Hi Alejandro,
I apologize, I did not see the answer in
http://permalink.gmane.org/gmane.science.biology.informatics.conductor/53937
I was waiting for a Bioc... sorry about that.

Ok, then those exons with very low log2FC and low p.value would belong, if
I got it right, to genes with many differentially used exons and some with
a very high log2FC, in that case, the linear model would recognise wrongly
as DU the complementary set of exons that actually are not. Since you say
that luckily these cases are exceptions but I have ~98000 exons with
p.adjust<0.05, I could have something really interesting or a terrible flaw
in my 48h compared to my 24 h treated samples.
If the former case, I could run DEXSeq at the gene-level to identify the
genes and trust, which log2FC? or which p.values? to detect interesting
exons?
I first thought to put a threshold of log2FC, but the "volcano" was strange
with few volcano-like behaviour.
or better should I make gene-level DEXSeq and then filter out those genes
with very huge log2FC exons.
Thanks again for your help
Jose



2014-04-03 13:15 GMT+02:00 Alejandro Reyes <alejandro.reyes@embl.de>:

> Hi Jose,
>
> I have an e-mail answering to this thread on the 24.03.2014, maybe you
> missed it or did I write your e-mail wrong?
>
>
> http://permalink.gmane.org/gmane.science.biology.informatics.conductor/53937
>
> Your concern is answered by the second point that I describe there. If you
> look at your "fitted splicing" plot, you can see this.  The extreme case
> is the coefficients fitted
> for your exon E032, it has a value of ~40,000 in one of your conditions
> and a value of ~800 on
> your other conditions.  This will affect the estimation of relative exon
> abundances from
> your other exons. As I mentioned before, this is a limitation of the
> DEXSeq model,
> but luckily, genes like this cases seem to be exceptions rather than the
> rule
> (at least in my experience!).
>
> About using the output from voom to test for DEU, I have not explored
> that option,
> but maybe the maintainers/authors of that package are able to guide you
> better.
>
> Hope it is useful,
> Alejandro
>
>
>
>
> > Dear Alejandro,
> > Have you had time to take a look at my problem (please see below)?
> > I am now using DEXSeq 1.9 to analyze the same ecs objects I had
> > analyzed with 1.8 but it produces the very same results. The problem
> > was regarding too many exons with very low log2FC and very low
> > p.values. I send here an object with the subsetByGene (ecs.one) with
> > one particular gene. The E029 has a very low p.value with a very low
> > log2FC. Either the log2FCs are not OK or the p.values. I cannot
> > understand how such low log2FC for the DEU analysis can give those low
> > p.values. Indeed the complete original ecs gave me 98000 exons with
> > table(res.48$padjust<0.05).
> > However the same analysis (ecs object 4CTRLS vs 4 TREATED) gave me
> > nice results when analysed with DEXSeq 1.6 on the complete design
> > without splitting into two.
> > Here's the picture with expression and splicing values:
> > Immagine in linea 2
> >
> > Here's the design of the ecs object created with CTRL vs HYPOXIA (only
> > at 48h):
> > > design(ecs.one)
> >          sampleName                                  fileName condition
> > N1               N1     Exon_Martelli_Sample_Martelli_N_1.bam      CTRL
> > N2               N2     Exon_Martelli_Sample_Martelli_N_2.bam      CTRL
> > CTRL2         CTRL2  Exon_Martelli_Sample_Martelli_CTRL_2.bam      CTRL
> > CTRL3         CTRL3  Exon_Martelli_Sample_Martelli_CTRL_3.bam      CTRL
> > HYPOXIA2   HYPOXIA2 Exon_Martelli_Sample_Martelli_HYPOXIA_2.bam   HYPOXIA
> > HYPOXIA3   HYPOXIA3 Exon_Martelli_Sample_Martelli_HYPOXIA_3.bam   HYPOXIA
> >
> > Maybe the sampleNames?? N1andN2 come from another batch but it is
> > still a CTRL. If they were different I would expect higher dispersions
> > and hence higher p.values not lower ones, wouldn't I?
