[maker-devel] AED score
Parul Kudtarkar
parulk at caltech.edu
Fri Dec 7 15:06:26 MST 2012
Dear Carson,
Thanks for detailed explanation and help. Now we know the exact parameters
that should help us with generating good gene-model.
The genome for which we are working on gene predictions is
Echinoderm(sea-urchin). lamprey was the closest organism for training
augustus that I could find. A quick question for training augustus, there
is augustus_species option how would you go from training data generated
by zff2augustus_gbk.pl > train.gb as specified here
http://sourceforge.net/mailarchive/message.php?msg_id=29361270 to
generating the species folder that can be specified to augustus_species
option.
The average expected gene-length in our case is ~15kb.
We also have a good repeat library for our genome.
Thanks and regards,
Parul Kudtarkar
> For step 2 and 3, use lamprey rather than human in augustus. For some
> reason the current version of augustus doesn't diplay it as an option for
> the species help menu (so I didn't see it), but it's there in
> �/augustus/config/species/lamprey
>
> For any additional training, make copies into
> �/augustus/config/species/lamprey2 and �/augustus/config/species/lamprey3
>
> Thanks,
> Carson
>
>
>
> From: Carson Holt <carsonhh at gmail.com>
> Date: Friday, 7 December, 2012 10:22 AM
> To: Daniel Ence <dence at genetics.utah.edu>, "maker-devel at yandell-lab.org"
> <maker-devel at yandell-lab.org>, Parul Kudtarkar <parulk at caltech.edu>
> Subject: Re: [maker-devel] AED score
>
> 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 �/augustus/config/species/human
>> �/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 �/augustus/config/species/lamprey and save it to
>> �/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
>>
>>
>>
>
>
>
--
Scientific Programmer
Center for Computational Regulatory Genomics
Beckman Institute,
California Institute of Technology
http://www.spbase.org
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