[maker-devel] AED score

Fri Dec 7 08:27:22 MST 2012

Don't use keep_preds on any of your steps.  That should really only be
used for Fungi, Oomycetes, or some insect species.  Other genomes with
long introns and short exons like tend to overcall so you will get way to
many false positives.  I gave a more detailed explanation of what I would
do in my other e-mail.

--Carson

On 12-12-06 5:51 PM, "Parul Kudtarkar" <parulk at caltech.edu> wrote:

>Dear Daniel,
>
>Once again thanks for extensive help. We get over estimation of number of
>genes at the end of step 2. So was wondering if there is a way to pick
>only the best annotated. That been said assembly, ESTs and proteins are
>not good and I am currently in process of running cegma.
>
>Also for step 2(ab-initio gene-prediction using SNAP and Augustus) is it
>best to turn off  keep_preds to remove any possible false-positives. Also
>should est2genome turned ON? Of course as pointed in previous email use
>est,proteins apart from training data to generate better gene-model and
>repeat masking as well to mask repeats for ab-initio predictors?
>I believe while running gff3_merge it is best to use option -g to get only
>the gene-predictions and filter evidence.
>
>Thanks and regards,
>Parul Kudtarkar
>
>> Hi Parul,
>> I dont' really have many suggestions for improving the gene models after
>> the annotation is done. Annotation is very dependent on the data you
>>have
>> at hand (the assembly, ESTs and proteins). One thing you could do to get
>> more annotations is to run something like iprscan on the ab-initio
>> predictions that didn't overlap any evidence and look for predictions
>>that
>> contain a pfam domain. Then you can send those predictions back through
>> maker and promote them to gene model status.
>>
>> Do you have the CEGMA results that Carson asked about? That really will
>> tell you what kind of annotation results you can expect. If the assembly
>> doesn't have an N50 greater than the expected median gene size, then you
>> can't expect very good results from automated annotation.
>>
>> 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 1:28 PM
>> To: Daniel Ence
>> Cc: maker-devel at yandell-lab.org
>> Subject: Re: [maker-devel] AED score
>>
>> Dear Daniel,
>>
>> Thanks for clearing doubts. For augustus I am using the closest
>> species(lamprey) to species we are annotating. For SNAP the training
>> set(hmm file) is generated using predictions made from evidence based
>> gene-predictions(sorry the file name was mis-leading). I think STEP 3-6
>> are not required(more or less repetitive without further improving
>> genemodel). Post Step 1(generating training data for SNAP using evidence
>> based prediction) and Step2(ab-initio gene-prediction using SNAP and
>> augustus) do you have recommendations for further improving the gene
>> model?
>>
>> Thanks and regards,
>> Parul Kudtarkar
>>
>>> 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_yan
>>>>>>>>>>>>>>>>dell
>>> -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
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>>>>>>>>>>>>>l-la
>>> b.
>>>>>>>>>>>>>or
>>>>>>>>>>>>>g
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>>> .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
>>>
>>> --
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>>> Center for Computational Regulatory Genomics
>>> Beckman Institute,
>>> California Institute of Technology
>>> http://www.spbase.org
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>> Scientific Programmer
>> Center for Computational Regulatory Genomics
>> Beckman Institute,
>> California Institute of Technology
>> http://www.spbase.org
>>
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>Center for Computational Regulatory Genomics
>Beckman Institute,
>California Institute of Technology
>http://www.spbase.org
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