[maker-devel] maker-devel Digest, Vol 77, Issue 4
Doyle, Jacqueline R M
jmdoyle at purdue.edu
Thu Oct 2 13:32:53 MDT 2014
Hi Carson! If you have them readily available, what are the citations for the honeybee genome manuscripts you referenced (below)? I imagine one is the 2006 Nature paper and one something more recent?
Thanks! Jackie
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Today's Topics:
1. Re: diff. numbers of geneson contigs vs. scaffolded genome
(Carson Holt)
----------------------------------------------------------------------
Message: 1
Date: Wed, 1 Oct 2014 19:18:43 +0000
From: Carson Holt <carson.holt at genetics.utah.edu>
To: "stefan.zoller at env.ethz.ch" <stefan.zoller at env.ethz.ch>,
"maker-devel at yandell-lab.org" <maker-devel at yandell-lab.org>, Mark
Yandell <myandell at genetics.utah.edu>
Subject: Re: [maker-devel] diff. numbers of geneson contigs vs.
scaffolded genome
Message-ID: <D051AC10.FDC2%carson.holt at genetics.utah.edu>
Content-Type: text/plain; charset="utf-8"
--> Should I filter them by e-value or some other parameter before
promoting them to an "approved" status? If it's the e-value, what threshold would be preferable?
Given the lack of evidence from aligned proteins or ESTs (and the fact that ab initio predictors over predict so much), I don't put much stock in the e-values. Without some form of evidence supporting them, they are all pretty much just as likely as any other. The PFAM domain at least provides an independent form of evidence support.
One thing to note is that some genomes have low gene counts because of assembly errors. You can get a good CEGMA score because the conserved genes CEGMA looks at are very very short compared to most genes, but then because of assembly issues long genes don't appear well. In cases like these you are more likely to end up with fragmented gene models relative to true gene model.
The honeybee genome is an example. They went from ~10,000 genes to
~15,000 on the reannotation after improving both their repeat database and fixing certain assembly issues.
Thanks,
Carson
On 10/1/14, 11:58 AM, "Stefan Zoller" <stefan.zoller at env.ethz.ch> wrote:
>Thanks for the swift answer. I just add a few clarifications below,
>because I might have omitted some information.
>On 01.10.14 18:20, Carson Holt wrote:
>>> 1) created a species specific repeat library, or actually several
>>>versions (e.g., filtered for hits on known plant transposable
>>>elements etc., or filtering out hits on proper plant proteins), and
>>>ran Maker with it on a subset of the genome. Whatever version of
>>>repeat library I use, I get +/- 5% the same number of Maker approved
>>>proteins. I get slightly more proteins with the "best" species
>>>specific repeat library, so I think it does make a difference, however not a big one.
>>> Interestingly, if I turn off the repeat masking totally, I get about
>>>20% more Maker approved protein models. So either I am doing
>>>something totally wrong here or the repeat masking is working quite
>>>well with the specific repeat libraries.
>> You expect more proteins if you turn all repeat masking off because
>>transposons encode real proteins and there will be a lot of them. Some
>>plant species for example have inflated gene counts because they
>>failed to properly remove transposons during annotation, and removing
>>these false
>> models is actually a major goal of many reannotation projects. Also
>> because transposons can occur in the middle of a gene or in an
>>intron, not masking them can actually cause the predictor to not call
>>the surrounding genes (what you are really interested in), but rather
>>you just a series of transposons. Try using RepeatModeler to build
>>the repeat dataset. It is not so much that you only want repeats
>>from your species in the dataset so much as it is adding any novel
>>repeats that will not be in any dataset.
>> For example, I normally run will all of RepBase together with the
>>novel repeats identified by RepeatModeler. You want to find
>>everything you can.
>I have used RepeatModeler and LTRharvester and MITE and have then
>filtered the combined dataset to remove "real" plant proteins that got
>in there accidentally. I am quite happy with the result. And in Maker I
>am also using including the repeat libraries of other plant species. So
>I am pretty much following your advice.
