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nextgenusfs / funannotate

Eukaryotic Genome Annotation Pipeline
http://funannotate.readthedocs.io
BSD 2-Clause "Simplified" License
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Error at: Predicting tRNAs #177

Closed KnudNorNielsen closed 5 years ago

KnudNorNielsen commented 6 years ago

Hi,

Running Funannotate v1.3.2, I got and error in the Predicting tRNAs step:

File "/services/tools/anaconda2/4.4.0/lib/python2.7/subprocess.py", line 1025, in _execute_child raise child_exception OSError: [Errno 2] No such file or directory

What is this about? Do you have any idea about how I should proced?

Cheers Knud

Error log:

[knunie@computerome01 mg_guests]$ cat run.funp180601.e
[03:39 PM]: OS: linux2, 28 cores, ~ 132 GB RAM. Python: 2.7.14
[03:39 PM]: Running funannotate v1.3.2
[03:40 PM]: Augustus training set for fusarium_graminearum already exists. To re-train provide unique --augustus_species argument
[03:40 PM]: AUGUSTUS (3.3) detected, version seems to be compatible with BRAKER and BUSCO
[03:40 PM]: Loading sequences and soft-masking genome
[03:40 PM]: Soft-masking: building RepeatModeler database
[03:40 PM]: Soft-masking: generating repeat library using RepeatModeler
[07:29 PM]: Soft-masking: running RepeatMasker with custom library
[07:50 PM]: Masked genome: 19 scaffolds; 37,431,244 bp; 3.73% repeats masked
[07:51 PM]: Mapping proteins to genome using Diamond blastx/Exonerate
[07:51 PM]: Using 545,147 proteins as queries
[07:51 PM]: Running Diamond pre-filter search
[08:07 PM]: Found 328,356 preliminary alignments
[03:04 AM]: Exonerate finished: found 1,232 alignments
[03:05 AM]: Running GeneMark-ES on assembly
[03:49 AM]: Converting GeneMark GTF file to GFF3
[03:49 AM]: Found 11,200 gene models
[03:49 AM]: Running Augustus gene prediction
[04:11 AM]: Found 11,546 gene models
[04:11 AM]: Pulling out high quality Augustus predictions
[04:11 AM]: Found 122 high quality predictions from Augustus (>90% exon evidence)
[04:11 AM]: Summary of gene models passed to EVM (weights):
-------------------------------------------------------
Augustus models (1):    11,431
GeneMark models (1):    11,200
Hi-Q models (5):        122
PASA gene models (10):  0
Other gene models (1):  0
Total gene models:      22,753
-------------------------------------------------------
[04:11 AM]: Setting up EVM partitions
[04:12 AM]: Generating EVM command list
[04:12 AM]: Running EVM commands with 1 CPUs
[04:48 AM]: Combining partitioned EVM outputs
[04:48 AM]: Converting EVM output to GFF3
[04:48 AM]: Collecting all EVM results
[04:48 AM]: 11,907 total gene models from EVM
[04:48 AM]: Generating protein fasta files from 11,907 EVM models
[04:48 AM]: now filtering out bad gene models (< 50 aa in length, transposable elements, etc).
[04:49 AM]: Found 90 gene models to remove: 0 too short; 0 span gaps; 90 transposable elements
[04:49 AM]: 11,817 gene models remaining
[04:49 AM]: Predicting tRNAs
Traceback (most recent call last):
  File "/services/tools/funannotate/1.3.2/bin/funannotate-predict.py", line 1232, in <module>
    lib.runtRNAscan(MaskGenome, os.path.join(args.out,'predict_misc'), tRNAscan)
  File "/services/tools/funannotate/1.3.2/lib/library.py", line 3964, in runtRNAscan
    runSubprocess(cmd, '.', log)
  File "/services/tools/funannotate/1.3.2/lib/library.py", line 590, in runSubprocess
    proc = subprocess.Popen(cmd, cwd=dir, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
  File "/services/tools/anaconda2/4.4.0/lib/python2.7/subprocess.py", line 390, in __init__
    errread, errwrite)
  File "/services/tools/anaconda2/4.4.0/lib/python2.7/subprocess.py", line 1025, in _execute_child
    raise child_exception
OSError: [Errno 2] No such file or directory
nextgenusfs commented 6 years ago

Probably tRNAscan is not installed correctly -- you can check the log file in output_dir/logfiles/funannotate-predict.log which will have some more information.

nextgenusfs commented 6 years ago

And it will run a lot faster if you give it more cores, i.e. --cpus 12 -- default is to only use --cpus 2.

