[maker-devel] Some errors reported by Maker2

Mon Sep 11 12:27:22 CDT 2017

Dear Carson:

Would you please explain what do you mean by "a single machine"? I am
running maker2 on our high performance cluster. The cluster has more than
1,620-core compute nodes with 128 GB RAM each. Univa Grid Engine was used
as the scheduler. Can I use MPICH3?

Thanks

Best
Quanwei

2017-09-11 13:18 GMT-04:00 Carson Holt <carsonhh at gmail.com>:

> If you are just using a single machine (and not cross machine MPI), use
> MPICH3 —> https://www.mpich.org
>
> It’s easy to install yourself, and tends to be very robust to failure.
>
> —Carson
>
>
>
> On Sep 11, 2017, at 11:16 AM, Quanwei Zhang <qwzhang0601 at gmail.com> wrote:
>
> 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-a
>>>>> llocate-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
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://box290.bluehost.com/pipermail/maker-devel_yandell-lab.org/attachments/20170911/6fd07594/attachment.html>