> > I have tried to trace the problem a bit with these gene:
> >
> > modelFrame<-constructModelFrame(ecs.one)
> > formula0 = ~sample + exon
> > formula1 = ~sample + exon + condition:exon
> > mm0<-DEXSeq:::rmDepCols(model.matrix(formula0,modelFrame))
> > mm1<-DEXSeq:::rmDepCols(model.matrix(formula1,modelFrame))
> >
> count<-DEXSeq:::getCountVector(ecs=ecs.one,geneID="ENSG00000170017","E029")
> >
> > > mm0
> >    (Intercept) sampleCTRL3 sampleHYPOXIA2 sampleHYPOXIA3 sampleN1
> > sampleN2 exonothers
> > 1            1     0              0              0        1        0
> >    0
> > 2            1     0              0              0        0        1
> >    0
> > *3            1       0              0              0        0
> >  0          0*
> > 4            1     1              0              0        0        0
> >    0
> > 5            1     0              1              0        0        0
> >    0
> > 6            1     0              0              1        0        0
> >    0
> > 7            1     0              0              0        1        0
> >    1
> > 8            1     0              0              0        0        1
> >    1
> > 9            1     0              0              0        0        0
> >    1
> > 10           1     1              0              0        0        0
> >    1
> > 11           1     0              1              0        0        0
> >    1
> > 12           1     0              0              1        0        0
> >    1
> > attr(,"assign")
> > [1] 0 1 1 1 1 1 2
> > attr(,"contrasts")
> > attr(,"contrasts")$sample
> > [1] "contr.treatment"
> >
> > attr(,"contrasts")$exon
> > [1] "contr.treatment"
> >
> > Does it seem OK to you? I guess the intercept is CTRL2 (in bold) but
> > why? Does it have to do with the 'CTRL' string in the sampleName? I
> > tried to change the sample names to CTRL1,CTRL2... but the result was
> > the same.
> >
> > Here's the mm1
> > > mm1
> >  (Intercept) sampleCTRL3 sampleHYPOXIA2 sampleHYPOXIA3 sampleN1
> > sampleN2 exonothers exonthis:conditionHYPOXIA
> > 1  1           0              0              0        1    0
> >  0                         0
> > 2  1           0              0              0        0    1
> >  0                         0
> > 3  1           0              0              0        0    0
> >  0                         0
> > 4  1           1              0              0        0    0
> >  0                         0
> > 5  1           0              1              0        0    0
> >  0                         1
> > 6  1           0              0              1        0    0
> >  0                         1
> > 7  1           0              0              0        1    0
> >  1                         0
> > 8  1           0              0              0        0    1
> >  1                         0
> > 9  1           0              0              0        0    0
> >  1                         0
> > 10 1           1              0              0        0  0          1
> >                         0
> > 11 1           0              1              0        0  0          1
> >                         0
> > 12 1           0              0              1        0  0          1
> >                         0
> > > sessionInfo()
> > R Under development (unstable) (2014-02-09 r64949)
> > Platform: x86_64-apple-darwin10.8.0 (64-bit)
> >
> > locale:
> > [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
> >
> > attached base packages:
> > [1] parallel  stats   graphics  grDevices utils     datasets  methods
> >   base
> >
> > other attached packages:
> > [1] edgeR_3.5.28   limma_3.19.28      DEXSeq_1.9.5
> > Biobase_2.23.6 BiocGenerics_0.9.3 vimcom.plus_0.9-93 setwidth_1.0-3
> > colorout_1.0-2
> >
> > loaded via a namespace (and not attached):
> >  [1] AnnotationDbi_1.25.15 biomaRt_2.19.3  Biostrings_2.31.15
> >  bitops_1.0-6          DBI_0.2-7         GenomeInfoDb_0.99.19
> >  GenomicRanges_1.15.39
> >  [8] hwriter_1.3       IRanges_1.21.34       RCurl_1.95-4.1
> >  Rsamtools_1.15.33     RSQLite_0.11.4  statmod_1.4.18        stats4_3.1.0
> > [15] stringr_0.6.2       tools_3.1.0           XML_3.98-1.1
> >  XVector_0.3.7         zlibbioc_1.9.0
> >
> >
> > I hope you can give me some hints since I am a bit confused and stuck
> > with these results.
> > By the way, for the other Bioc, I know limma/voom used on exons can
> > also work nicely. Is there an easy way to implement a sort of DEU test
> > with limma voom counts? I guess the annotation gtf used to count them
> > should be used to construct models and include it in a similar way
> > with formulae in linearmodels as DEXSeq does with glmnb.fit function.
> > It would be perfect to have it straight as a function also in limma to
> > compare results.