>>> 2) filtered the non-overlapping ab-initio proteins with PFAM domains
>>>according to your how-to. This works very nicely, thanks. However, I
>>>get quite a lot of models with PFAM hits, even when stringently
>>>filtering for e-value. For example, in the subset of the contiged
>>>genome I usually get around 300 Maker models. And now I have an
>>>additional 180 from the "non-overlapping-with-PFAM-domain" when
>>>filtering for e-value <1e-20.
>>> For e-value < 1e-10 it would be 280, almost twice the number of
>>>proteins. Extrapolating this to the full genome, this would be more
>>>than
>>> 32'000 proteins. This seems a bit excessive and I am not sure if I
>>>am even supposed to use such a stringent e-value filtering. One
>>>reason of having so many additional proteins I can think of, is that
>>>augustus and snap are predicting similar non-overlapping models for
>>>the same location and of course they then both have a PFAM domain. I
>>>can actually see this for some locations when I load the data in
>>>WebApollo. I can think of a crude way to select only the "best"
>>>model for a location (while preferably also considering the already
>>>Maker approved protein) but I wonder if maybe there is already a
>>>solution for this in Maker?
>> The non-overlapping ab-initio proteins are already non-redundant.
>>They will not overlap each other or any of the genes already called by MAKER.
>> Also make sure you have identified novel repeats for your species or
>>these models will be full of transposons which WILL have PFAM
>>domains. Just reading the names of identified domains lets you know
>>if it's a repeat related protein. Also you must have your gene
>>predictors trained on your species. You cannot use a related species
>>as your model if trying to add genes via PFAM domain content. This
>>is because you will get fragmented gene models from the predictors if
>>you are using a related species, and since there is no overlapping
>>evidence alignment to help correct for this (these are the
>>unsupported models after all), then you will be adding very poor
>>models back in.
>OK, I was not aware of these models not overlapping each other. I must
>have looked at the wrong models in WebApollo then. The old Apollo was
>so much easier to set up...
>I had a look at the names in the interproscan output and less than 5%
>of all the models with domains have a name which is clearly
>repeat-related (e.g., PPR repeat, or G-beta repeat).
>I have also spent a lot of time on training Augustus and SNAP on our
>species. Especially the Augustus predictions look rather good I think.
>So also here I am following rather closely your advice. And I must say
>I am VERY grateful for the extensive help and advice you offer,
>because, being almost a one-man-show, it would not be possible for me
>to do all this work without it.
>
>In the end the "mystery" of having different numbers of models in the
>scaffolded vs. contiged genome is partially solved or at least explained.
>One thing that you could maybe give a quick answer: I will go ahead and
>select some of the non-overlapping ab-initio proteins with PFAM domains.
>Should I filter them by e-value or some other parameter before
>promoting them to an "approved" status? If it's the e-value, what
>threshold would be preferable?
>
>Thanks again!
>Stefan
>
>>
>> Thanks,
>> Carson
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>> In short, I think the repeat masking seems not to be the problem
>>>(And I think I have put quite some effort in the repeat library
>>>creation). On the other hand, there are a lot of "good" models in
>>>the non-overlapping proteins that could be filtered and promoted to
>>>proper models, if I only could make the right selection.
>>>
>>> Maybe, based on these additional informations you could point out
>>>additional tests, filtering approaches or analyses I could do to
>>>home-in to the "good" gene models in the non-overlapping gene models
>>>(or Maker approved gene models in general).
>>>
>>> Thanks again for your help!
>>> Stefan
>>>
>>>
>>>
>>> On 25.09.14 20:17, Carson Holt wrote:
>>>> Sorry for the slow reply. I was trying to locate a script that
>>>>might be useful for you.
>>>>
>>>> I think a species specific repeat libary will be of most benefit
>>>>here (it's surprising just how influential this step is). Also
>>>>note that you should train SNAP and Augustus on your species and
>>>>are not just using another related species as a stand in.
>>>>
>>>> With respect to PFAM domains, on some organisms you may not get a
>>>>lot of cross species protein alignments because of divergence or
>>>>assembly issues.
>>>> This of course makes it harder to support these models with direct
>>>>protein alignments. However you can run InterProscan over the
>>>>non-overlapping.proteins.fasta file produced by MAKER (contains
>>>>non-redundant rejected models). Because an HMM is used for domain
>>>>identification, it can pick up protein domains that would not
>>>>produce a significant BLAST alignment because of divergence. You
>>>>can then add models with positive hits for protein domains back
>>>>into your gene set.
