High-resolution gene expression analysis

My PhD thesis defense seminar, given at the Johns Hopkins Biostatistics Department 2/17/15

Transcript

1. High-resolution gene
   expression analysis
   Alyssa Frazee
   Department of Biostatistics
   Thesis Defense Seminar
   February 17, 2015
   

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2. Research goal:
   Find genes that behave differently
   between populations
   

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3. View Slide

4. Gene expression
   AUCAGUCGAUCACCGAU
   transcription
   DNA
   RNA
   ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
   

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5. Gene expression
   AUCAGUCGAUCACCGAU
   transcription
   DNA
   RNA
   ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
   gene
   

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6. Gene expression
   AUCAGUCGAUCACCGAU
   transcription
   DNA
   RNA
   ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
   exons
   

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7. Gene expression
   AUCAGUCGAUCACCGAU
   transcription
   DNA
   RNA
   ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
   introns
   

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8. Gene expression
   AUCAGUCGAUCACCGAU
   transcription
   DNA
   RNA
   ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
   transcript or
   isoform
   

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9. Gene expression
   AUCAGUCGAUCACCGAU
   transcription
   DNA
   RNA
   ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
   junctions
   

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10. Gene expression
    AUCAGUCGAUCACCGAU
    transcription
    DNA
    RNA
    ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
    gene’s “expression level” = amount of
    RNA in cell that was transcribed from
    that gene
    

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11. Measuring gene expression: RNA-seq
    RNA-seq
    reads
    Genome
    (DNA)
    RNA transcripts
    (many possible
    variants)
    

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12. sequencing machine
    

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13. @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:3:177:-56:294:S/2
    GCGTGAGCCACAGGGCCCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCT
    +
    @=ABBBBIIIIIIIIHHGGGGIIDBDIIIIIIGIIIIHIIIIHFDD@BBDBGGFIDEE8DCC/29>BGFCGHHHGF
    @22:16362385-16362561W:ENST00000440999:4:177:137:254:S/1
    TCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCAAGGCCTGAACTACCTGCaGTGGGGAGCACCTCAGGGTTT
    +
    DDGBBCGGGIGGGBDDDHIIGGDGD77=BDIIIIIIIIFHHHHIIIHEFFHGGDD8A>DEGHHIFDDHH8@BEDDI
    @22:16362385-16362561W:ENST00000440999:5:177:68:251:S/2
    AGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCCTGGAGCGAGTTGTGGATGGCAAAAAGACNCGCC
    +
    HIGHIHFHEGE4111:.;8@?@HDIIIIIIIEGGIHHHIIGA?=:FIIIDD8.02506A8=AC#############
    @22:16362385-16362561W:ENST00000440999:6:177:348:453:S/1
    AAGGCCTGAACTACCTGCGGTGGGGAGCACCTCAGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCC
    +
    B9?@8=42:E@GDEDIIIIIGGHIIIFBEEAGIIDIIDHHGGHIIEGEIIIIIHIHFHFFEEFGGGGGB88>:DGH
    @22:51205934-51222090C:ENST00000464740:132:612:223:359:S/2
    GGAAGTATGATGCTGATGACAACGTGAAGATCATCTGCCTGGGAGACAGCGCAGTGGGCAAATCCAAACTCATGGA
    +
    IIEHHHHHIIIIIIIHGGDGHHEDDG8=;?==19;<<>D@@GGGIIHIIHGGDDHGBA=ABEG@@DFCCAA<:=>8
    @22:51205934-51222090C:ENST00000464740:125:612:-1:185:S/1
    TGGAGTGCGCTGCGGCGCGAGCTGGGCCGGCGGGCGTGGTTCGAGAGCGCGCAGAGTCCAGACTGGCGGCAGGGCC
    +
    HHIIIHIDGG@;=@GIIIIIDDGBBBEDB@8>5554,/':9B@@C?==@1:2@?=GG=;

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14. expression = 24
    Genome
    (DNA)
    Measuring expression using counts
    EdgeR (Robinson et al, Bioinformatics 2010)
    DESeq (Anders and Huber, Genome Biology 2010)
    Voom (Law et al, Genome Biology 2014)
    

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15. High information loss
    RNA
    transcripts
    Genome
    (DNA)
    Plus: cannot detect expression outside annotated genes, incorrect
    annotation causes problems, difficult to study non-canonical genomes
    (e.g., cancer)
    

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16. Research goal:
    Find genes that behave differently
    between populations
    1. Discover previously unknown gene
    activity
    2. Find expression differences at the
    transcript level
    

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17. Contributions
    1. DER Finder: Novel method to discover
    previously unknown gene activity
    2. Ballgown: Tools for expression
    analysis, including transcript-level
    differential expression analysis
    3. Polyester: simulator for evaluating
    statistical properties of new DE
    methods
    