> >
> > Thanks again for your efforts,
> >
> > Looking forward to hearing to your comments.
> > Jose
> >
> >
> >
> >
> > 2014-03-20 16:07 GMT+01:00 Jose Garcia
> > <garciamanteiga.josemanuel@hsr.it
> > <mailto:garciamanteiga.josemanuel@hsr.it>>:
> >
> >     Hi Alejandro,
> >     I solved the problem by re-creating the object ecs.24. I had made
> >     one DEXSeq analysis up to the end by first creating an ecs object.
> >     Then I just split the ecs object, which already had p.value and
> >     other info, and re-run the analysis from sizeFactors on onto the
> >     new split ecs.24 object.
> >     Now it has worked.
> >     However, I have obtained a much harder to interpret result which
> >     points to something wrong I do not know why. And it is present in
> >     both the split and the original ecs.24 and ecs objects.
> >     From scratch:
> >     I made dexseq_prepare_annotation.py with the script from DEXSeq
> >     1.6 which contained the '-r' parameter in order to avoid counting
> >     exons overlapping different genes. Then I tried to count using the
> >     new dexseq_count.py in the same package but it gave me an error
> >     because it had been introduced a check for NH tag in the bam that
> >     I do not have because I use SOAPSplice. You suggested to use the
> >     old dexseq_count.py whithout the check (from DEXSeq 1.4).
> >     It worked and then I used the following script:
> >
> >     sampleFiles.R_ExonOUT<-Files
> >     sampleName.R_ExonOUT<-Names
> >
> sampleCondition.R_ExonOUT<-c(rep("HYPOXIA",2),rep("CTRL",4),rep("HYPOXIA",2))
> >     sampleExperiment.R_ExonOUT<-c(rep("RUN_2",4),rep("RUN_1",4))
> >     sampleTable.R_ExonOUT <- data.frame(sampleName =
> sampleName.R_ExonOUT,
> >                                  fileName = sampleFiles.R_ExonOUT,
> >                                  condition = sampleCondition.R_ExonOUT,
> >                                  experiment = sampleExperiment.R_ExonOUT)
> >     inDir = getwd()
> >     annotationfile = file.path
> >
> ("/lustre1/genomes/hg19/annotation","Homo_sapiens.ensembl72.DEXSeq.gff")
> >
> >     ecs = read.HTSeqCounts(countfiles = file.path(inDir,
> >     sampleTable.R_ExonOUT$fileName),design = sampleTable.R_ExonOUT,
> >     flattenedfile = annotationfile)
> >
> >     sampleNames(ecs) = sampleTable.R_ExonOUT$sampleName
> >     ecs <- estimateSizeFactors(ecs)
> >     library(parallel)
> >     ecs <- estimateDispersions(ecs,nCores=8)
> >     ecs <- fitDispersionFunction(ecs)
> >     ecs <- testForDEU(ecs, nCores=8)
> >     ecs <- estimatelog2FoldChanges(ecs, nCores=8)
> >     res<- DEUresultTable(ecs)
> >
> >     The problem is that I have some exons with a ridiculous log2FC but
> >     with a very good p.adjust.
> >     Same thing with the ecs.24 or ecs.48 split objects. Here an example:
> >
> >     head(res.48[which(res.48$geneID=="ENSG00000148516"),])
> >
> >     geneID exonID  dispersion       pvalue      padjust  meanBase
> >     log2fold(HYPOXIA/CTRL)
> >
> >     ENSG00000148516:E036 ENSG00000148516   E036 0.014798679
> >     2.873434e-16 5.646223e-14  171.5313          -6.075811e-01
> >
> >     ENSG00000148516:E049 ENSG00000148516   E049 0.011425856
> >     2.461690e-14 2.846653e-12  414.4351          -1.907197e-01
> >
> >     ENSG00000148516:E039 ENSG00000148516   E039 0.014486497
> >     2.332678e-13 2.043916e-11  181.3705          -4.226252e-01
> >
> >     *ENSG00000148516:E050 ENSG00000148516   E050 0.009733072
> >     1.131825e-12 8.326638e-11 1432.6492          -1.278668e-05*
> >
> >     ENSG00000148516:E033 ENSG00000148516   E033 0.037143254
> >     3.483915e-12 2.236853e-10  514.5010          -5.273017e-01
> >
> >     ENSG00000148516:E034 ENSG00000148516   E034 0.019826955
> >     4.660942e-12 2.896722e-10  113.6851          -6.541261e-01
> >
> >
> >     If you look at the plot (just a few exons around E50)
> >
> >     plotDEXSeq(ecs.48,"ENSG00000148516",splicing=T)
> >
> >
> >     Immagine in linea 3
> >
> >     It seems clear that all those p-values cannot come from those
> >     log2FC that are adjusted for expression of all exons coming from
> >     the same gene.