>>>>
>>>> This ad hoc procedure usually can only increase gene counts by
>>>>about 10% though for organisms where it's required. I've attached a
>>>>script that makes generating results for these genes easier.
>>>>
>>>> 1. First you run InterProScan with just PFAM.
>>>> 2. Then you take the IDs of all models that have a domain in the
>>>>report and create a list (1 ID per line).
>>>> 3. Next use the fasta_tool script that comes with MAKER together
>>>>with the --select flag to separate just the positive hits (ID's in
>>>>your list) from the non-overlapping.proteins.fasta and
>>>>non-overlapping.transscripts.fasta
>>>> files.
>>>> 4. Use the attached script to separate just the positive hits (your
>>>>ID
>>>> list) from the GFF3. The script will upgrade match/match_part
>>>>results to gene/mRNA/exon/CDS results and print them out for you.
>>>> 5. Use the fasta_maerge and gff3_merge scripts that come with MAKER
>>>>to merge the selected/upgraded GFF3 entries and selected FASTA
>>>>entries back into the original MAKER results.
>>>>
>>>> --Carson
>>>>
>>>>
>>>>
>>>> On 9/23/14, 6:36 AM, "Stefan Zoller" <stefan.zoller at env.ethz.ch>
>>>>wrote:
>>>>
>>>>> Please forgive my ignorance, I am not entirely sure if I
>>>>>understand your question correctly, but I will try to answer.
>>>>> As evidence we use:
>>>>> 1) our own transcriptome (trinity assembled RNAseq, filtering out
>>>>>the very low expression transcripts).
>>>>> 2) all swissprot plant proteins, and several protein sets from
>>>>>closely related plant species downloaded from NCBI.
>>>>> I am not sure if the ab-initio predictions without evidence have
>>>>>pfamm domains. Honestly, I would not know how to tell and how to
>>>>>include/exclude.
>>>>> I was assuming that we should not have too many Maker approved
>>>>>predictions without evidence anyway, because we use "keeps_preds=0".
>>>>> The numbers of gene predictions I mentioned in my email are the
>>>>>predictions reported by the fasta_merge/gff3_merge scripts in the
>>>>>"*maker.proteins.fasta". There are of course many more predictions
>>>>>in e.g., "*maker.augustus_masked.proteins.fasta" (about 68'000 in
>>>>>this file).
>>>>>
>>>>> I hope I am not totally off with my answer.
>>>>> Cheers, Stefan
>>>>>
>>>>>
>>>>>
>>>>> On 23.09.14 02:10, Mark Yandell wrote:
>>>>>> Also are you numbers including the ab-inito predictions without
>>>>>> evidence that have pfamm domains?
>>>>>>
>>>>>> cheers,
>>>>>>
>>>>>>
>>>>>> --mark
>>>>>>
>>>>>>
>>>>>>
>>>>>> Mark Yandell
>>>>>> Professor of Human Genetics
>>>>>> H.A. & Edna Benning Presidential Endowed Chair Co-director USTAR
>>>>>> Center for Genetic Discovery Eccles Institute of Human Genetics
>>>>>> University of Utah
>>>>>> 15 North 2030 East, Room 2100
>>>>>> Salt Lake City, UT 84112-5330
>>>>>> ph:801-587-7707
>>>>>>
>>>>>> ________________________________________
>>>>>> From: maker-devel [maker-devel-bounces at yandell-lab.org] on behalf
>>>>>> of Carson Holt [carson.holt at genetics.utah.edu]
>>>>>> Sent: Monday, September 22, 2014 2:17 PM
>>>>>> To: stefan.zoller at env.ethz.ch; maker-devel at yandell-lab.org
>>>>>> Subject: Re: [maker-devel] diff. numbers of geneson contigs vs.
>>>>>> scaffolded genome
>>>>>>
>>>>>> The contiged assembly is more likely to give spurious hits and
>>>>>>alignments.