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18. DER Finder
    Frazee, Sabunciyan, Hansen,
    Irizarry, and Leek, Biostatistics 2014
    Concept: scan genome base-
    by-base, highlight regions
    showing differential
    expression signal
    

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19. Read coverage
    coverage
    vector
    2 6 0 11 6
    Genome
    (DNA)
    

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20. DER Finder
    genomic position
    read
    coverage
    

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21. Nucleotide-level signal
    samples indexed by i
    locations indexed by l
    j
    confounders indexed by k
    expression confounders
    covariate of
    interest
    

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22. samples indexed by i
    locations indexed by l
    j
    confounders indexed by k
    expression confounders
    covariate of
    interest
    Nucleotide-level signal
    

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23. DER Finder
    genomic position
    DE signal
    read
    coverage
    “bump hunting” idea: Jaffe et al, Int J Epidemiol 2012
    

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24. hidden states (unknown truth)
    DE DE not
    DE
    t
    1
    t
    2
    t
    3
    t
    4
    t
    5
    DE not
    DE
    emissions (observed): moderated t-statistics (Smyth 2004)
    Segmentation: Hidden Markov Model
    

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25. candidate DERs
    region-level statistics
    linear
    models
    HMM
    

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26. linear
    models
    HMM
    permutation tests
    for statistical
    significance
    

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27. match to
    annotation if
    desired:
    CECR1, “may
    play a role in
    regulating cell
    proliferation”
    

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28. ● Data: Y chromosome expression for 9
    males and 6 females
    ● Question: which transcripts are
    differentially expressed between males
    and females?
    ● Expected answer: all
    ● Expected p-value distribution: most near
    0, uniformly distributed away from 0
    Check performance
    

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29. Results: Y chromosome
    Frazee et al, Biostatistics 2014
    (a) (b)
    (d)
    (c)
    

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30. No genes
    annotated
    here
    Annotation
    here does
    not match
    data
    Results:
    Frazee et al, Biostatistics 2014
    

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31. Research goal:
    Find genes that behave differently
    between populations
    1. Can we discover previously unknown
    gene activity? (DER Finder)
    2. Can we discover expression
    differences at the transcript level?
    (Ballgown)
    

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32. Ideal solution: full reconstruction
    Reads
    Estimated
    Transcripts
    Genome
    (DNA)
    

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33. Abundance estimation
    expression ≈ 12 for both
    assembled transcripts
    Genome
    Estimated
    Transcripts
    

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34. Abundance estimation
    expression ≈ 12 for both
    assembled transcripts
    Genome
    Estimated
    Transcripts
    FPKM
    

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35. But: assembly is hard
    Bernard et al, Bioinformatics 2014
    Simulated Data
    

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36. But: assembly is hard
    Bernard et al, Bioinformatics 2014
    Real Data
    

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37. ● Data: RNA-seq from 12 normal samples
    and 12 tumor samples (Kim et al, PloS One 2013)
    ● Question: which transcripts are
    differentially expressed between tumor
    and normal conditions?
    ● Expected answer: most
    ● Expected p-value distribution: most near
    0, uniformly distributed away from 0
    Check performance of current
    assembly-based DE method
    

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38. Cuffdiff 2 (Trapnell et al, Nature Biotechnology 2013) on tumor/normal data (Kim et al, PloS One 2013),
    downloaded from InSilico DB (Coletta et al, Genome Biology 2012)
    Check performance of current
    assembly-based DE method
    

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39. some
    possible
    assemblies
    Inherently ambiguous
    Genome
    

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40. Count models not appropriate
    Genome
    

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41. Concept: software
    infrastructure and simple,
    robust statistical techniques
    improve inference for
    assemblies
    Ballgown
    Frazee, Pertea, Jaffe, Langmead, Salzberg, and Leek.
    Nature Biotechnology (accepted)
    

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42. Ballgown
    Frazee et al, Nature Biotechnology (accepted)
    transcriptome
    assembly
    pipelines
    R/Bioconductor
    DE analysis
    

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43. Defines R data structure for assemblies
    expr
    GRanges
    data frames
    Canonical format
    for differential
    expression analysis
    

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44. Facilitates exploratory analysis
    

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45. Facilitates exploratory analysis
    

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46. Differential expression analysis
    drop-in replacement for Cuffdiff
    F-tests comparing nested models
    

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47. Improved accuracy
    

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48. Results: Timecourse analysis
    

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49. Ballgown: flexible, fast, accurate
    ● Suitable for transcripts (not count-based)
    ● Enables timecourse and multi-group
    analyses
    ● Can adjust for confounders or batch
    effects
    ● Runs in seconds: on cancer data set, 0.7
    sec; Cuffdiff: 10 hours and EBSeq: 6 hours
    ● Correctly identifies known differential
    expression
    EBSeq: Leng et al, Bioinformatics 2013
    

