[maker-devel] Some questions regarding ab-initio training

Thu Jun 5 02:15:55 MDT 2014

Hi,

thanks for the feedback. I spent some more time on this and am still somewhat unsatisfied with the whole thing…

A few comments:

I quite frequently see augustus and in extension Maker including exons that are not supported by EST/Protein evidence and are not critical for the gene model (i.e. I can take them out and still get a proper CDS). Maybe I don’t know enough about how Maker creates hints and more importantly what role these hints play for augustus, but I cannot really see a great effect (any, really) on the gene models even if both ESTs and proteins contradict an augustus gene model and the surplus exon is non-essential. 

(all evidence is provided as fasta files, protein2genome and est2genome are set to 0)

As for the repeat library, I suppose this is a critical point. I am using repeats from a closely related species via Repeatmasker, modelled and filtered repeats from RepeatModeler and repeats derived from a high-coverage 454 data set. Not sure what else I can do to improve that.

As for evidence, I am using the curated reference proteome from a related species (<5 Mio years), unprot_swissprot and high-quality ESTs + 454 reads. I don’t think it gets a whole lot better, in terms of what data can be used.

So in summary, I just don’t get where I want to using Augustus and Maker - specifically, the gene models are full of weird, unsupported artefacts despite manually curating > 850 models for training. I suppose I was hoping for some secret trick to improve on this - but I guess there is none? Actually, if I only do a pure evidence build (seeing that my input data is very high quality), it looks better - which sort of goes against the premise of Maker :/

Regards,

Marc

Marc P. Hoeppner, PhD
Team Leader
Department for Medical Biochemistry and Microbiology
Uppsala University, Sweden
marc.hoeppner at bils.se

On 27 May 2014, at 17:25, Carson Holt <carsonhh at gmail.com> wrote:

> Extra exons can be required for predictors to make sense of a region (they
> do the best they can).  This can be due to imperfect assemblies or
> repeats.  For plants the repeat database is the the one thing that will
> most affect the annotation quality.  You may need to spend some time
> building the best repeat library you can.  The repeat library is the next
> most important thing next to training the predictor, because they confuse
> the predictor (sometimes a lot) causing it to behave oddly in those
> regions (because repeats do encode real protein and protein fragments).
> Also when running now with MAKER make sure to include the entire proteome
> of a related species and not just UniProt, and you will get better
> performance.  Now that you have Augustus trained, using it inside of MAKER
> with an improved repeat library and additional protein evidence should
> give it the feedback that will allow it to perform better than it would
> with just naked ab initio prediction.
> 
> Thanks,
> Carson
> 
> 
> On 5/27/14, 2:12 AM, "Marc Höppner" <marc.hoeppner at bils.se> wrote:
> 
>> Hi,
>> 
>> I wanted to get some feedback regarding the training of ab-initio gene
>> finders - it’s not strictly Maker related, but I suppose there are many
>> people on this list that have encountered and solved this issue in one
>> way or another.
>> 
>> Specifically, I am trying to train Augustus (and possibly SNAP) for a
>> plant genome. This has always been a very frustrating process for me, but
>> while I have a better idea now how to do it, I still don’t get the sort
>> of accuracy that I am hoping for. A quick run-through of my process;
>> 
>> Evidence build with maker on level 1 and 2 proteins from Uniprot +
>> Sanger-sequenced EST data
>> 
>> Filtered for Models with an AED <= 0.3
>> 
>> Loaded that into WebApollo, together with an existing reference
>> annotation and the evidence tracks
>> 
>> Manually curated/selected 750 gene models using the following rules:
>> - Must have start/stop codon
>> - Most have as many exons as possible
>> - Must agree with evidence
>> - Must be >= 2kb part from other gene models (provided as flanking
>> regions for augustus to train intergenic sequence)
>> 
>> From these models, I created  a GBK file, split it into 650 (train) and
>> 100 (test) models and created a new profile using the documented
>> procedure.
>> 
>> But:
>> 
>> While the naked ab-init models created through maker get a lot of genes
>> ‘sort of right’, I still see too many issues to be really satisfied.
>> Problems include:
>> 
>> - random exon calls which are not supported by any line of evidence (~1
>> per gene model, I would guess)
>> - poor congruency with some gene models (especially ones not used for
>> training/testing)
>> 
>> Is there any best-practice guide on how to improve this? The Augustus
>> website is unfortunately quite poor on detail… My impression so far is
>> that ramping up the number of training models isn’t really doing too much
>> beyond a certain point (tried 400, 500 and 750).
>> 
>> Regards,
>> 
>> Marc
>> 
>> 
>> Marc P. Hoeppner, PhD
>> Team Leader
>> BILS Genome Annotation Platform
>> Department for Medical Biochemistry and Microbiology
>> Uppsala University, Sweden
>> marc.hoeppner at bils.se
>> 
>> 
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> 
> 
> 
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