KnudNorNielsen commented 6 years ago

Looking into the output_dir/logfiles/funannotate-predict.log:

[06/02/18 04:49:25]: bedtools intersect -f 0.9 -a /home/projects/mg_guests/people/knunie/Funannotate/predict180601/predict_misc/evm.round1.gff3 -b /home/projects/mg_guests/people/knunie/Funannotate/predict180601/predict_misc/repeatmasker.gff3
[06/02/18 04:49:26]: Found 90 gene models to remove: 0 too short; 0 span gaps; 90 transposable elements
[06/02/18 04:49:26]: 11,817 gene models remaining
[06/02/18 04:49:26]: Predicting tRNAs
[06/02/18 04:49:26]: tRNAscan-SE -o Funannotate/predict180601/predict_misc/tRNAscan.out /home/projects/mg_guests/people/knunie/Funannotate/predict180601/predict_misc/genome.softmasked.fa
nextgenusfs commented 6 years ago

There isn't an error after that in the log file? Try to run that command manually..

tRNAscan-SE -o Funannotate/predict180601/predict_misc/tRNAscan.out /home/projects/mg_guests/people/knunie/Funannotate/predict180601/predict_misc/genome.softmasked.fa
KnudNorNielsen commented 6 years ago

Nop, that was the end of it. Manually? How would that command look?

nextgenusfs commented 6 years ago

Just copy/paste that into terminal....

tRNAscan-SE -o Funannotate/predict180601/predict_misc/tRNAscan.out /home/projects/mg_guests/people/knunie/Funannotate/predict180601/predict_misc/genome.softmasked.fa
KnudNorNielsen commented 6 years ago

I have a server where I will have to call the script, like: -de /services/tools/funannotate/1.3.2/bin/funannotate-predict.py I presume this chances the command need:

Given the command above it just says: -bash: tRNAscan-SE: command not found

KnudNorNielsen commented 6 years ago

The command used to executive the prediction step: xqsub -d pwd -M knn@ign.ku.dk -m be -l nodes=1:ppn=28,mem=120gb,walltime=4:00:00:00 -re run.funp180601.e -ro run.funp180601.o -N run.funp180601 -V -de /services/tools/funannotate/1.3.2/bin/funannotate-predict.py -i Funannotate/UCPHDK1.ann.180601.fa -o Funannotate/predict180601 --species "fusarium graminearum"

where I will include --cpus 12 next time. Are there restrictions on the --cpus number - can I set it to 28?

Thank you for the generuos suport.

nextgenusfs commented 6 years ago

So what I think is the problem is that tRNAscan-SE is not installed correctly on the server. So if you have to issue the command, perhaps it would look like this??:

xqsub -d pwd -M knn@ign.ku.dk -m be -l nodes=1:ppn=28,mem=120gb,walltime=4:00:00:00 \
-re run.funp180601.e -ro run.funp180601.o -N run.funp180601 -V \
-de tRNAscan-SE -h

This would perhaps simply output the help menu? Or do you need to specify the actual path? Can you log in interactively instead?

KnudNorNielsen commented 6 years ago

I think I need the path - as when I inputted /services/tools/funannotate/1.3.2/bin/funannotate-predict.py I got the following trying:

[knunie@computerome01 mg_guests]$ cat run.funp180606.e
/var/spool/torque/mom_priv/jobs/19847999.risoe-r04-sn064.cm.cluster.SC: line 4: exec: tRNAscan-SE: not found

Is tRNAscan an external dependency? If I run funannotate check I get the following:

[knunie@computerome01 logfiles]$ funannotate check --show-versions
-------------------------------------------------------
Checking dependencies for funannotate v1.3.2
-------------------------------------------------------
You are running Python v 2.7.14. Now checking python packages...
biopython: 1.70
goatools: 0.7.11
matplotlib: 2.1.0
natsort: 5.2.0
numpy: 1.14.2
pandas: 0.22.0
psutil: 5.4.3
requests: 2.18.4
scikit-learn: 0.19.1
scipy: 1.0.1
seaborn: 0.8.1
All 11 python packages installed

You are running Perl v 5.024000. Now checking perl modules...
Bio::Perl: 1.007001
Carp: 1.40
Clone: 0.39
DBD::SQLite: 1.54
DBD::mysql: 4.042
DBI: 1.636
DB_File: 1.84
Data::Dumper: 2.161
File::Basename: 2.85
File::Which: 1.21
Getopt::Long: 2.5
Hash::Merge: 0.200
JSON: 2.94
LWP::UserAgent: 6.26
Logger::Simple: 2.0
POSIX: 1.65
Parallel::ForkManager: 1.19
Pod::Usage: 1.69
Scalar::Util::Numeric: 0.40
Storable: 2.56
Text::Soundex: 3.05
Thread::Queue: 3.12
Tie::File: 1.02
URI::Escape: 3.31
YAML: 1.23
threads: 2.16
threads::shared: 1.57
All 27 Perl modules installed

Checking external dependencies...
RepeatMasker: RepeatMasker 4.0.7
RepeatModeler: RepeatModeler version DEV
Trinity: 2.4.0
augustus: 3.3
bamtools: bamtools 2.5.1
bedtools: bedtools v2.26.0
blat: BLAT v36x2
diamond: diamond 0.8.22
emapper.py: emapper-0.12.7
ete3: 3.1.1
exonerate: exonerate 2.4.0
fasta: no way to determine
gmap: 2018-03-25
gmes_petap.pl: 4.35
hisat2: 2.1.0
hmmscan: HMMER 3.1b2 (February 2015)
hmmsearch: HMMER 3.1b2 (February 2015)
java: 1.8.0_121
kallisto: 0.44.0
mafft: v7.313 (2017/Nov/15)
makeblastdb: makeblastdb 2.2.28+
minimap2: 2.6-r623
nucmer: 3.1
pslCDnaFilter: no way to determine
rmOutToGFF3.pl: 0.1
rmblastn: rmblastn 2.2.27+
samtools: samtools 1.5
tbl2asn: unknown, likely 25.3
tblastn: tblastn 2.2.28+
trimal: trimAl v1.4.rev15 build[2013-12-17]
All 30 external dependencies are installed

