[maker-devel] AED score
Carson Holt
carsonhh at gmail.com
Fri Dec 7 08:22:10 MST 2012
Just to add to Daniels comments.
Things to change in step 1:
> protein= <just try and add more protein evidence in general>
> est2genome=0
> protein2genome=1
> split_hit=20000
> min_contig=50000
Reasoning:
> Your ESTs are very short especially if this is a lamprey species which have
> very long introns and really short exons. In lamprey (i.e. Petromyzon
> marinus), genes tend to be very long (remember gene lengths include introns
> and UTR and is not just the size of the coding sequence), so contigs shorter
> than 50kb are useless for training as you are unlikely to get nice complete
> gene models on those. Also lampreys have very long introns, so you have to
> allow for bigger introns in alignments (split_hit parameter). Finally add as
> much protein evidence from as many sources as possible. Your maker training
> run will take a long time as proteins take forever to align, but because of
> the evolutionary distance of lamprey from everything else and the short exon
> structure of its genome, very little aligns directly to its genome from other
> deuterstome and vertebrate species. I'm assuming this is a lamprey species
> because of what you said about the augustus species file you are using.
> Really the only thing closely related to lampreys unfortunately are other
> lampreys. Lancelets, hagfish, and sharks are not closely related to lamprey
> (while they branch closely together on the tree of life, there are too many
> years since the last common ancestor). So while they may have similar issues
> related to annotation (long introns and short exons etc.) they will not really
> match that well for the gene predictor or even protein alignments.
Additional note:
> I have training files for the lamprey species Petromyzon marinus for both
> Augustus and SNAP that I could share with you in a few week, when the genome
> publication is is released. But before that happens, new gene models will be
> available through the UCSC browser (hopefully within a couple of weeks), and
> gene models are already available through ENSEMBL. Get those protein files
> for training, it may be a big help for you. If you want early access to the
> lamprey training files for Augustus and SNAP, you would have to request it
> from Weiming Li at Michigan State University (the head of that genome
> project).
>
Things to change in step 2:
> Optimally you would be doing de novo training using mRNAseq results, but with
> on;ly sparse protein alignments and such a fragmented assembly, you are
> probably better off just trying to adapt the human HMM files. They won't
> match that well, but you probably won't have the evidence for De Novo
> training. First make a copy of the augustus human species directory and
> rename it to lamprey (cp -R Š/augustus/config/species/human
> Š/augustus/config/species/lamprey). Use it as the base species for retraining
> augustus using your new models. You will have to edit multiple files in the
> directory after you copy it so that they no longer say human or homo sapiens
> internally or in the file name. Use maker2zff to generate the filtered ZFF
> file for training SNAP, but don't train SNAP. Rather use the training file to
> better train Augustus info here (just ignore the CEGMA part) -->
> http://sourceforge.net/mailarchive/message.php?msg_id=29361270
>
> MAKE a backup of the step 1 maker output directory and run step 2 in the old
> step 1 directory (this allows you to change the parameters and reuse files
> form step 1 so you don't have to recalculate all the protein and EST
> alignments). So control files for step 2 are identical to step 1 except for
> these parameters.
>
> protein2genome=0
> augustus_species=lamprey
> snaphmm=lamprey.hmm #optional if you decide to use SNAP
>
> Don't both training SNAP here as you probably won't have enough data and you
> assembly is too fragmented for it to work well, so just stick to augustus.
> Try SNAP if you want just to see how well it works. Manually open up the
> largest contigs in a viewer to look at the models produced from the MAKER run
> to see if they look reasonable (this will also help you decide whether to keep
> SNAP).
>
Things to change in step 3:
> Step 3 should just be a clone of step 2 as it is bootstrapping. But make
> copies of Š/augustus/config/species/lamprey and save it to
> Š/augustus/config/species/lamprey2 (editing all the files and names as you did
> in step 2). This way you don't loose that training data if you decide to step
> back. Also Give your SNAP HMM a new name (I.e. lamprey2.hmm)
>
> augustus_species=lamprey2
> snaphmm=lamprey2.hmm #optional if you decide to use SNAP
>
> Make a backup of Step 2 and run step 3 in the old Step 2 directory (This is
> for file reuse, so the step will run fast). This must be the exact same step
> directory as step 2 for the reuse trick to work.
