recount-webinar

recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor

Transcript

1. 11
   recount workflow: Accessing over 70,000 human RNA-seq
   samples with Bioconductor
   Leonardo Collado-Torres
   @fellgernon
   sites.google.com/view/lalresearchgroup/our-weekly-webinar
   speakerdeck.com/lcolladotor/recount-webinar
   April 17, 2018
   

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2. Reference genome
   Reads
   f1000research.com/articles/6-1558/v1
   

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3. f1000research.com/articles/6-1558/v1
   

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4. GTEx TCGA
   slide adapted from Shannon Ellis
   

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5. SRA
   

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6. http://rail.bio/
   Slide adapted from Ben Langmead
   

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7. http://blogs.citrix.com/2012/10/17/announcing-general-availability-of-sharefile-with-storagezones/
   

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8. https://jhubiostatistics.shinyapps.io/recount/
   

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9. jx 1 jx 2 jx 3 jx 4
   jx 5
   jx 6
   Coverage
   Reads
   Gene
   Isoform 1
   Isoform 2
   Potential
   isoform 3
   exon 1 exon 2 exon 3 exon 4
   Expressed region 1:
   potential exon 5
   f1000research.com/articles/6-1558/v1
   

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10. f1000research.com/articles/6-1558/v1
    

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11. exon 1 exon 2
    exon 3
    f1000research.com/articles/6-1558/v1
    

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12. disjoint exon 1
    disjoint exon 2 disjoint exon 3
    f1000research.com/articles/6-1558/v1
    

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13. f1000research.com/articles/6-1558/v1
    

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14. f1000research.com/articles/6-1558/v1
    

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15. > library('recount')
    > download_study( 'ERP001942', type='rse-gene')
    > load(file.path('ERP001942 ', 'rse_gene.Rdata'))
    > rse github.com/leekgroup/recount-analyses/
    

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16. >library('recount')
    > download_study('SRP029880', type='rse-gene')
    > download_study('SRP059039', type='rse-gene')
    > load(file.path('SRP029880 ', 'rse_gene.Rdata'))
    > load(file.path('SRP059039', 'rse_gene.Rdata'))
    > mdat github.com/leekgroup/recount-analyses/
    

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17. Collado Torres et al. Nat. Biotech 2017
    

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18. slide adapted from Kai Kammers
    Can combine with
    genotype data to
    identify eQTLs
    

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19. biorxiv.org/content/early/2018/01/12/247346
    

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20. http://www.rna-seqblog.com/
    Can we use
    expression
    data to predict
    tissue?
    slide adapted from Shannon Ellis
    

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21. Number of Regions 589 589 589 589
    Number of
    Samples (N)
    4,769 4,769 7,193 8,951
    97.3% 96.5%
    71.9%
    50.6%
    Tissue
    prediction is
    accurate
    across data
    sets
    doi.org/10.1093/nar/gky102
    

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22. Number of Regions 589 589 589 589 589
    Number of Samples
    (N)
    4,769 4,769 613 6,579 8,951
    97.3% 96.5% 91.0%
    70.2%
    Prediction
    is more
    accurate in
    healthy
    tissue
    50.6%
    doi.org/10.1093/nar/gky102
    

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23. > library('recount')
    > download_study( 'ERP001942', type='rse-gene')
    > load(file.path('ERP001942 ', 'rse_gene.Rdata'))
    > rse > rse_with_pred github.com/leekgroup/recount-analyses/
    

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24. Ashkaun Razmara, in prep.
    

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25. expression data for ~70,000 human samples
    samples
    phenotypes
    ?
    GTEx
    N=9,962
    TCGA
    N=11,284
    SRA
    N=49,848
    samples
    expression
    estimates
    gene
    exon
    junctions
    ERs
    Answer meaningful
    questions about
    human biology and
    expression
    sex tissue
    M Blood
    F Heart
    F Liver
    slide adapted from Shannon Ellis
    

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26. Code Example:
    research.libd.org/recount-brain/example_PMI/example_PMI.html
    research.libd.org/recount-brain/example_PMI/example_PMI.Rmd
    Replicates part of the GTEx PMI paper by Ferreira et al.
    doi.org/10.1038/s41467-017-02772-x
    Ashkaun Razmara, in prep.
    

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27. slide adapted from Jeff Leek
    

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28. The recount2 team
    Hopkins
    Kai Kammers
    Shannon Ellis
    Margaret Taub
    Kasper Hansen
    Jeff Leek
    Ben Langmead
    OHSU
    Abhinav Nellore
    LIBD
    Leonardo
    Collado-Torres
    Andrew Jaffe
    recount-brain
    Ashkaun Razmara
    Funding and hosting
    NIH R01 GM105705
    NIH 1R21MH109956
    CONACyT 351535
    AWS in Education
    Seven Bridges
    IDIES SciServer
    

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29. expression data for ~70,000 human samples
    (Multiple) Postdoc positions available to
    - develop methods to process and analyze data from recount2
    - use recount2 to address specific biological questions
    This project involves the Hansen, Leek, Langmead and Battle labs at JHU
    Contact: Kasper D. Hansen (khansen@jhsph.edu | www.hansenlab.org)
    Code for making a 5 min video on recount2:
    github.com/lcolladotor/biopeerprize2018
    

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