[maker-devel] Help with updating an annotation

Fri Jun 20 15:42:13 MDT 2014

"I have to specify an ab initio gene model for any locus that I wish to
annotate using evidence alignment (i.e. there must be a preexisting model)?"

Not exactly.  You need to supply an HMM for SNAP or species file for
Augusutus, etc.  MAKER doesn't generate gene predictions, SNAP does. You
cannot get updated models unless you've provided a way for those models to
be updated.  MAKER will provide SNAP/Augustus with hints to make them
perform better based on the evidence, but those hints won't even be
genertated and the programs won't even run unless you provide the HMM.  Also
if you provide models in gff3 format to pred_gff, there is not hint feedback
(because there is no program to receive the hints - just an immutable GFF3
file).

If you don't have an HMM for SNAP for your organism, you can generate one
using the documentation here (from GMOD 2014 tutorial) -->
http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/MAKER_Tutorial_for_G
MOD_Online_Training_2014#Training_ab_initio_Gene_Predictors

--Carson

From:  Saad Arif <saad.arif at tuebingen.mpg.de>
Date:  Wednesday, June 18, 2014 at 10:42 AM
To:  Daniel Ence <dence at genetics.utah.edu>
Cc:  "<maker-devel at yandell-lab.org>" <maker-devel at yandell-lab.org>
Subject:  Re: [maker-devel] Help with updating an annotation

Thanks Daniel. I think it's more clear to me now.

So If I understand correctly now: I have to specify an ab initio gene model
for any locus that I wish to annotate using evidence alignment (i.e. there
must be a preexisting model)? These ab initio gene models can be trained
internally in Maker with SNAP using my cufflinks output as EST
evidence.Alternatively, I can provide alternative ab inito predictions (for
regions not present in my ensembl ref passed to model_GFF) for regions
overlapping my cufflinks output via the pred_GFF option?

Since i'm interested in unannotated regions, i'm also passing in reference
proteomes of closely related species as protein homology evidence.

As such i should be able to keep, only evidence supported predictions (for
regions not present in my model_GFF and or better supported models for
present regions) from my pred_GFF and merge them with Ensembl annotations
from the model_GFF?

Let me know if i'm still missing something here.

Thanks in advance.

best,
Saad
On 18 Jun 2014, at 17:21, Daniel Ence wrote:

> Hi Saad, 
> 
> Maker doesn't view EST or protein evidence as a gene model in themselves.
> There's a good reason for this. Aligners like blast  don't guarantee complete
> gene models, with accurate start and stop codons and splice sites. With it's
> default settings maker won't make a gene model unless there's evidence that
> overlaps an ab-initio prediction (or something from the pred_gff option).
> 
> You can use est2genome to promote everything from the est_gff option to a gene
> model, but this will probably give you many spurious results. What you're
> saying with est2genome is, "Everything that this tool found is a complete gene
> model." I don't think that's true even for cufflinks output.
> 
> One of the gene predictors that can run internally is snap. It's really easy
> to train; here's a link to a tutorial for training it:
> http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/MAKER_Tutorial_for_GMO
> D_Online_Training_2014#Training_ab_initio_Gene_Predictors
> 
> Let me know if that helps, or if you have more question
> 
> 
> ~Daniel
> 
> Daniel Ence
> Graduate Student
> dence at genetics.utah.edu
> Eccles Institute of Human Genetics
> University of Utah
> 15 North 2030 East, Room 2100
> Salt Lake City, UT 84112-5330
> 
> On Jun 18, 2014, at 5:09 AM, Saad Arif <saad.arif at tuebingen.mpg.de>
>  wrote:
> 
>> Thank you for the response. I still have one question though, with these
>> options:
>> 
>> est_GFF=cufflinksout.GFF
>> 
>> modle_GFF= ensembl reference.GFF
>> 
>> What happens to cufflinks assembled transcripts that are not confined to
>> current gene loci (i.e. novel genes in cufflinks ouput)? Would i have to
>> prepare ab initio gene predictions for each of these predicted 'new' genes?
>> Is there a simple way to combine adding (new genes) and improving of an
>> existing annotation?
>> 
>> Any feedback on this would be greatly appreciated.
>> 
>> saad
>> 
>> On 13 Jun 2014, at 17:59, Carson Holt wrote:
>> 
>>> Use the cufflinks instead of the tophat features (tophat tends to be
>>> really noisy).  Give the existing models to model_gff (they will then
>>> always be kept unless something better is found).  There is no option to
>>> keep models and then just add isoforms.  The model_gff input will either
>>> be kept as is (unchanged), or replaced with an updated model suggested by
>>> the evidence (the updated model may contain multiple isoforms though), and
>>> map_forward=1 can be used to pull names forward from the old model onto
>>> the new models.
>>> 
>>> Thansk,
>>> Carson
>>> 
>>> 
>>> On 6/13/14, 5:03 AM, "Saad Arif" <saad.arif at tuebingen.mpg.de> wrote:
>>> 
>>>> Dear All,
>>>> 
>>>> I would like to use Maker pipeline  to expand a current annotation (new
>>>> isoforms and novel genes with respect to current annotation) and was
>>>> wondering if anyone had experience with this and or suggestions to my
>>>> questions.
>>>> 
>>>> Briefly:
>>>> 
>>>> I have tophat splice junctions from RNAseq data or alternatively
>>>> cufflinks generated transcript models (fasts format) that i want to use
>>>> as my new data (est_gff or est).
>>>> 
>>>> I want to provide the current Ensembl annotation for gene prediction but
>>>> i want this annotation to remain unchanged. Hence, i’m not sure if i
>>>> should provide this annotation as pred_gff
>>>> or model_gff. Can the model_gff be used for gene prediction or is this
>>>> just a subset of pred_gff that remain unaltered? Can we provide the same
>>>> annotation for both options (pred_ and mod_gff)?
>>>> 
>>>> 
>>>> 
>>>> Importantly, my main goal is to use the new RNAseq data to add more
>>>> isoforms and (any) novel genes to the existing Ensembl annotation. Any
>>>> thoughts or suggestions on how to go about  this would be  sincerely
>>>> appreciated.
>>>> 
>>>> 
>>>> Thanks in advance,
>>>> saad
>>>> 
>>>> 
>>>> 
>>>> 
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>>> 
>>> 
>> 
>> 
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