>
> Manually review the models and if you are satisfied move to step 4. Also note
> that most parameters including the protein, EST, and repeats should not change
> from step1-step3, and should not be removed for step 4 either, you can add
> more evidence, but don't remove evidence (like the repeats).
>
>
Things to change in step 4:
> For this step, just set min_contig=10000 and rerun MAKER inside the step 3
> directory to get the smaller contigs annotated. This should be your final
> step, although you can try altering other parameters or adding more evidence
> sources here etc.
>
>
For other things to keep in mind, you should consider taking extra time to
build a comprehensive library of repeats for your species, I know that
Petromyzon marinus was virtually unannotatable until we had a very deep
repeat library built for it. Any repeat library should be used in all
steps. Also for lamprey, you will expect between 20,000-30,000 genes both
because of your assemblies fragmentation and because of some ancestral
genome duplication. Also be aware that lampreys appear to undergo
programmed genome loss in somatic tissues, so any gene count you get is only
going to represent a maximum of 75-80% of all genes unless the assembly is
derived from germline tissue.
Thanks,
Carson
>
On 12-12-06 2:17 PM, "Daniel Ence" <dence at genetics.utah.edu> wrote:
> Hi Parul,
>
> In step one, if protein2genome isn't turned on, then the protein alignments
> wont be used to generate gene models. Carson said before to use protein2genome
> to generate gene models for your trainings set, but you should know that those
> data aren't being used right now.
>
> In step 2, you can include the est and protein evidence that you used in step
> one, and the ab-initio predictors will takes those data as hints to guide
> their predictions. Also, are you reannotating human here? I'm just wondering
> why the snap file is called Pult, but the augustus species model is human.
> Also, I think you should include the same masking files from step 1, otherwise
> the ab-initio predictors will be predicting on the unmasked sequence which
> will give you many spurious predictions.
>
> In step 3, I'm not certain what you mean by boot-strapping. The control file
> you sent for step 3 will just pass-through all of the data from before.
>
> In step 4, I think what you'll get is pretty close to what you already had in
> step 3. The gene models from step 3 are already based on the est and protein
> data from step 1, so without giving any different evidence or ab-initio
> predictors, I think that you will just get the same gene models that you got
> from step 3. Those gene models are what you should be using to train snap.
> Also, is this the same genome as in the other steps? The genome file here is
> called genome.linear.fa, but before it was called genome.fa.
>
> Step 5. So maker uses both evidence and ab-initio predictions to create
> models. If you don't give any evidence (EST or protein) maker will not
> annotate any gene models. You have also changed the augustus species model in
> this step, but I don't know what you've gained by going from one species to
> another. You should be training augustus with the gene models and then
> creating a new species model in augustus. As I understand it, the filter only
> operates on gene models, not ab-initio predictions or alignments, so it
> probably isn't doing anything the way you have it set.
>
> Step 6. I think step 5 and 6 should be combined. I don't know what you mean by
> boot-strapping here too.
>
> Hopefully that clears up some of the confusion with how maker works. Carson
> will probably have a lot of suggestions too.
>
> Thanks,
> Daniel
>
> Daniel Ence
> Graduate Student
> Eccles Institute of Human Genetics
> University of Utah
> 15 North 2030 East, Room 2100
> Salt Lake City, UT 84112-5330
> ________________________________________
> From: Parul Kudtarkar [parulk at caltech.edu]
> Sent: Thursday, December 06, 2012 10:08 AM
> To: carsonhh at gmail.com
> Cc: Daniel Ence; maker-devel at yandell-lab.org
> Subject: Re: [maker-devel] AED score
>
> Hi Carson,
>
> Once again thanks for a quick response. We expect to get 25,000 to 30,000
> genes.
> Here are the step
> Step1. EST and proteins are used to predict gene-models. the resulting
> file is used as training data-set for SNAP in step2. est2genome is turned
> ON
> Step2. Augustus and SNAP is used to predict genes.
