threads is indeed a waste of resources).
On Wed, Jun 14, 2023 at 1:50 PM Alex Leonard @.***> wrote:
Hi Katharina,
I had some questions regarding optimal running of GALBA for large mammalian genomes (~3 Gb). Currently GALBA downsamples to 8000 if there are too many training genes are identified out of miniprot
I came across this https://darencard.net/blog/2020-07-23-augustus-optimization/ (but not many other supporting claims) suggesting that training with more than 1000 has limited benefit. Do you know if that is still generally true, or perhaps the downsampling limit can be exposed as a parameter? I'd rather get a "pretty good" annotation done in reasonable time than the "best possible" annotation being killed after 24 hours on 8 cores.
The other question is the manuscript discusses runtimes when using 72 cores, but the README seems to suggest that running with more than 8 has limited benefit due to optimize_augustus.pl and other steps only using up to 8 cores. Is one of these statements more useful to follow?
@CEPHAS-01 https://github.com/CEPHAS-01, did you get GALBA to finish on your assemblies (and if so, what was the CPU time)?
Best, Alex
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ASLeonard
CEPHAS-01
Hi Katharina,
I had some questions regarding optimal running of GALBA for large mammalian genomes (~3 Gb). Currently GALBA downsamples to 8000 if there are too many training genes are identified out of miniprot
https://github.com/Gaius-Augustus/GALBA/blob/2b1f8253b326cbe47d3a6f2acbadcf08cb7a6420/scripts/galba.pl#L4123
I came across this (but not many other supporting claims) suggesting that training with more than 1000 has limited benefit. Do you know if that is still generally true, or perhaps the downsampling limit can be exposed as a parameter? I'd rather get a "pretty good" annotation done in reasonable time than the "best possible" annotation being killed after 24 hours on 8 cores.
The other question is the manuscript discusses runtimes when using 72 cores, but the README seems to suggest that running with more than 8 has limited benefit due to
optimize_augustus.pland other steps only using up to 8 cores. Is one of these statements more useful to follow?@CEPHAS-01, did you get GALBA to finish on your assemblies (and if so, what was the CPU time)?
Best, Alex