Checking Environmental Variables...
$FUNANNOTATE_DB=/home/people/knunie/mg_guests/funannotate_db4
$PASAHOME=/services/tools/pasapipeline/2.2.0
$TRINITYHOME=/services/tools/trinityrnaseq/2.4.0
$EVM_HOME=/services/tools/evm/1.1.1
$AUGUSTUS_CONFIG_PATH=/services/tools/augustus/3.3/config
$GENEMARK_PATH=/services/tools/genemark-es/4.33/gmes_petap
$BAMTOOLS_PATH=/services/tools/bamtools/2.5.1
All 7 environmental variables are set
nextgenusfs commented 6 years ago

Yes it is, but I forgot to add to funannotate check -- I just pushed a commit that adds it. I didn't realize it wasn't being looked for, sorry about that.

nextgenusfs commented 6 years ago

I could make it conditional in next version, i.e. it only runs if it is installed -- but I think probably most people would want tRNAs annotated and it isn't a very time sensitive search.

KnudNorNielsen commented 6 years ago

Ok, so what should I ask my HPC support? tRNAscan version number? and an update of funannotate to 1.3.3.

nextgenusfs commented 6 years ago

If you give me an hour or so I will bump a new version to 1.3.4 with these fixes + a few others. But yes, just need to have tRNAscan-SE installed, the version is quite old that I'm running v1.2.3.

KnudNorNielsen commented 6 years ago

That sounds great! Will you check whether we can use the newest version of tRNAscan? Thanks for your time and help - I will look at your changes tomorrow (The workday is at its end in Denmark)

nextgenusfs commented 6 years ago

Okay, just released a new version. Any version of tRNAscan-SE should work - as long as installed correctly, funannotate just uses the default settings.

KnudNorNielsen commented 6 years ago

Hi, again. Thanks for the new version. I have rerun predict and everything looks fine.

[02:41 PM]: Funannotate predict is finished, output files are in the Funannotate/predict180607/predict_results folder
[02:41 PM]: Your next step might be functional annotation, suggested commands:

Just one thing: the two lines above come from an error massage - but I presume that we don't have an error? Full error log 

[knunie@computerome01 mg_guests]$ cat run.funp180607.e
[11:05 AM]: OS: linux2, 32 cores, ~ 1057 GB RAM. Python: 2.7.14
[11:05 AM]: Running funannotate v1.3.4
[11:05 AM]: Augustus training set for fusarium_graminearum already exists. To re-train provide unique --augustus_species argument
[11:05 AM]: AUGUSTUS (3.3) detected, version seems to be compatible with BRAKER and BUSCO
[11:05 AM]: Loading sequences and soft-masking genome
[11:05 AM]: Soft-masking: building RepeatModeler database
[11:05 AM]: Soft-masking: generating repeat library using RepeatModeler
[12:39 PM]: Soft-masking: running RepeatMasker with custom library
[12:42 PM]: Masked genome: 19 scaffolds; 37,431,244 bp; 3.28% repeats masked
[12:43 PM]: Mapping proteins to genome using Diamond blastx/Exonerate
[12:43 PM]: Using 545,147 proteins as queries
[12:43 PM]: Running Diamond pre-filter search
[12:46 PM]: Found 328,356 preliminary alignments
[02:09 PM]: Exonerate finished: found 1,232 alignments
[02:09 PM]: Running GeneMark-ES on assembly
[02:29 PM]: Converting GeneMark GTF file to GFF3
[02:29 PM]: Found 11,196 gene models
[02:29 PM]: Running Augustus gene prediction
[02:33 PM]: Found 11,546 gene models
[02:33 PM]: Pulling out high quality Augustus predictions
[02:33 PM]: Found 122 high quality predictions from Augustus (>90% exon evidence)
[02:33 PM]: Summary of gene models passed to EVM (weights):
-------------------------------------------------------
Augustus models (1):    11,431
GeneMark models (1):    11,196
Hi-Q models (5):        122
PASA gene models (10):  0
Other gene models (1):  0
Total gene models:      22,749
-------------------------------------------------------
[02:33 PM]: Setting up EVM partitions
[02:34 PM]: Generating EVM command list
[02:34 PM]: Running EVM commands with 17 CPUs
[02:37 PM]: Combining partitioned EVM outputs
[02:37 PM]: Converting EVM output to GFF3
[02:37 PM]: Collecting all EVM results
[02:37 PM]: 11,903 total gene models from EVM
[02:37 PM]: Generating protein fasta files from 11,903 EVM models
[02:38 PM]: now filtering out bad gene models (< 50 aa in length, transposable elements, etc).
[02:38 PM]: Found 90 gene models to remove: 0 too short; 0 span gaps; 90 transposable elements
[02:38 PM]: 11,813 gene models remaining
[02:38 PM]: Predicting tRNAs
[02:39 PM]: Found 190 tRNA gene models
[02:39 PM]: 183 tRNAscan models are valid (non-overlapping)
[02:39 PM]: Generating GenBank tbl annotation file
[02:39 PM]: Converting to final Genbank format
[02:41 PM]: Collecting final annotation files for 11,996 total gene models
[02:41 PM]: Funannotate predict is finished, output files are in the Funannotate/predict180607/predict_results folder
[02:41 PM]: Your next step might be functional annotation, suggested commands:
-------------------------------------------------------
Run InterProSca

n (Docker required):
funannotate iprscan -i Funannotate/predict180607 -m docker -c 18