> Step3. The results are re-annoated(boot-strap)
> Step4. EST and proteins are used to predict gene-models. The gff file from
> step3. is used as model_gff. The resulting file is used as training
> data-set for SNAP in step2
> Step 5. Augustus and SNAP is used to predict genes. with AED set to 0.5
> Step6. The results are re-annoated(boot-strap) with AED set to 0.5 and
> contig_size to 10kb
>
> Thanks and regards,
> Parul Kudtarkar
>
> I have attached the configuration file for each step.
>> Your output has no genes. They've all been filtered out. The gene
> predictions are left for reference purposes, but there are no gene models
>> in the file. You need to look at the type columns in the GFF3 file -->
> match/match_part features are evidence and reference data but not
> models.
>> All models will have types gene/mRNA/exon/CDS. For example if you just
> want gene models in your file you can use the gff3_merge script with the
> -g option, and it will only print out the gene models.
>> I think you may be misinterpreting what is happening at different steps,
> as well as how to read the result files. Could you give me a detailed
> explanation of what you expect to get back together with your control
> files and I can walk you through the configuration, and indicate what to
> expect. Also what was the report like from CEGMA. Could you include the
> report file that shows how complete your genome is and how fragmented it
> is?
>> Thanks,
>> Carson
>> On 12-12-05 3:03 PM, "Parul Kudtarkar" <parulk at caltech.edu> wrote:
>>> Dear Carson,
>>> Thanks for a quick response. keep_preds is set to 0
>>> Though for previous step(ab-intio gene predictions keep_preds was set to
> 1, see Scaffold1_input.gff). I have also attached the output file
> Scaffold1_out.gff.
>>> Please advice.
>>> Thanks and regards,
>>> Parul Kudtarkar
>>>> You should always get all predictions, but should only get models
> (match/match_part) with AED scores less than 0.5. You should never get
>>>> models (gene/mRNA/CDS) with an AED score of 1.00 unless you have
> keep_preds set.
>>>> Do you have keeps_preds set or gene models with AED values above your
> threshold (not match/match_part features)? If the first one is the
>>>> case,
>>>> just set it to 0. If the second one in the case, could you send me
> example GFF3?
>>>> Thanks,
>>>> Carson
>>>> On 12-12-04 7:27 PM, "Parul Kudtarkar" <parulk at caltech.edu> wrote:
>>>>> Dear Carson,
>>>>> Thanks once again, we have limited experimental data with very short
>>>>> ESTs.
>>>>> CEGMA is useful for us to gauge our gene-model.
>>>>> On a different note to re-annotate genome(post evidence based
>>>>> prediction(used as training dataset)and abinitio gene-prediction).
> Here
>>>>> are the control parameters I am using with AED score set to 0.5,
>>>>> however
>>>>> I
>>>>> get predictions that includes the ones with AED score of 1.00 in
>>>>> resulting
>>>>> gff3 file. Though I do see the number of genes reduced to 1/3 of
>>>>> initial
>>>>> gff3 file.
>>>>> #-----Genome (Required for De-Novo Annotation)
>>>>> genome=Scaffold1.fa #genome sequence file in fasta format
>>>>> organism_type=eukaryotic #eukaryotic or prokaryotic. Default is
>>>>> eukaryotic
>>>>> #-----Re-annotation Using MAKER Derived GFF3
>>>>> genome_gff= Scaffold1.gff #re-annotate genome based on this gff3 file
> est_pass=1 #use ests in genome_gff: 1 = yes, 0 = no
>>>>> altest_pass=0 #use alternate organism ests in genome_gff: 1 = yes, 0 =
> no
>>>>> protein_pass=1 #use proteins in genome_gff: 1 = yes, 0 = no
>>>>> rm_pass=0 #use repeats in genome_gff: 1 = yes, 0 = no
>>>>> model_pass=1 #use gene models in genome_gff: 1 = yes, 0 = no
>>>>> pred_pass=1 #use ab-initio predictions in genome_gff: 1 = yes, 0 = no
> other_pass=0 #passthrough everything else in genome_gff: 1 = yes, 0 =
>>>>> no
>>>>> #-----MAKER Behavior Options
>>>>> AED_threshold=0.5 #Maximum Annotation Edit Distance allowed (bound by
> 0
>>>>> and 1)
>>>>> Thanks and regards,
>>>>> Parul Kudtarkar
>>>>>> Wow 330,000 is a lot. a large portion of genes are likely to be
>>>>>> partial
>>>>>> at
>>>>>> best. You should seriously consider using mRNAseq to capture those
> by
>>>>>> using maker's est_gff option to pass in results from cufflinks or
>>>>>> trinity.