> >
> >     I have checked the design and formula:
> >
> >     design(ecs.48)
> >
> >      sampleName  fileName condition experiment
> >
> >     N1     N1 Exon_Martelli_Sample_Martelli_N_1.bam      CTRL      RUN_2
> >
> >     N2     N2 Exon_Martelli_Sample_Martelli_N_2.bam      CTRL      RUN_2
> >
> >     CTRL2 CTRL2  Exon_Martelli_Sample_Martelli_CTRL_2.bam  CTRL
>  RUN_1
> >
> >     CTRL3 CTRL3  Exon_Martelli_Sample_Martelli_CTRL_3.bam  CTRL
>  RUN_1
> >
> >     HYPOXIA2 HYPOXIA2 Exon_Martelli_Sample_Martelli_HYPOXIA_2.bam
> >     HYPOXIA      RUN_1
> >
> >     HYPOXIA3 HYPOXIA3 Exon_Martelli_Sample_Martelli_HYPOXIA_3.bam
> >     HYPOXIA      RUN_1
> >
> >
> >     formula(ecs.48)
> >
> >     $formulaDispersion
> >
> >     [1] "~sample + exon + condition:exon"
> >
> >
> >     $formula0
> >
> >     [1] "~sample + exon"
> >
> >
> >     $formula1
> >
> >     [1] "~sample + exon + condition:exon"
> >
> >
> >     So, I am a bit stuck with it. I guess everything comes from having
> >     used different versions but I cannot come across it. Summarizing:
> >
> >     SOASPSplice
> >
> >     dexseq_prepare_annotation.py (From DEXSeq 1.6) with Ensembl72
> >     (hg19) -r no
> >
> >     dexseq_count.py (From DEXSeq 1.4)
> >
> >     Analysis (DEXSeq 1.8)
> >
> >     Thanks for the help,
> >
> >
> >     Jose
> >
> >
> >
> >     sessionInfo()
> >
> >     R version 3.0.1 (2013-05-16)
> >
> >     Platform: x86_64-unknown-linux-gnu (64-bit)
> >
> >
> >     locale:
> >
> >      [1] LC_CTYPE=en_US       LC_NUMERIC=C LC_TIME=en_US
> >
> >      [4] LC_COLLATE=en_US     LC_MONETARY=en_US LC_MESSAGES=en_US
> >
> >      [7] LC_PAPER=C           LC_NAME=C LC_ADDRESS=C
> >
> >     [10] LC_TELEPHONE=C       LC_MEASUREMENT=en_US LC_IDENTIFICATION=C
> >
> >
> >     attached base packages:
> >
> >     [1] parallel stats     graphics  grDevices utils datasets  methods
> >
> >     [8] base
> >
> >
> >     other attached packages:
> >
> >     [1] DEXSeq_1.8.0       Biobase_2.22.0 BiocGenerics_0.8.0
> >
> >
> >     loaded via a namespace (and not attached):
> >
> >      [1] biomaRt_2.18.0       Biostrings_2.30.1 bitops_1.0-6
> >
> >      [4] GenomicRanges_1.14.3 hwriter_1.3 IRanges_1.20.6
> >
> >      [7] RCurl_1.95-4.1       Rsamtools_1.14.2 statmod_1.4.18
> >
> >     [10] stats4_3.0.1         stringr_0.6.2 tools_3.0.1
> >
> >     [13] XML_3.98-1.1         XVector_0.2.0 zlibbioc_1.8.0
> >
> >
> >
> >     2014-03-19 13:18 GMT+01:00 Jose Garcia
> >     <garciamanteiga.josemanuel@hsr.it
> >     <mailto:garciamanteiga.josemanuel@hsr.it>>:
> >
> >         Hi Alejandro,
> >         I am analyzing with DEXSeq my data. 4 CTRLs and 2 Treated
> >         samples. My design is the following:
> >
> >         design(ecs.24)
> >
> >               sampleName                   fileName condition experiment
> >
> >         H1            H1 Exon_Martelli_Sample_Martelli_H_1.bam
> >         HYPOXIA      RUN_2
> >
> >         H2            H2 Exon_Martelli_Sample_Martelli_H_2.bam
> >         HYPOXIA      RUN_2
> >
> >         N1            N1 Exon_Martelli_Sample_Martelli_N_1.bam   CTRL
> >             RUN_2
> >
> >         N2            N2 Exon_Martelli_Sample_Martelli_N_2.bam   CTRL
> >             RUN_2
> >
> >         CTRL2      CTRL2 Exon_Martelli_Sample_Martelli_CTRL_2.bam
> >         CTRL      RUN_1
> >
> >         CTRL3      CTRL3 Exon_Martelli_Sample_Martelli_CTRL_3.bam
> >         CTRL      RUN_1
> >
> >         When I follow the vignette:
> >
> >         ecs.24 <- estimateDispersions(ecs.24,nCores=8)
> >
> >         ....Done
> >
> >         ecs.24 <- fitDispersionFunction(ecs.24)
> >
> >         ....Done
> >
> >         ecs.24 <- testForDEU(ecs.24, nCores=8)
> >
> >         .....