Run antiSMASH:
funannotate remote -i Funannotate/predict180607 -m antismash -e youremail@server.edu

Annotate Genome:
funannotate annotate -i Funannotate/predict180607 --cpus 18 --sbt yourSBTfile.txt

And the log from logfiles

[knunie@computerome01 logfiles]$ cat funannotate-predict.log
[06/07/18 11:05:05]: /services/tools/funannotate/1.3.4/bin/funannotate-predict.py -i Funannotate/UCPHDK1.ann.180607.fa -o Funannotate/predict180607 --species fusarium graminearum --cpus 18

[06/07/18 11:05:05]: OS: linux2, 32 cores, ~ 1057 GB RAM. Python: 2.7.14
[06/07/18 11:05:05]: Running funannotate v1.3.4
[06/07/18 11:05:09]: Augustus training set for fusarium_graminearum already exists. To re-train provide unique --augustus_species argument
[06/07/18 11:05:11]: AUGUSTUS (3.3) detected, version seems to be compatible with BRAKER and BUSCO
[06/07/18 11:05:11]: Loading sequences and soft-masking genome
[06/07/18 11:05:11]: Soft-masking: building RepeatModeler database
[06/07/18 11:05:14]: Soft-masking: generating repeat library using RepeatModeler
[06/07/18 12:39:04]: Soft-masking: running RepeatMasker with custom library
[06/07/18 12:42:48]: rmOutToGFF3.pl genome.fasta.out
[06/07/18 12:42:54]: Masked genome: 19 scaffolds; 37,431,244 bp; 3.28% repeats masked
[06/07/18 12:43:08]: Mapping proteins to genome using Diamond blastx/Exonerate
[06/07/18 14:09:45]: /services/tools/augustus/3.3/scripts/exonerate2hints.pl --in=Funannotate/predict180607/predict_misc/exonerate.out --out=Funannotate/predict180607/predict_misc/hints.P.gff --minintronlen=10 --maxintronlen=3000
[06/07/18 14:09:45]: perl /services/tools/augustus/3.3/scripts/join_mult_hints.pl
[06/07/18 14:09:46]: Running GeneMark-ES on assembly
[06/07/18 14:09:46]: gmes_petap.pl --ES --max_intron 3000 --soft_mask 5000 --cores 18 --sequence /home/projects/mg_guests/people/knunie/Funannotate/predict180607/predict_misc/genome.softmasked.fa --fungus
[06/07/18 14:29:44]: (None, '')
[06/07/18 14:29:44]: Converting GeneMark GTF file to GFF3
[06/07/18 14:29:45]: perl /services/tools/evm/1.1.1/EvmUtils/misc/augustus_GFF3_to_EVM_GFF3.pl Funannotate/predict180607/predict_misc/genemark.gff
[06/07/18 14:29:49]: Found 11,196 gene models
[06/07/18 14:29:49]: Running Augustus gene prediction
[06/07/18 14:33:39]: perl /services/tools/evm/1.1.1/EvmUtils/misc/augustus_GFF3_to_EVM_GFF3.pl Funannotate/predict180607/predict_misc/augustus.gff3
[06/07/18 14:33:42]: Pulling out high quality Augustus predictions
[06/07/18 14:33:43]: Found 122 high quality predictions from Augustus (>90% exon evidence)
[06/07/18 14:33:44]: Summary of gene models passed to EVM (weights):
-------------------------------------------------------
Augustus models (1):    11,431
GeneMark models (1):    11,196
Hi-Q models (5):        122
PASA gene models (10):  0
Other gene models (1):  0
Total gene models:      22,749
-------------------------------------------------------
[06/07/18 14:37:59]: 11,903 total gene models from EVM
[06/07/18 14:37:59]: Generating protein fasta files from 11,903 EVM models
[06/07/18 14:37:59]: /services/tools/evm/1.1.1/EvmUtils/gff3_file_to_proteins.pl /home/projects/mg_guests/people/knunie/Funannotate/predict180607/predict_misc/evm.round1.gff3 /home/projects/mg_guests/people/knunie/Funannotate/predict180607/predict_misc/genome.softmasked.fa
[06/07/18 14:38:10]: now filtering out bad gene models (< 50 aa in length, transposable elements, etc).
[06/07/18 14:38:10]: diamond blastp --sensitive --query Funannotate/predict180607/predict_misc/evm.round1.proteins.fa --threads 18 --out Funannotate/predict180607/predict_misc/repeats.xml --db /home/people/knunie/mg_guests/funannotate_db4/repeats.dmnd --evalue 1e-10 --max-target-seqs 1 --outfmt 5
[06/07/18 14:38:18]: diamond v0.8.22.84 | by Benjamin Buchfink <buchfink@gmail.com>
Check http://github.com/bbuchfink/diamond for updates.

