High-resolution gene expression analysis

567d15666cd2891a4e6c49e007f30a08?s=47 Alyssa Frazee
February 17, 2015

High-resolution gene expression analysis

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

567d15666cd2891a4e6c49e007f30a08?s=128

Alyssa Frazee

February 17, 2015
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  1. 3.
  2. 13.

    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??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=;<HHHHGIIHHEC-;;3?
  3. 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)
  4. 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)
  5. 16.

    Research goal: Find genes that behave differently between populations 1.

    Discover previously unknown gene activity 2. Find expression differences at the transcript level
  6. 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
  7. 18.

    DER Finder Frazee, Sabunciyan, Hansen, Irizarry, and Leek, Biostatistics 2014

    Concept: scan genome base- by-base, highlight regions showing differential expression signal
  8. 21.

    Nucleotide-level signal samples indexed by i locations indexed by l

    j confounders indexed by k expression confounders covariate of interest
  9. 22.

    samples indexed by i locations indexed by l j confounders

    indexed by k expression confounders covariate of interest Nucleotide-level signal
  10. 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
  11. 27.

    match to annotation if desired: CECR1, “may play a role

    in regulating cell proliferation”
  12. 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
  13. 30.

    No genes annotated here Annotation here does not match data

    Results: Frazee et al, Biostatistics 2014
  14. 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)
  15. 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
  16. 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
  17. 41.

    Concept: software infrastructure and simple, robust statistical techniques improve inference

    for assemblies Ballgown Frazee, Pertea, Jaffe, Langmead, Salzberg, and Leek. Nature Biotechnology (accepted)
  18. 43.

    Defines R data structure for assemblies expr GRanges data frames

    Canonical format for differential expression analysis
  19. 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
  20. 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
  21. 51.

    How is accuracy assessed? sequence RNA align reads estimate transcript

    abundances test for differential expression DE pipeline: assemble transcripts spike-in experiment
  22. 52.

    How is accuracy assessed? sequence RNA align reads estimate transcript

    abundances test for differential expression DE pipeline: assemble transcripts simulate reads
  23. 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
  24. 54.

    Polyester annotated transcript sequences $ R > library(polyester) > simulate_experiment(fasta,

    baseline_counts, fold_changes, ...) Frazee, Jaffe, Langmead, and Leek. Manuscript under revision.
  25. 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.
  26. 57.

    $ R > library(polyester) > simulate_experiment(fasta, baseline_counts, fold_changes, ...) Polyester

    user-set differential expression Frazee, Jaffe, Langmead, and Leek. Manuscript under revision.
  27. 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
  28. 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
  29. 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
  30. 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.
  31. 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
  32. 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
  33. 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
  34. 71.

    turning “big” data into small data “Make big data as

    small as possible as quick as is possible” -Robert Gentleman
  35. 72.

    turning “big” data into small data @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD

    @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??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)
  36. 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
  37. 75.

    Download my processed data; save time! 3653 hours 999 hours

    651 hours 5299 total hours, assuming 4 cores available