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50. How is accuracy assessed?
    sequence
    RNA
    align
    reads
    estimate
    transcript
    abundances
    test for
    differential
    expression
    DE pipeline:
    assemble
    transcripts
    simulate
    abundances from
    expression model
    

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51. How is accuracy assessed?
    sequence
    RNA
    align
    reads
    estimate
    transcript
    abundances
    test for
    differential
    expression
    DE pipeline:
    assemble
    transcripts
    spike-in
    experiment
    

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52. How is accuracy assessed?
    sequence
    RNA
    align
    reads
    estimate
    transcript
    abundances
    test for
    differential
    expression
    DE pipeline:
    assemble
    transcripts
    simulate reads
    

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53. How is accuracy assessed?
    sequence
    RNA
    align
    reads
    estimate
    transcript
    abundances
    test for
    differential
    expression
    DE pipeline:
    assemble
    transcripts
    simulate reads
    Existing read simulation software did not
    simulate differential expression
    

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54. Polyester
    annotated
    transcript
    sequences
    $ R
    > library(polyester)
    > simulate_experiment(fasta,
    baseline_counts, fold_changes, ...)
    Frazee, Jaffe, Langmead, and Leek. Manuscript under revision.
    

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55. Polyester
    $ R
    > library(polyester)
    > simulate_experiment(fasta,
    baseline_counts, fold_changes, ...)
    read counts: drawn
    from negative binomial
    distribution across
    replicates
    Frazee, Jaffe, Langmead, and Leek. Manuscript under revision.
    

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56. Statistical model for read counts
    samples indexed by i
    transcripts indexed by j
    groups indexed by k
    

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57. $ R
    > library(polyester)
    > simulate_experiment(fasta,
    baseline_counts, fold_changes, ...)
    Polyester
    user-set
    differential
    expression
    Frazee, Jaffe, Langmead, and Leek. Manuscript under revision.
    

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58. Additional features
    ● GC expression bias
    ● Positional sequencing bias
    ● Empirical error models
    ● Empirical fragment length distribution
    ● Exact specification of number of reads per
    sample per transcript
    

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59. Compare to real data
    

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60. Assess differential expression
    methods
    Frazee et al, Nature Biotechnology (accepted)
    

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61. Thank you!
    Co-authors / collaborators:
    Jeff Leek
    Sarven Sabunciyan
    Kasper Hansen
    Rafael Irizarry
    Steven Salzberg
    Ben Langmead
    Andrew Jaffe
    Geo Pertea
    Leonardo Collado Torres
    

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62. Thank you!
    Committee Members:
    Jeff Leek
    Kasper Hansen
    Steven Salzberg
    Anthony Leung
    Dan Arking
    

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63. Thank you!
    Biostatistics Department
    Karen Bandeen-Roche
    Marie Diener-West, John McGready
    Mary Joy Argo, Ashley Johnson, Marti Gilbert
    Marvin Newhouse, Mark Miller, Fernando Pineda,
    Jiong Yang
    Classmates, officemates, friends (!!!)
    Genomics Working Group
    Hopkins Sommer Scholars Program
    

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64. Thank you!
    My parents Shelley and Dave and my sister Kayla
    

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65. Thank you!
    Jeff Leek
    Thanks for believing in me, exemplifying
    fearlessness for me, continually pushing me to
    improve, constantly supporting my career goals, and
    relentlessly encouraging me.
    

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66. Frazee AC, Sabunciyan S, Hansen KD, Irizarry RA, Leek JT (2014). “Differential expression analysis of RNA-seq data at single-
    base resolution.” Biostatistics 15(3): 413-426
    Frazee AC, Pertea G, Jaffe AE, Salzberg SL, Leek JT (2015). “Ballgown bridges the gap between transcriptome assembly and
    expression analysis.” Nature Biotechnology, to appear.
    Frazee AC, Jaffe AE, Langmead B, Leek JT (2014): “Polyester: simulating RNA-seq datasets with differential transcript
    expression.” Under revision at Bioinformatics.
    AC’t Hoen P et al (2013): “Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories.” Nature
    Biotechnology 31(11): 1015-22.
    Anders S and Huber W (2010). “Differential expression analysis for sequence count data.” Genome Biology 11(10): R106.
    Bernard E, Jacob L, Mairal J, Vert J (2014). “Efficient RNA isoform identification and quantification from RNA-seq data with
    network flows.” Bioinformatics 30(17): 2447-2455.
    Efron B (2008): “Microarrays, empirical Bayes, and the two-groups model.” Statistical Science 23(1): 1-22.
    Jaffe AE, Murakami P, Lee H, Leek JT, Fallin MD, Feinberg AP, Irizarry RA (2012): “Bump hunting to identify differentially
    methylated regions in epigenetic epidemiology studies.” International Journal of Epidemiology 41(1): 200-209.
    Law CW, Chen Y, Shi W, Smyth GK (2014): “Voom: precision weights unlock linear model analysis tools for RNA-seq read
    counts.” Genome Biology 15(2): R29.
    Lappalainen T et al (2013). “Transcriptome and genome sequencing uncovers functional variation in humans.” Nature 501
    (7468): 506-11.
    Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BMG, Haag JD, Gould MN, Steward RM, Kendziorski C (2013).
    “EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.” Bioinformatics 29(8): 1035-1043.
    Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital
    gene expression data.” Bioinformatics 26(1): 139-40.
    Smyth GK (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.
    Statistical Applications in Genetics and Molecular Biology 3(1):3.
    Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L (2013). “Differential analysis of gene regulation at
    transcript resolution with RNA-seq.” Nature Biotechnology 31(1): 46-53.
    References
    