>>>>>> Also I wouldn't even try to annotate contigs less than 10kb in
> size,
>>>>>> just
>>>>>> have maker skip them by setting the min_contig filter in the
> maker_opts.ctl file.
>>>>>> Thanks,
>>>>>> Carson
>>>>>> On 12-11-29 7:31 PM, "Parul Kudtarkar" <parulk at caltech.edu> wrote:
>>>>>>> Thanks for the guidance Carson, total contig size is 330,611 with
> N50
>>>>>>> of
>>>>>>> 39.17kb. I agree we have short ESTs. So this is the possible reason
>>>>>>> when
>>>>>>> filtering based on AED score 0.75 there are no gene models predicted
> despite the model_gff file has few genes with scores less than 0.75?
> Thanks and regards,
>>>>>>> Parul Kudtarkar
>>>>>>> There are certain characteristics that are apparent in this
> contig.
>>>>>>> First
>>>>>>> it seems to be repeat rich with a very low gene density. You also
>>>>>>> have
>>>>>>> very short ESTs, and because of the lengths you are probably getting
> many
>>>>>>> of them to align spuriously which produces very short gene models
> that
>>>>>>> are
>>>>>>> more than likely false positives or at the very least just a piece
>>>>>>> of
>>>>>>> a
>>>>>>> gene. I would turn off est2genome as a predictor for this reason
>>>>>>> unless
>>>>>>> you can get longer EST assemblies (i.e. From mRNAseq). Your
>>>>>>> protein
>>>>>>> alignments also seem to be few and far between. You probably need
> to
>>>>>>> add
>>>>>>> more proteins from a couple of related species, and you might
> consider
>>>>>>> using protein2genome rather than est2genome as a predictor if you
> are
>>>>>>> still working to generate a training set. Also est2genome produced
> models
>>>>>>> almost always have an AED score near 0 so mixing est2genome with
> the
>>>>>>> AED_threshold with such limited protein support does create an
> artificial
>>>>>>> bias to get back very short and incomplete models.
>>>>>>> How many contigs do you have in total and what is the N50 value
> for
>>>>>>> the
>>>>>>> assembly? If you have a large number of very short contigs, you will
>>>>>>> get
>>>>>>> very inflated gene counts because you get genes split across contigs
>>>>>>> and
>>>>>>> many contigs tend t be subtle rearrangements of other contigs just
> assembled in a slightly different way (so you can get bits and
> pieces
>>>>>>> of
>>>>>>> the same genes just rearranged). This scenario is another
>>>>>>> confounding
>>>>>>> factor if using the est2genome predictor with short ESTs. I would
> recommend running CEGMA to get an estimate for the genome
>>>>>>> completeness
>>>>>>> as
>>>>>>> well as get an estimate of fragmentation as one of the statistics
>>>>>>> produced
>>>>>>> is a percent of genes that are found complete (end to end) vs
> those
>>>>>>> that
>>>>>>> are partial. CEGMA identifies house keeping genes that tend to be
> shorter
>>>>>>> and less intron rich than other genes in the genome, so if CEGMA
> gives
>>>>>>> a
>>>>>>> high partial percentage and a low complete percentage, then this
>>>>>>> pattern
>>>>>>> can be expected to be even more exaggerated for other genes in the
> genome.
>>>>>>> If your genome is highly fragmented or proteins do not align well
> then
>>>>>>> there are other strategies. For example, some vertebrate genomes
> end
>>>>>>> up
>>>>>>> having extremely fragmented assemblies (on the order of 100,000
> contigs),
>>>>>>> and if they are distantly related to other annotated species few
>>>>>>> proteins
>>>>>>> may align to the contigs because the introns in the alignments
> tend
>>>>>>> to
>>>>>>> be
>>>>>>> so long and exons so short that it pushes down the significance
> scores
>>>>>>> too
>>>>>>> much. In those cases heavy mRNAseq seems to be the best if not
> only
>>>>>>> way
>>>>>>> to get enough evidence to stitch gene models together.