#CPU threads: 18
Scoring parameters: (Matrix=BLOSUM62 Lambda=0.267 K=0.041 Penalties=11/1)
#Target sequences to report alignments for: 1
Temporary directory: Funannotate/predict180607/predict_misc
Opening the database...  [0.101384s]
Opening the input file...  [0.011088s]
Opening the output file...  [0.001715s]
Loading query sequences...  [0.166041s]
Building query histograms...  [0.070964s]
Allocating buffers...  [0.003384s]
Loading reference sequences...  [0.173377s]
Building reference histograms...  [0.095082s]
Allocating buffers...  [0.002393s]
Initializing temporary storage...  [0.324414s]
Processing query chunk 0, reference chunk 0, shape 0, index chunk 0.
Building reference index...  [0.024319s]
Building query index...  [0.014466s]
Building seed filter...  [0.00318s]
Searching alignments...  [0.063208s]
Processing query chunk 0, reference chunk 0, shape 0, index chunk 1.
Building reference index...  [0.0273s]
Building query index...  [0.01486s]
Building seed filter...  [0.003021s]
Searching alignments...  [0.064123s]
Processing query chunk 0, reference chunk 0, shape 0, index chunk 2.
Building reference index...  [0.029751s]
Building query index...  [0.017846s]
Building seed filter...  [0.002932s]
Searching alignments...  [0.055696s]
Processing query chunk 0, reference chunk 0, shape 0, index chunk 3.
Building reference index...  [0.022635s]
Building query index...  [0.015213s]
Building seed filter...  [0.002998s]
Searching alignments...  [0.063251s]
Processing query chunk 0, reference chunk 0, shape 1, index chunk 0.
Building reference index...  [0.022956s]
Building query index...  [0.013478s]
Building seed filter...  [0.003086s]
Searching alignments...  [0.061954s]
Processing query chunk 0, reference chunk 0, shape 1, index chunk 1.
Building reference index...  [0.026143s]
Building query index...  [0.015065s]
Building seed filter...  [0.003014s]
Searching alignments...  [0.062939s]
Processing query chunk 0, reference chunk 0, shape 1, index chunk 2.
Building reference index...  [0.025767s]
Building query index...  [0.015274s]
Building seed filter...  [0.002952s]
Searching alignments...  [0.067011s]
Processing query chunk 0, reference chunk 0, shape 1, index chunk 3.
Building reference index...  [0.022599s]
Building query index...  [0.015056s]
Building seed filter...  [0.002961s]
Searching alignments...  [0.060815s]
Processing query chunk 0, reference chunk 0, shape 2, index chunk 0.
Building reference index...  [0.02465s]
Building query index...  [0.013468s]
Building seed filter...  [0.003006s]
Searching alignments...  [0.06361s]
Processing query chunk 0, reference chunk 0, shape 2, index chunk 1.
Building reference index...  [0.026255s]
Building query index...  [0.014646s]
Building seed filter...  [0.00306s]
Searching alignments...  [0.064999s]
Processing query chunk 0, reference chunk 0, shape 2, index chunk 2.
Building reference index...  [0.029439s]
Building query index...  [0.015262s]
Building seed filter...  [0.002949s]
Searching alignments...  [0.060991s]
Processing query chunk 0, reference chunk 0, shape 2, index chunk 3.
Building reference index...  [0.02376s]
Building query index...  [0.013409s]
Building seed filter...  [0.003039s]
Searching alignments...  [0.0622s]
Processing query chunk 0, reference chunk 0, shape 3, index chunk 0.
Building reference index...  [0.025786s]
Building query index...  [0.013761s]
Building seed filter...  [0.003133s]
Searching alignments...  [0.058055s]
Processing query chunk 0, reference chunk 0, shape 3, index chunk 1.
Building reference index...  [0.024937s]
Building query index...  [0.014779s]
Building seed filter...  [0.003145s]
Searching alignments...  [0.062205s]
Processing query chunk 0, reference chunk 0, shape 3, index chunk 2.
Building reference index...  [0.026835s]
Building query index...  [0.015296s]
Building seed filter...  [0.002953s]
Searching alignments...  [0.05775s]
Processing query chunk 0, reference chunk 0, shape 3, index chunk 3.
Building reference index...  [0.022707s]
Building query index...  [0.013379s]
Building seed filter...  [0.003074s]
Searching alignments...  [0.061157s]
Processing query chunk 0, reference chunk 0, shape 4, index chunk 0.
Building reference index...  [0.024718s]
Building query index...  [0.013471s]
Building seed filter...  [0.003061s]
Searching alignments...  [0.05372s]
Processing query chunk 0, reference chunk 0, shape 4, index chunk 1.
Building reference index...  [0.028342s]
Building query index...  [0.014933s]
Building seed filter...  [0.003122s]
Searching alignments...  [0.065859s]
Processing query chunk 0, reference chunk 0, shape 4, index chunk 2.
Building reference index...  [0.027162s]
Building query index...  [0.015312s]
Building seed filter...  [0.002968s]
Searching alignments...  [0.060472s]
Processing query chunk 0, reference chunk 0, shape 4, index chunk 3.
Building reference index...  [0.024005s]
Building query index...  [0.015046s]
Building seed filter...  [0.003094s]
Searching alignments...  [0.061217s]
Processing query chunk 0, reference chunk 0, shape 5, index chunk 0.