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67. ● human diversity: Simon Abrams (via Flickr), CC
    BY-SA 2.0 [link]
    ● tumor cells: cnicholsonpath (via Flickr), CC BY-
    SA 2.0 [link]
    ● awesome cast: Jennifer Carole, CC BY-NA 2.0
    [link]
    ● cell differentiation: Rasback (via Wikipedia), CC
    BY-SA 2.5 [link] (I cropped it)
    ● sequencer: Kinghorn Centre for Clinical
    Genomics (via Flickr), CC-BY-ND 2.0 [link]
    Image Credits
    

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68. Emission distribution
    parameter estimation
    Efron, Statistical Science 2008
    

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69. Emission distribution
    parameter estimation
    

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70. Processing the GEUVADIS
    dataset
    Genetic EUropean VAriation in health and DISease
    Lappalainen et al, Nature 2013; AC’t Hoen et al, Nature Biotechnology 2013
    

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71. turning “big” data into small data
    “Make big data as small as
    possible as quick as is possible”
    -Robert Gentleman
    

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72. turning “big” data into small data
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2
    CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA
    +
    GGFFGBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD
    raw reads (~3 Tb)
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    aligned reads (~1.5 Tb)
    chr1 Cufflinks exon 14765 16672 . + .
    gene_id "XLOC_000001"; transcript_id "TCONS_00000001"; exon_number
    "1"; oId "CUFF.9.1"; tss_id "TSS1";
    chr1 Cufflinks exon 566984 569564 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000002"; exon_number
    "1"; oId "CUFF.14.1"; tss_id "TSS2";
    chr1 Cufflinks exon 569902 570307 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000002"; exon_number
    "2"; oId "CUFF.14.1"; tss_id "TSS2";
    chr1 Cufflinks exon 567008 568410 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000003"; exon_number
    "1"; oId "CUFF.14.2"; tss_id "TSS2";
    chr1 Cufflinks exon 569017 570307 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000003"; exon_number
    "2"; oId "CUFF.14.2"; tss_id "TSS2";
    chr1 Cufflinks exon 567066 567843 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000004"; exon_number
    "1"; oId "CUFF.14.3"; tss_id "TSS2";
    chr1 Cufflinks exon 568627 570307 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000004"; exon_number
    data-driven transcriptome assembly (~150 Mb)
    

    View Slide

73. 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0
    101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG
    GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG
    [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ
    O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0
    MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971
    HI:i:0
    aligned reads (~1.5 Tb)
    chr1 Cufflinks exon 14765 16672 . + .
    gene_id "XLOC_000001"; transcript_id "TCONS_00000001"; exon_number
    "1"; oId "CUFF.9.1"; tss_id "TSS1";
    chr1 Cufflinks exon 566984 569564 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000002"; exon_number
    "1"; oId "CUFF.14.1"; tss_id "TSS2";
    chr1 Cufflinks exon 569902 570307 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000002"; exon_number
    "2"; oId "CUFF.14.1"; tss_id "TSS2";
    chr1 Cufflinks exon 567008 568410 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000003"; exon_number
    "1"; oId "CUFF.14.2"; tss_id "TSS2";
    chr1 Cufflinks exon 569017 570307 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000003"; exon_number
    "2"; oId "CUFF.14.2"; tss_id "TSS2";
    chr1 Cufflinks exon 567066 567843 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000004"; exon_number
    "1"; oId "CUFF.14.3"; tss_id "TSS2";
    chr1 Cufflinks exon 568627 570307 . + .
    gene_id "XLOC_000002"; transcript_id "TCONS_00000004"; exon_number
    data-driven transcriptome assembly
    (~150 Mb)
    ballgown
    objects
    (200-600 Mb)
    turning “big” data into small data
    http://figshare.
    com/articles/GEUVADIS_Pr
    ocessed_Data/1130849
    

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74. Reproducible; freely available
    

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75. Download my processed data; save time!
    3653 hours 999 hours 651 hours
    5299 total hours, assuming 4
    cores available
    

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