>>>>>>> Thanks,
>>>>>>> Carson
>>>>>>> On 12-11-28 4:40 PM, "Parul Kudtarkar" <parulk at caltech.edu> wrote:
>>>>>>> Dear Carson and Daniel,
>>>>>>> Thanks. I ran sample file for filtering genes based on AED score.
> The
>>>>>>> input gff3 file was provided to option model_pred(see attached file
> Scaffold1.gff), the cutoff AED score was set to 0.75. There are at
>>>>>>> least
>>>>>>> 5
>>>>>>> genes with AED score less than 0.75. However there were no genes
>>>>>>> predicted
>>>>>>> in the output file(see attached file Scaffold1_out). I have also
>>>>>>> attached
>>>>>>> the maker_opts.ctl. Could you please advice on this.
>>>>>>> Thanks and regards,
>>>>>>> Parul Kudtarkar
>>>>>>> Use the AED_threshold option in the maker_opts.ctl file if you
>>>>>>> just
>>>>>>> want
>>>>>>> to restrict final gene models to close matches directly within
>>>>>>> maker.
>>>>>>> On
>>>>>>> the other hand, if you are trying to build a dataset for
> training
>>>>>>> gene
>>>>>>> predictors, use the maker2zff script for generating a filtered
>>>>>>> dataset
>>>>>>> for
>>>>>>> SNAP training. There are a number of filters available. Just
> call
>>>>>>> the
>>>>>>> script once without parameters to see the options.
>>>>>>> Thanks,
>>>>>>> Carson
>>>>>>> On 12-11-27 5:55 PM, "Daniel Ence" <dence at genetics.utah.edu>
>>>>>>> wrote:
>>>>>>> Hi Parul,
>>>>>>> I think the way you described (with the maker_opts.ctl file) is
> how
>>>>>>> you
>>>>>>> want to proceed. You still need to give the genome too.
>>>>>>> Daniel
>>>>>>> Daniel Ence
>>>>>>> Graduate Student
>>>>>>> Eccles Institute of Human Genetics
>>>>>>> University of Utah
>>>>>>> 15 North 2030 East, Room 2100
>>>>>>> Salt Lake City, UT 84112-5330
>>>>>>> ________________________________________
>>>>>>> From: maker-devel-bounces at yandell-lab.org
>>>>>>> [maker-devel-bounces at yandell-lab.org] on behalf of Parul
>>>>>>> Kudtarkar
>>>>>>> [parulk at caltech.edu]
>>>>>>> Sent: Tuesday, November 27, 2012 3:41 PM
>>>>>>> To: Parul Kudtarkar
>>>>>>> Cc: maker-devel at yandell-lab.org
>>>>>>> Subject: Re: [maker-devel] AED score
>>>>>>> Also, are there any other parameters that are required when
> filtering
>>>>>>> based on AED score?
>>>>>>> Hello Carson,
>>>>>>> Just to confirm, Is there a script that would filter gene
> models
>>>>>>> at
>>>>>>> specific AED score.
>>>>>>> Alternatively if I were to do this within maker with regards
> to
>>>>>>> parameters
>>>>>>> in maker_opts.ctl file I would have to provide my predicted
>>>>>>> genes
>>>>>>> gff3
>>>>>>> file to model_gff and set AED_threshold at desired threshold?
>>>>>>> Thanks and regards,
>>>>>>> Parul Kudtarkar
>>>>>>> AED score with 1 are the ones you don't want. 0 is best and
> 1
>>>>>>> is
>>>>>>> worst
>>>>>>> as
>>>>>>> it is a distance metric. You can use the AED_threshold
> parameter
>>>>>>> to
>>>>>>> require better matching to the evidence by setting it closer
> to
>>>>>>> 0.
