[maker-devel] Some errors reported by Maker2

Mon Sep 11 11:16:49 MDT 2017

Dear Carson:

I met some problems to use MPI. I will give it another try.
Thank you!

Best
Quanwei

2017-09-11 13:14 GMT-04:00 Carson Holt <carsonhh at gmail.com>:

> It could be either. Please use MPI instead of starting multiple instances.
> It will greatly reduce both IO and RAM usage.
>
> —Carson
>
>
>
> On Sep 11, 2017, at 11:12 AM, Quanwei Zhang <qwzhang0601 at gmail.com> wrote:
>
> Dear Carson:
>
> I only run 5 Maker instances in each directory (and set cpus=2). If it is
> related to memory issue or an IO issue, I am not sure why the much longer
> scaffolds (than the failed ones) were all annotated successfully, but the
> relatively shorter ones failed.
>
> I have set "tries=5" (#number of times to try a contig if there is a
> failure for some reason). I will try "clean_try=1" and test on the failed
> scaffolds individually with larger memory to see whether they can be
> annotated.
>
> Thank you!
>
> Best
> Quanwei
>
> 2017-09-11 13:07 GMT-04:00 Carson Holt <carsonhh at gmail.com>:
>
>> I think the cause of the error may have been a little further upstream
>> from what you pasted in the e-mail. One thing that may be happening is that
>> you are taxing resources (like IO) if running MAKER multiple times or on
>> too many CPUs. That can lead to failures because of truncated BLAST reports
>> etc. In which case you can just retry and that will get around those types
>> of IO derived errors. MAKER can generate a lot of IO, and if you are
>> working on network mounted locations (i.e. the storage being used is
>> actually across the network), then they can be lest robust than local
>> storage (when under heavy load NFS can falsely report success on read/write
>> operations that actually failed). It’s the reason we built in the retry
>> capabilities of MAKER.
>>
>> For contigs that continuously fail, you may need to set clean_try=1. That
>> will cause failures to start from scratch (i.e. delete all old reports on
>> failure rather than just those suspected of being truncated).
>>
>> —Carson
>>
>>
>>
>> On Sep 11, 2017, at 10:19 AM, Quanwei Zhang <qwzhang0601 at gmail.com>
>> wrote:
>>
>> Dear Carson:
>>
>> About the error in my above email, I found the contig was correctly
>> annotated at the second time RETRY. So please ignore my last email. But
>> now, for a few number of scaffolds, I met problems to process the repeats
>> (as shown below in red). I used both Mammalia repeat library and species
>> specific repeat library (which is generated by your pipeline "
>> http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/Rep
>> eat_Library_Construction--Basic"). There were no such problems when I
>> only used Mammalia repeat library. Do you have any ideas about this? What
>> could be the reason? Or do you have any suggestions for me to find the
>> reason? Many thanks
>>
>> Here are some parameters I used
>>
>> #-----Repeat Masking (leave values blank to skip repeat masking)
>> model_org=Mammalia #select a model organism for RepBase masking in
>> RepeatMasker
>> rmlib=../consensi.fa.classifiednoProtFinal #provide an organism specific
>> repeat library in fasta format for Repe
>>
>> max_dna_len=300000
>> split_hit=40000
>> depth_blastn=30 #Blastn depth cutoff (0 to disable cutoff)
>> depth_blastx=30 #Blastx depth cutoff (0 to disable cutoff)
>> depth_tblastx=30 #tBlastx depth cutoff (0 to disable cutoff)
>> bit_rm_blastx=30 #Blastx bit cutoff for transposable element masking
>>
>>
>> Died at /gs/gsfs0/hpc01/apps/MAKER/2.31.9/bin/../lib/Bio/Search/Hit/PhatHit/Base.pm
>> line 188.
>> 33708 --> rank=NA, hostname=n409
>> 33709 ERROR: Failed while processing all repeats
>> 33710 ERROR: Chunk failed at level:3, tier_type:1
>> 33711 FAILED CONTIG:Contig31
>>
>>
>> Best
>> Quanwei
>>
>> 2017-09-08 23:25 GMT-04:00 Quanwei Zhang <qwzhang0601 at gmail.com>:
>>
>>> Dear Carson:
>>>
>>> I got the following error again. Is this still related to memory issues?
>>> I wonder whether there can be other reasons lead to this error? This time,
>>> I got this error during training of the SNAP model. Before, even I set
>>> max_dna_len=1Mb, I can train the model successfully.  And in the current
>>> training (where I get the following error),  I have decreased the
>>> max_dna_len to 300kb. I required the same amount memory as before. The only
>>> difference is that I am using both mammalian repeat library and species
>>> specific repeat library, while previously I only use the mammalian repeat
>>> library. Will it greatly increases the requirement of memory to use both
>>> repeat libraries (even when I decrease max_dna_len from 1Mb to 300kb)? I
>>> have also set the depth_blast as 30 in current training.
>>>
>>> Thank you! Have a nice weekend!
>>>
>>>
>>>