Building reference index...  [0.022787s]
Building query index...  [0.013376s]
Building seed filter...  [0.003128s]
Searching alignments...  [0.060158s]
Processing query chunk 0, reference chunk 0, shape 5, index chunk 1.
Building reference index...  [0.02827s]
Building query index...  [0.014951s]
Building seed filter...  [0.002968s]
Searching alignments...  [0.060421s]
Processing query chunk 0, reference chunk 0, shape 5, index chunk 2.
Building reference index...  [0.026035s]
Building query index...  [0.016745s]
Building seed filter...  [0.003019s]
Searching alignments...  [0.05712s]
Processing query chunk 0, reference chunk 0, shape 5, index chunk 3.
Building reference index...  [0.024292s]
Building query index...  [0.013822s]
Building seed filter...  [0.003116s]
Searching alignments...  [0.059038s]
Processing query chunk 0, reference chunk 0, shape 6, index chunk 0.
Building reference index...  [0.022733s]
Building query index...  [0.013413s]
Building seed filter...  [0.003102s]
Searching alignments...  [0.061012s]
Processing query chunk 0, reference chunk 0, shape 6, index chunk 1.
Building reference index...  [0.029124s]
Building query index...  [0.015043s]
Building seed filter...  [0.003074s]
Searching alignments...  [0.062626s]
Processing query chunk 0, reference chunk 0, shape 6, index chunk 2.
Building reference index...  [0.025918s]
Building query index...  [0.015613s]
Building seed filter...  [0.002977s]
Searching alignments...  [0.064208s]
Processing query chunk 0, reference chunk 0, shape 6, index chunk 3.
Building reference index...  [0.023268s]
Building query index...  [0.013335s]
Building seed filter...  [0.003177s]
Searching alignments...  [0.061672s]
Processing query chunk 0, reference chunk 0, shape 7, index chunk 0.
Building reference index...  [0.023178s]
Building query index...  [0.01341s]
Building seed filter...  [0.003061s]
Searching alignments...  [0.057122s]
Processing query chunk 0, reference chunk 0, shape 7, index chunk 1.
Building reference index...  [0.028621s]
Building query index...  [0.016392s]
Building seed filter...  [0.003106s]
Searching alignments...  [0.059468s]
Processing query chunk 0, reference chunk 0, shape 7, index chunk 2.
Building reference index...  [0.02947s]
Building query index...  [0.015493s]
Building seed filter...  [0.003072s]
Searching alignments...  [0.072411s]
Processing query chunk 0, reference chunk 0, shape 7, index chunk 3.
Building reference index...  [0.022544s]
Building query index...  [0.013426s]
Building seed filter...  [0.003151s]
Searching alignments...  [0.05795s]
Processing query chunk 0, reference chunk 0, shape 8, index chunk 0.
Building reference index...  [0.022866s]
Building query index...  [0.013311s]
Building seed filter...  [0.00305s]
Searching alignments...  [0.056961s]
Processing query chunk 0, reference chunk 0, shape 8, index chunk 1.
Building reference index...  [0.024854s]
Building query index...  [0.01457s]
Building seed filter...  [0.003031s]
Searching alignments...  [0.059034s]
Processing query chunk 0, reference chunk 0, shape 8, index chunk 2.
Building reference index...  [0.026094s]
Building query index...  [0.01528s]
Building seed filter...  [0.002988s]
Searching alignments...  [0.059508s]
Processing query chunk 0, reference chunk 0, shape 8, index chunk 3.
Building reference index...  [0.023761s]
Building query index...  [0.015244s]
Building seed filter...  [0.002984s]
Searching alignments...  [0.060382s]
Processing query chunk 0, reference chunk 0, shape 9, index chunk 0.
Building reference index...  [0.022799s]
Building query index...  [0.013553s]
Building seed filter...  [0.00307s]
Searching alignments...  [0.056763s]
Processing query chunk 0, reference chunk 0, shape 9, index chunk 1.
Building reference index...  [0.02481s]
Building query index...  [0.014798s]
Building seed filter...  [0.003081s]
Searching alignments...  [0.056782s]
Processing query chunk 0, reference chunk 0, shape 9, index chunk 2.
Building reference index...  [0.025709s]
Building query index...  [0.015139s]
Building seed filter...  [0.003033s]
Searching alignments...  [0.058358s]
Processing query chunk 0, reference chunk 0, shape 9, index chunk 3.
Building reference index...  [0.022967s]
Building query index...  [0.013641s]
Building seed filter...  [0.002962s]
Searching alignments...  [0.05634s]
Processing query chunk 0, reference chunk 0, shape 10, index chunk 0.
Building reference index...  [0.022753s]
Building query index...  [0.013815s]
Building seed filter...  [0.003108s]
Searching alignments...  [0.064614s]
Processing query chunk 0, reference chunk 0, shape 10, index chunk 1.
Building reference index...  [0.028221s]
Building query index...  [0.014513s]
Building seed filter...  [0.003033s]
Searching alignments...  [0.057898s]
Processing query chunk 0, reference chunk 0, shape 10, index chunk 2.
Building reference index...  [0.029359s]
Building query index...  [0.015307s]
Building seed filter...  [0.003001s]
Searching alignments...  [0.053086s]