>>>>>>> You
>>>>>>> can
>>>>>>> also try to increase protein homology evidence as some of
> your
>>>>>>> calls
>>>>>>> may
>>>>>>> be split genes due to lack of evidence linking them.
>>>>>>> --Carson
>>>>>>> On 12-11-26 4:35 PM, "Parul Kudtarkar" <parulk at caltech.edu>
>>>>>>> wrote:
>>>>>>> Dear Maker community,
>>>>>>> For gene-prediction I get training data-set from evidence
>>>>>>> based
>>>>>>> prediction, I use this data-set to train SNAP as well as Augustus
> predictions, followed by boot-strapping. I would typically expect
> 20-30K
>>>>>>> genes however I am getting 8 times the expected gene count
>>>>>>> indicating
>>>>>>> too
>>>>>>> many false positives. Is there a way to further refine these
>>>>>>> predication/script to retain predictions with AED score 1 and if yes
>>>>>>> how
>>>>>>> to go about this?
>>>>>>> Thanks and regards,
>>>>>>> Parul Kudtarkar
>>>>>>> --
>>>>>>> Scientific Programmer
>>>>>>> Center for Computational Regulatory Genomics
>>>>>>> Beckman Institute,
>>>>>>> California Institute of Technology
>>>>>>> http://www.spbase.org
>>>>>>> _______________________________________________
>>>>>>> maker-devel mailing list
>>>>>>> maker-devel at box290.bluehost.com
>>>>>>> http://box290.bluehost.com/mailman/listinfo/maker-devel_yandell
> -l
>>>>>>> ab
>>>>>>> .o
>>>>>>> rg
>>>>>>> --
>>>>>>> Scientific Programmer
>>>>>>> Center for Computational Regulatory Genomics
>>>>>>> Beckman Institute,
>>>>>>> California Institute of Technology
>>>>>>> http://www.spbase.org
>>>>>>> --
>>>>>>> Scientific Programmer
>>>>>>> Center for Computational Regulatory Genomics
>>>>>>> Beckman Institute,
>>>>>>> California Institute of Technology
>>>>>>> http://www.spbase.org
>>>>>>> _______________________________________________
>>>>>>> maker-devel mailing list
>>>>>>> maker-devel at box290.bluehost.com
>>>>>>> http://box290.bluehost.com/mailman/listinfo/maker-devel_yandell-la
> b.
>>>>>>> or
>>>>>>> g
>>>>>>> _______________________________________________
>>>>>>> maker-devel mailing list
>>>>>>> maker-devel at box290.bluehost.com
>>>>>>> http://box290.bluehost.com/mailman/listinfo/maker-devel_yandell-la
> b.
>>>>>>> or
>>>>>>> g
>>>>>>> _______________________________________________
>>>>>>> maker-devel mailing list
>>>>>>> maker-devel at box290.bluehost.com
>>>>>>> http://box290.bluehost.com/mailman/listinfo/maker-devel_yandell-lab
> .o
>>>>>>> rg
>>>>>>> --
>>>>>>> Scientific Programmer
>>>>>>> Center for Computational Regulatory Genomics
>>>>>>> Beckman Institute,
>>>>>>> California Institute of Technology
>>>>>>> http://www.spbase.org
>>>>>>> --
>>>>>>> Scientific Programmer
>>>>>>> Center for Computational Regulatory Genomics
>>>>>>> Beckman Institute,
>>>>>>> California Institute of Technology
>>>>>>> http://www.spbase.org
>>>>> --
>>>>> Scientific Programmer
>>>>> Center for Computational Regulatory Genomics
>>>>> Beckman Institute,
>>>>> California Institute of Technology
>>>>> http://www.spbase.org
>>> --
>>> Scientific Programmer
>>> Center for Computational Regulatory Genomics
>>> Beckman Institute,
>>> California Institute of Technology
>>> http://www.spbase.org
>
>
> --
> Scientific Programmer
> Center for Computational Regulatory Genomics
> Beckman Institute,
> California Institute of Technology
> http://www.spbase.org
>
> --
> Scientific Programmer
> Center for Computational Regulatory Genomics
> Beckman Institute,
> California Institute of Technology
> http://www.spbase.org
>
>
>
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