> >
> >         ecs.24 <- estimatelog2FoldChanges(ecs.24, nCores=8)
> >
> >         *Error in `row.names<-.data.frame`(`*tmp*`, value =
> >         c("geneID", "exonID",  : *
> >
> >         *  duplicate 'row.names' are not allowed*
> >
> >         *Calls: estimatelog2FoldChanges ... pData<- -> pData<- ->
> >         row.names<- -> row.names<-.data.frame*
> >
> >         *In addition: Warning message:*
> >
> >         *non-unique value when setting 'row.names':
> >         'log2fold(CTRL/HYPOXIA)' *
> >
> >
> >         I checked for duplication as you had suggested elsewhere
> >
> >         any(duplicated(featureNames(ecs.24)))
> >
> >         [1] FALSE
> >
> >          any(duplicated(paste(geneIDs(ecs.24),exonIDs(ecs.24),sep=":")))
> >
> >         [1] FALSE
> >
> >
> >         I also checked that the condition in design where factors:
> >
> >
> >         is.factor(pData(ecs.24)$condition)
> >
> >         [1] TRUE
> >
> >
> >         The only explanation I could come to is the fact that I have
> >         an even number of samples for control and treated? or that I
> >         have the 'experiment' column in the design, but it would be
> >         irrelevant since the default formula is only taking condition
> >         into consideration, isn't it?
> >
> >         What could be the origin of the error?
> >
> >         Thanks again!
> >
> >         Jose
> >
> >
> >
> >         > sessionInfo()
> >
> >         R version 3.0.1 (2013-05-16)
> >
> >         Platform: x86_64-unknown-linux-gnu (64-bit)
> >
> >
> >         locale:
> >
> >          [1] LC_CTYPE=en_US       LC_NUMERIC=C       LC_TIME=en_US
> >
> >          [4] LC_COLLATE=en_US LC_MONETARY=en_US    LC_MESSAGES=en_US
> >
> >          [7] LC_PAPER=C           LC_NAME=C       LC_ADDRESS=C
> >
> >         [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US LC_IDENTIFICATION=C
> >
> >
> >         attached base packages:
> >
> >         [1] parallel  stats     graphics grDevices utils     datasets
> >         methods
> >
> >         [8] base
> >
> >
> >         other attached packages:
> >
> >         [1] DEXSeq_1.8.0       Biobase_2.22.0 BiocGenerics_0.8.0
> >
> >
> >         loaded via a namespace (and not attached):
> >
> >          [1] biomaRt_2.18.0 Biostrings_2.30.1    bitops_1.0-6
> >
> >          [4] GenomicRanges_1.14.3 hwriter_1.3       IRanges_1.20.6
> >
> >          [7] RCurl_1.95-4.1 Rsamtools_1.14.2     statmod_1.4.18
> >
> >         [10] stats4_3.0.1         stringr_0.6.2       tools_3.0.1
> >
> >         [13] XML_3.98-1.1         XVector_0.2.0       zlibbioc_1.8.0
> >
> >
> >
> >         2014-03-13 16:32 GMT+01:00 Alejandro Reyes
> >         <alejandro.reyes@embl.de <mailto:alejandro.reyes@embl.de>>:
> >
> >             Dear Xiayu Rao,
> >
> >             Thanks for your interest in DEXSeq.