>>>>>> They also can be harder to repeat mask. Also gene predictors
>>>>>>can behave slightly different on small sequences than on longer
>>>>>>ones. If you have fewer gene models than you expect, your first
>>>>>>step should be to process the scaffolds with CEGMA. It will
>>>>>>give you an estimate of the genomes "completeness". If CEGMA
>>>>>>gives a 60% completeness value for example then you can expect
>>>>>>to only recover 60% of the expected number of genes.
>>>>>> Next
>>>>>> you should run RepeatModeler of similar software to help generate
>>>>>>a species specific repeat library. Under masked repeats can make
>>>>>>predicting genes on longer scaffolds far more difficult for ab
>>>>>>initio predictors.
>>>>>>
>>>>>> --Carson
>>>>>>
>>>>>>
>>>>>> On 9/19/14, 12:32 AM, "Stefan Zoller" <stefan.zoller at env.ethz.ch>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> I am working on the annotation of a plant genome (about 600MB)
>>>>>>>and we have a reasonable draft assembly, a fairly good
>>>>>>>transcriptome and quite a few proteins from related species. We
>>>>>>>have also extensively trained augustus and are also feeding
>>>>>>>genmark and snap predictions.
>>>>>>>
>>>>>>> Recently I noticed a behavior of Maker that seems fairly odd and
>>>>>>>which I cannot explain at all. When I take the scaffolded
>>>>>>>genome (about
>>>>>>>23000
>>>>>>> scaffolds) I get roughly 9'000 maker approved gene models. Which
>>>>>>>is admittedly a bit on the low side and we have to work on this.
>>>>>>> However,
>>>>>>> when I break up the scaffolds into contigs at stretches of N
>>>>>>>longer 500bp (about 60'000 contigs) I get about 17'000 maker gene models.
>>>>>>> Now
>>>>>>> obviously 17'000 is more in the range what I would expect, so I
>>>>>>>am inclined to go with these. I have looked at both annotations
>>>>>>>and the evidence in WebApollo and the evidence alignments are
>>>>>>>identical for both runs. The approved genes seem to be the
>>>>>>>same, except for the additional ones in the "contiged" genome
>>>>>>>version. The additional gene models are not necessarily at the
>>>>>>>ends of the contigs, so I think it has nothing to do with
>>>>>>>having the stretches of Ns nearby in the scaffolded genome.
>>>>>>> Do
>>>>>>> you have any idea why maker comes up with the additional numbers
>>>>>>>of gene models and how I could "convince" maker to give me the
>>>>>>>same gene models for the scaffolded assembly?
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Stefan
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Stefan Zoller, PhD
>>>>>>> Bioinformatics
>>>>>>> Genetic Diversity Centre
>>>>>>> ETH Zurich CHN E55.1
>>>>>>> Universit?tsstrasse 16
>>>>>>> 8092 Zurich
>>>>>>> Switzerland
>>>>>>>
>>>>>>> Phone: +41 44 632 66 85
>>>>>>> E-Mail: stefan.zoller at env.ethz.ch
>>>>>>> Web: www.gdc.ethz.ch
>>>>>>>
>>>>>>>
>>>>>> _______________________________________________
>>>>>> maker-devel mailing list
>>>>>> maker-devel at box290.bluehost.com
>>>>>>
>>>>>>
>>>>>>http://box290.bluehost.com/mailman/listinfo/maker-devel_yandell-la
>>>>>>b.o
>>>>>>rg
>>> --
>>> Stefan Zoller, PhD
>>> Bioinformatics
>>> Genetic Diversity Centre
>>> ETH Zurich CHN E55.1
>>> Universit?tsstrasse 16
>>> 8092 Zurich
>>> Switzerland
>>>
>>> Phone: +41 44 632 66 85
>>> E-Mail: stefan.zoller at env.ethz.ch
>>> Web: www.gdc.ethz.ch
>>>
>
>--
>Stefan Zoller, PhD
>Bioinformatics
>Genetic Diversity Centre
>ETH Zurich CHN E55.1
>Universit?tsstrasse 16
>8092 Zurich
>Switzerland
>
>Phone: +41 44 632 66 85
>E-Mail: stefan.zoller at env.ethz.ch
>Web: www.gdc.ethz.ch
>
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