Comments (5)
Hey Jamie,
This is definitely a problem, thanks for pointing this out. I have re-written how the stratified data is output and it is much faster now. I haven't added an option yet for non-stratified output only.
Thanks,
Gavin
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metagenome_pipeline.py
outputs functions stratified by functional contributions by default now. per_sample_functions.py
is still experimental script to use probability distributions rather than discrete predictions.
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Hi @gavinmdouglas , I updated the local clone today and noticed that metagenome_pipeline.py is taking a long time to run (job has been running for 8 hours and still running). I guess it's because it's calculating stratified output? Is there a way to turn off this option? Thank you so much.
Best,
-Jamie
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Thanks so much Gavin! It is blazing fast now, but I noticed there is much less number of lines in the pred_metagenome_unstrat.tsv compared to the OUT_PREFIX.genefamilies.biom.tsv in a previous version running with the same data (853 lines vs 3333 lines respectively). Also strangely, in pred_metagenome_strat.tsv, when I check which sequences are mapped to the EC's, only a few (9 out of 485 sequences) are used/output. For example:
$ cut -f2 pred_metagenome_strat.tsv | sort-uniq-count-rank
595 seq_16
549 seq_13
504 seq_11
455 seq_9
454 seq_4
454 seq_6
443 seq_2
442 seq_5
160 seq_7
1 sequence
Coincidentally these sequences are the very first ones in my data. Could this be a bug (i.e. not all output was written) or PICRUSt2 only mapped a few of my sequences to genes?
Best,
Jamie
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Just to follow-up, the problem was gone as of the latest clone of PICRUSt2 yesterday.
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