> >             That looks strange, does it happen when you use files
> >             different from the
> >             one you generated by your own? Could you maybe send me
> >             (offline) your
> >             gtf file and the first 1000 lines of one of your sam files?
> >
> >             Bests,
> >             Alejandro
> >
> >             > Hello,
> >             >
> >             > DEXSeq is a great tool. Thank you for that. I recently
> >             generate my own gtf file with the same format as the
> >             exon.gff generated by dexseq_prepare_annotation.py.
> >             However, the output is strange. I tried to find the reason
> >             with no success. Could you please provide any idea about
> >             that problem? Thank you very much in advance!
> >             >
> >             > Note: I used the latest dexseq, and the sam files had
> >             been sorted.
> >             >
> >             > 1       genes.gtf exonic_part     12228   12594   .
> >               +       . exonic_part_number "001"; gene_id
> >             "ENSG00000223972"
> >             > 1       genes.gtf exonic_part     12722   12974   .
> >               +       . exonic_part_number "002"; gene_id
> >             "ENSG00000223972"
> >             > 1       genes.gtf exonic_part     13053   13220   .
> >               +       . exonic_part_number "003"; gene_id
> >             "ENSG00000223972"
> >             > 1       genes.gtf exonic_part     14830   14969   .
> >               -       . exonic_part_number "001"; gene_id
> >             "ENSG00000223972+ENSG00000227232"
> >             > .............
> >             >
> >             >
> >             > ==> SRR791043_counts.txt <==
> >             > :001G00027000003"
> >             > :002G00021000003"
> >             > :003G00070000003"
> >             > :004G00040000003"
> >             > :005G00060000003"
> >             > :006G00030000003"
> >             > :007G00019000003"
> >             > :008G00045600003"
> >             > :009G00020400003"
> >             > :001G00000000005"
> >             >
> >             >
> >             > Thanks,
> >             > Xiayu
> >
> >             _______________________________________________
> >             Bioconductor mailing list
> >             Bioconductor@r-project.org <mailto:
> Bioconductor@r-project.org>
> >             https://stat.ethz.ch/mailman/listinfo/bioconductor
> >             Search the archives:
> >
> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >
> >
> >
> >
> >         --
> >         Jose M. Garcia Manteiga PhD
> >         Research Assistant - Data Analysis in Functional Genomics
> >         Center for Translational Genomics and BioInformatics
> >         Dibit2-Basilica, 3A3
> >         San Raffaele Scientific Institute
> >         Via Olgettina 58, 20132 Milano (MI), Italy
> >
> >         Tel: +39-02-2643-4751
> >         e-mail: garciamanteiga.josemanuel@hsr.it
> >         <mailto:garciamanteiga.josemanuel@hsr.it>
> >
> >
> >
> >
> >     --
> >     Jose M. Garcia Manteiga PhD
> >     Research Assistant - Data Analysis in Functional Genomics
> >     Center for Translational Genomics and BioInformatics
> >     Dibit2-Basilica, 3A3
> >     San Raffaele Scientific Institute
> >     Via Olgettina 58, 20132 Milano (MI), Italy
> >
> >     Tel: +39-02-2643-4751
> >     e-mail: garciamanteiga.josemanuel@hsr.it
> >     <mailto:garciamanteiga.josemanuel@hsr.it>
> >
> >
> >
> >
> > --
> > Jose M. Garcia Manteiga PhD
> > Research Assistant - Data Analysis in Functional Genomics
> > Center for Translational Genomics and BioInformatics
> > Dibit2-Basilica, 3A3
> > San Raffaele Scientific Institute
> > Via Olgettina 58, 20132 Milano (MI), Italy
> >
> > Tel: +39-02-2643-4751
> > e-mail: garciamanteiga.josemanuel@hsr.it
> > <mailto:garciamanteiga.josemanuel@hsr.it>
>
>


-- 
Jose M. Garcia Manteiga PhD
Research Assistant - Data Analysis in Functional Genomics
Center for Translational Genomics and BioInformatics
Dibit2-Basilica, 3A3
San Raffaele Scientific Institute
Via Olgettina 58, 20132 Milano (MI), Italy

Tel: +39-02-2643-4751
e-mail: garciamanteiga.josemanuel@hsr.it

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