Processing query chunk 0, reference chunk 0, shape 10, index chunk 3.
Building reference index...  [0.022501s]
Building query index...  [0.013234s]
Building seed filter...  [0.003028s]
Searching alignments...  [0.058823s]
Processing query chunk 0, reference chunk 0, shape 11, index chunk 0.
Building reference index...  [0.025791s]
Building query index...  [0.015193s]
Building seed filter...  [0.003235s]
Searching alignments...  [0.055198s]
Processing query chunk 0, reference chunk 0, shape 11, index chunk 1.
Building reference index...  [0.028195s]
Building query index...  [0.014762s]
Building seed filter...  [0.003042s]
Searching alignments...  [0.058013s]
Processing query chunk 0, reference chunk 0, shape 11, index chunk 2.
Building reference index...  [0.027272s]
Building query index...  [0.016895s]
Building seed filter...  [0.002965s]
Searching alignments...  [0.061067s]
Processing query chunk 0, reference chunk 0, shape 11, index chunk 3.
Building reference index...  [0.022316s]
Building query index...  [0.014804s]
Building seed filter...  [0.002941s]
Searching alignments...  [0.054598s]
Processing query chunk 0, reference chunk 0, shape 12, index chunk 0.
Building reference index...  [0.022584s]
Building query index...  [0.014914s]
Building seed filter...  [0.003032s]
Searching alignments...  [0.053852s]
Processing query chunk 0, reference chunk 0, shape 12, index chunk 1.
Building reference index...  [0.027515s]
Building query index...  [0.014457s]
Building seed filter...  [0.002998s]
Searching alignments...  [0.052948s]
Processing query chunk 0, reference chunk 0, shape 12, index chunk 2.
Building reference index...  [0.027426s]
Building query index...  [0.015331s]
Building seed filter...  [0.002946s]
Searching alignments...  [0.056588s]
Processing query chunk 0, reference chunk 0, shape 12, index chunk 3.
Building reference index...  [0.02531s]
Building query index...  [0.014034s]
Building seed filter...  [0.003016s]
Searching alignments...  [0.059625s]
Processing query chunk 0, reference chunk 0, shape 13, index chunk 0.
Building reference index...  [0.022646s]
Building query index...  [0.01495s]
Building seed filter...  [0.003082s]
Searching alignments...  [0.062558s]
Processing query chunk 0, reference chunk 0, shape 13, index chunk 1.
Building reference index...  [0.024686s]
Building query index...  [0.014389s]
Building seed filter...  [0.003013s]
Searching alignments...  [0.056501s]
Processing query chunk 0, reference chunk 0, shape 13, index chunk 2.
Building reference index...  [0.025657s]
Building query index...  [0.016725s]
Building seed filter...  [0.003075s]
Searching alignments...  [0.054498s]
Processing query chunk 0, reference chunk 0, shape 13, index chunk 3.
Building reference index...  [0.022365s]
Building query index...  [0.013427s]
Building seed filter...  [0.002935s]
Searching alignments...  [0.057227s]
Processing query chunk 0, reference chunk 0, shape 14, index chunk 0.
Building reference index...  [0.022601s]
Building query index...  [0.014814s]
Building seed filter...  [0.003044s]
Searching alignments...  [0.055229s]
Processing query chunk 0, reference chunk 0, shape 14, index chunk 1.
Building reference index...  [0.028024s]
Building query index...  [0.015538s]
Building seed filter...  [0.003006s]
Searching alignments...  [0.056543s]
Processing query chunk 0, reference chunk 0, shape 14, index chunk 2.
Building reference index...  [0.027668s]
Building query index...  [0.017303s]
Building seed filter...  [0.002981s]
Searching alignments...  [0.061617s]
Processing query chunk 0, reference chunk 0, shape 14, index chunk 3.
Building reference index...  [0.025316s]
Building query index...  [0.014668s]
Building seed filter...  [0.002961s]
Searching alignments...  [0.054122s]
Processing query chunk 0, reference chunk 0, shape 15, index chunk 0.
Building reference index...  [0.025675s]
Building query index...  [0.013167s]
Building seed filter...  [0.003111s]
Searching alignments...  [0.061935s]
Processing query chunk 0, reference chunk 0, shape 15, index chunk 1.
Building reference index...  [0.028031s]
Building query index...  [0.014628s]
Building seed filter...  [0.003038s]
Searching alignments...  [0.057501s]
Processing query chunk 0, reference chunk 0, shape 15, index chunk 2.
Building reference index...  [0.025569s]
Building query index...  [0.014899s]
Building seed filter...  [0.002915s]
Searching alignments...  [0.058764s]
Processing query chunk 0, reference chunk 0, shape 15, index chunk 3.
Building reference index...  [0.022473s]
Building query index...  [0.014791s]
Building seed filter...  [0.00302s]
Searching alignments...  [0.05842s]
Deallocating buffers...  [0.000407s]
Computing alignments...  [0.533963s]
Deallocating reference...  [0.000485s]
Loading reference sequences...  [0.000283s]
Deallocating buffers...  [0.000112s]
Deallocating queries...  [3.9e-05s]
Loading query sequences...  [9e-06s]
Closing the output file...  [0.003574s]
Closing the database file...  [5e-06s]
Total time = 8.06617s
Reported 90 pairwise alignments, 253 HSSPs.
90 queries aligned.

