[maker-devel] Curious pattern in AED distributions

[Lior Glick]

Hello again and thank you all for your interesting answers.
I mistakenly answered Mark yesterday from an unsubscribed mail, which
resulted in only him getting it, so for documentation sake, I'm posting my
answer here again, and Mark's reply:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Dear Mark,
Thank you for the quick reply. I'm happy to see this ignites your interest
and am willing to endure your punishing questions (;
Before I answer them, I just want to make sure we're on the same page - as
far as I understand, lower AED scores indicate higher agreement with the
evidence, so the "good stuff" is actually left of the 0.5 surge. Am I
correct? Otherwise, this is a very poor annotation...
Now for the questions:
1) I did not make any filtrations so far, so single exon genes are included
as well. in fact, I'm exploring the results in order to develop some
criteria for filtering the genes. Would you suggest discarding single exon
genes?

2) My evidence consist of assembled transcripts, proteins and predicted
gene models (pred_gff).

3) As for repeats, I'm masking based on a repeats library obtained from a
previous publication, specific to my organism of interest.

Unfortunately, I didn't understand your final question. Could you please
explain what you mean by "final build"?

Hope these answers are helpful, and waiting to hear more thoughts.

Thanks again.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
*To which Mark replied:*

Sorry. I’m dyslexic, especially early in the morning. Yes, good stuff is on
the left. As regards single exon genes, that’s always a hard call, as these
have a higher false positive rate. Things to consider are how prevalent are
introns in your org? Cason can give more advice on this point, I’m sure.

·

·         By ‘"final build", I meant is this using the ‘Standard build’  or
‘Max Build’ protocol from PMC4286374?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 Mark - well, as I said I haven't done any filtration yet, so I guess my
annotation currently includes genes that would be discarded even with the
"max build". I'll give this a try and look at the resulting distribution.

Xabier - thanks, but I'm not using SNAP (just Augustus).

Carson - I see a few fingers pointing in the direction of single-exon
models, so maybe I should see what happens to the distribution of AED when
these genes are removed.

I'll get back to you with some more results.

‫בתאריך יום ב׳, 8 באפר׳ 2019 ב-8:20 מאת ‪Carson Holt‬‏ <‪carsonhh at gmail.com
‬‏>:‬

> Yes. maker2zff tries to further select a subset of the best supported
> models by requiring multiple forms of evidence support.
>
> —Carson
>
>
> On Apr 7, 2019, at 10:42 PM, Xabier Vázquez-Campos <xvazquezc at gmail.com>
> wrote:
>
> If you train SNAP, the maker2zff script has internal quality cutoffs based
> on the existence of evidence. e.g. by default it will require having some
> EST evidence
>
> On Mon, 8 Apr 2019 at 11:32, Carson Holt <carsonhh at gmail.com> wrote:
>
>> That’s interesting. It could be a handful of internal filters that help
>> with spurious results.
>>
>> I use a 0.5 sensitivity/specificity to identify shared edges for a
>> jaccardian split on overlapping evidence clusters for example. There are
>> also a couple of places where if the only thing supporting a model is a
>> single exon blastx hit (i.e. no exonerate, ab initio model, or est splice
>> support, but just a chunk od single exon blastx) then maker will use a
>> reading frame aware AED value of 0.5 as a filter (as in it checks if the
>> reading frame matches and not just raw overlap). If that’s the case, the
>> spike near 0.5 may indicate I needed to be a little strickter than my
>> empirical cutoff estimate. Perhaps 0.4 or 0.45 would be the better cuttoff
>> for these spurious blastx induced models.
>>
>> —Carson
>>
>>
>> > On Apr 7, 2019, at 7:25 AM, Lior Glick <liorglic at mail.tau.ac.il> wrote:
>> >
>> > Hi MAKER users,
>> > Lately I've been performing annotations for multiple genomes from the
>> same species.
>> > When plotting the histogram of AED scores over all genes, I repeatedly
>> see a very specific pattern, that looks something like this:
>> > <AED_hist.png>
>> > This pattern is a bit surprising to me, in two aspects:
>> > 1) Why is there a surge towards 0.5?
>> > 2) Why is there a sudden drop right after that surge?
>> >
>> > Has anyone else seen this, or is this a specific outcome of my
>> data/configuration?
>> > Any ideas of what may cause such a distribution?
>> >
>> > While this is not necessarily an indication of a problem or bug, it
>> does seem a bit odd, and  might imply some bias or artifact.
>> > Would appreciate your comments.
>> > Thank you!
>> > _______________________________________________
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>> > maker-devel at box290.bluehost.com
>> > http://box290.bluehost.com/mailman/listinfo/maker-devel_yandell-lab.org
>>
>>
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>>
>
>
> --
> Xabier Vázquez-Campos, *PhD*
> *Research Associate*
> NSW Systems Biology Initiative
> School of Biotechnology and Biomolecular Sciences
> The University of New South Wales
> Sydney NSW 2052 AUSTRALIA
>
>
>
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