>>> #---------------------------------------------------------------------
>>> Now starting the contig!!
>>> SeqID: Contig10
>>> Length: 18773588
>>> #---------------------------------------------------------------------
>>>
>>>
>>> setting up GFF3 output and fasta chunks
>>> doing repeat masking
>>> doing blastx repeats
>>> doing blastx repeats
>>> doing blastx repeats
>>> doing blastx repeats
>>> doing blastx repeats
>>> doing blastx repeats
>>> doing blastx repeats
>>> doing blastx repeats
>>> doing blastx repeats
>>> doing blastx repeats
>>> collecting blastx repeatmasking
>>> processing all repeats
>>> doing repeat masking
>>> Can't kill a non-numeric process ID at /gs/gsfs0/hpc01/apps/MAKER/2.31.9/bin/../lib/File/NFSLock.pm
>>> line 1050.
>>> --> rank=NA, hostname=n224
>>> ERROR: Failed while doing repeat masking
>>> ERROR: Chunk failed at level:0, tier_type:1
>>> FAILED CONTIG:Contig10
>>>
>>> ERROR: Chunk failed at level:2, tier_type:0
>>> FAILED CONTIG:Contig10
>>>
>>> Best
>>> Quanwei
>>>
>>> 2017-09-06 12:06 GMT-04:00 Carson Holt <carsonhh at gmail.com>:
>>>
>>>>
>>>> (2) By reading some of your replies in the maker google group, and I
>>>> noticed that it can reduce memory and save time for annotation if I set
>>>> depth_blast to a certain number. So I changed the following parameters. But
>>>> I wonder, whether it will decrease the quality of annotation? If it won't
>>>> affect the quality, can I even use a smaller number (e.g., 20) to save more
>>>> memory and time?
>>>>
>>>> depth_blastn=30 #Blastn depth cutoff (0 to disable cutoff)
>>>> depth_blastx=30 #Blastx depth cutoff (0 to disable cutoff)
>>>> depth_tblastx=30 #tBlastx depth cutoff (0 to disable cutoff)
>>>> bit_rm_blastx=30 #Blastx bit cutoff for transposable element masking
>>>>
>>>>
>>>> This values really only affects the final evidence kept in the GFF3
>>>> when you look at it in a browser. It has not affect on the annotation. This
>>>> is because internally MAKER already collapses evidence down to the 10 best
>>>> non-redundant features per evidence set per locus. The rest are put in the
>>>> GFF3 just for reference. by setting it lower, you are just letting MAKER
>>>> know it can through things away even sooner since you don’t want them in
>>>> the GFF3. It provides a minor improvement for memory use, but
>>>> max_dna_length is the big one that has the greatest effect.
>>>>
>>>>
>>>> (3) I also have some concerns about the speed, especially for the long
>>>> scaffolds (around 100Mb). I wonder which part is the most time consuming
>>>> for genome annotation (repeat masking, blast, or polishing?).
>>>> Particularly, I wonder whether the blastx of protein evidence will take
>>>> majority of time. Now, I have prepared 99k mammalian Swiss protein
>>>> sequences and 340k rodent TrEMBL protein sequences as protein evidences. I
>>>> am considering whether I can save much time if I only use the 99k mammalian
>>>> Swiss protein sequences as evidences.
>>>>
>>>>
>>>> BLASTN (ESTs) -> fastest as it is searching nucleotide space
>>>> BLASTX (proteins) -> must search 6 reading frames so will be at least 6
>>>> times slower than BLASTN
>>>> TBLASTX (alt-ESTs) -> must search 12 reading frames so will be at least
>>>> 12 times slower than BLASTN and twice as slow as BLASTX
>>>>
>>>> Also double the dataset size, double the runtime. Larger window sizes
>>>> via max_dna_length will also increase runtimes.
>>>>
>>>>
>>>> (4) For some reasons, I can not run maker though MPI on our cluster. So
>>>> I can only start multiple maker. I wonder if it is possible to let multiple
>>>> maker to annotate the same long scaffold (i.e., for a single sequence I
>>>> start multiple maker, without splitting the long sequence into shorter
>>>> ones).
>>>>
>>>>
>>>> Without MPI you won’t be able to split up large contigs. At the very
>>>> least you can try and run on a single node and set MPI to use all CPUs on
>>>> that node. It’s less difficult to set up compared to cross node jobs via
>>>> MPI.
>>>>
>>>>
>>>> (5) Still about the speed issue. I read some of your comments about
>>>> "cpus" parameters in the maker_opts file (http://gmod.827538.n3.nabble.
>>>> com/open3-fork-failed-Cannot-allocate-memory-td4025117.html). And I
>>>> know it indicate the number of cpus for a single chunk. So if I set
>>>> "cpus=2" in the maker_opts file, then I can use the following command to
>>>> submit the job, right?
>>>>
>>>>
>>>> The cpu parameter only affects how many CPUs are given to the blast
>>>> command line. So only the BLASt step will speed up, so I recommend using
>>>> MPI to get all steps to speed up. Even if you are only running on a single
>>>> node, you can give all CPUs to the mpiexec command.
>>>>
>>>>
>>>> —Carson
>>>>
>>>
>>>
>>
>>
>
>
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