[06/07/18 14:38:18]: bedtools intersect -f 0.9 -a /home/projects/mg_guests/people/knunie/Funannotate/predict180607/predict_misc/evm.round1.gff3 -b /home/projects/mg_guests/people/knunie/Funannotate/predict180607/predict_misc/repeatmasker.gff3
[06/07/18 14:38:19]: Found 90 gene models to remove: 0 too short; 0 span gaps; 90 transposable elements
[06/07/18 14:38:19]: 11,813 gene models remaining
[06/07/18 14:38:19]: Predicting tRNAs
[06/07/18 14:38:19]: tRNAscan-SE -o Funannotate/predict180607/predict_misc/tRNAscan.out /home/projects/mg_guests/people/knunie/Funannotate/predict180607/predict_misc/genome.softmasked.fa
[06/07/18 14:39:33]:
tRNAscan-SE v.1.3.1 (January 2012) - scan sequences for transfer RNAs

  Please cite:
        Lowe, T.M. & Eddy, S.R. (1997) "tRNAscan-SE: A program for
        improved detection of transfer RNA genes in genomic sequence"
        Nucl. Acids Res. 25: 955-964.

  This program uses a modified, optimized version of tRNAscan v1.3
  (Fichant & Burks, J. Mol. Biol. 1991, 220: 659-671),
  a new implementation of a multistep weight matrix algorithm
  for identification of eukaryotic tRNA promoter regions
  (Pavesi et al., Nucl. Acids Res. 1994, 22: 1247-1256),
  as well as the RNA covariance analysis package Cove v.2.4.2
  (Eddy & Durbin, Nucl. Acids Res. 1994, 22: 2079-2088).

------------------------------------------------------------
Sequence file(s) to search:  /home/projects/mg_guests/people/knunie/Funannotate/predict180607/predict_misc/genome.softmasked.fa
Search Mode:                 Eukaryotic
Results written to:          Funannotate/predict180607/predict_misc/tRNAscan.out
Output format:               Tabular
Searching with:              tRNAscan + EufindtRNA -> Cove
Covariance model:            TRNA2-euk.cm
tRNAscan parameters:         Strict
EufindtRNA parameters:       Relaxed (Int Cutoff= -32.1)
Search log saved in:
------------------------------------------------------------

[06/07/18 14:39:33]: Found 190 tRNA gene models
[06/07/18 14:39:33]: bedtools intersect -v -a Funannotate/predict180607/predict_misc/trnascan.gff3 -b Funannotate/predict180607/predict_misc/evm.cleaned.gff3
[06/07/18 14:39:33]: 183 tRNAscan models are valid (non-overlapping)
[06/07/18 14:39:33]: Generating GenBank tbl annotation file
[06/07/18 14:39:35]: Converting to final Genbank format
[06/07/18 14:39:35]: tbl2asn -y "Annotated using funannotate v1.3.4" -N 1 -p Funannotate/predict180607/predict_misc/tbl2asn -t /services/tools/funannotate/1.3.4/lib/test.sbt -M n -Z Funannotate/predict180607/predict_results/fusarium_graminearum.discrepency.report.txt -j "[organism=fusarium graminearum]" -V b -c fx -T -a r10u -l paired-ends
[06/07/18 14:40:55]: [tbl2asn] This copy of tbl2asn is more than a year old.  Please download the current version.
[tbl2asn] Flatfile genome

[tbl2asn] Validating genome

[06/07/18 14:41:08]: Collecting final annotation files for 11,996 total gene models
[06/07/18 14:41:08]: Funannotate predict is finished, output files are in the Funannotate/predict180607/predict_results folder
[06/07/18 14:41:08]: Your next step might be functional annotation, suggested commands:
-------------------------------------------------------
Run InterProScan (Docker required):
funannotate iprscan -i Funannotate/predict180607 -m docker -c 18

Run antiSMASH:
funannotate remote -i Funannotate/predict180607 -m antismash -e youremail@server.edu

Annotate Genome:
funannotate annotate -i Funannotate/predict180607 --cpus 18 --sbt yourSBTfile.txt
-------------------------------------------------------
nextgenusfs commented 6 years ago

Doesn't look like an error to me -- strange that those two commands would be pushed to stderr -- they are just part of the logging method which should be printing to file and stdout (like the rest of the data). Glad you got it working!