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
2. Research goal: Find genes that behave differently between populations
4. Gene expression AUCAGUCGAUCACCGAU transcription DNA RNA ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
5. Gene expression AUCAGUCGAUCACCGAU transcription DNA RNA ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT gene
6. Gene expression AUCAGUCGAUCACCGAU transcription DNA RNA ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT exons
7. Gene expression AUCAGUCGAUCACCGAU transcription DNA RNA ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT introns
8. Gene expression AUCAGUCGAUCACCGAU transcription DNA RNA ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT transcript or isoform
9. Gene expression AUCAGUCGAUCACCGAU transcription DNA RNA ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT junctions
10. Gene expression AUCAGUCGAUCACCGAU transcription DNA RNA ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT gene’s “expression level” = amount of RNA in cell that was transcribed from that gene
11. Measuring gene expression: RNA-seq RNA-seq reads Genome (DNA) RNA transcripts (many possible variants)
12. sequencing machine
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?
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)
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)
16. Research goal: Find genes that behave differently between populations 1. Discover previously unknown gene activity 2. Find expression differences at the transcript level
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
18. DER Finder Frazee, Sabunciyan, Hansen, Irizarry, and Leek, Biostatistics 2014 Concept: scan genome base- by-base, highlight regions showing differential expression signal
19. Read coverage coverage vector 2 6 0 11 6 Genome (DNA)
20. DER Finder genomic position read coverage
21. Nucleotide-level signal samples indexed by i locations indexed by l j confounders indexed by k expression confounders covariate of interest
22. samples indexed by i locations indexed by l j confounders indexed by k expression confounders covariate of interest Nucleotide-level signal
23. DER Finder genomic position DE signal read coverage “bump hunting” idea: Jaffe et al, Int J Epidemiol 2012
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
25. candidate DERs region-level statistics linear models HMM
26. linear models HMM permutation tests for statistical significance
27. match to annotation if desired: CECR1, “may play a role in regulating cell proliferation”
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
29. Results: Y chromosome Frazee et al, Biostatistics 2014 (a) (b) (d) (c)
30. No genes annotated here Annotation here does not match data Results: Frazee et al, Biostatistics 2014
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)
32. Ideal solution: full reconstruction Reads Estimated Transcripts Genome (DNA)
33. Abundance estimation expression ≈ 12 for both assembled transcripts Genome Estimated Transcripts
34. Abundance estimation expression ≈ 12 for both assembled transcripts Genome Estimated Transcripts FPKM
35. But: assembly is hard Bernard et al, Bioinformatics 2014 Simulated Data
36. But: assembly is hard Bernard et al, Bioinformatics 2014 Real Data
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
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
39. some possible assemblies Inherently ambiguous Genome
40. Count models not appropriate Genome
41. Concept: software infrastructure and simple, robust statistical techniques improve inference for assemblies Ballgown Frazee, Pertea, Jaffe, Langmead, Salzberg, and Leek. Nature Biotechnology (accepted)
42. Ballgown Frazee et al, Nature Biotechnology (accepted) transcriptome assembly pipelines R/Bioconductor DE analysis
43. Defines R data structure for assemblies expr GRanges data frames Canonical format for differential expression analysis
44. Facilitates exploratory analysis
45. Facilitates exploratory analysis
46. Differential expression analysis drop-in replacement for Cuffdiff F-tests comparing nested models
47. Improved accuracy
48. Results: Timecourse analysis
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
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
51. How is accuracy assessed? sequence RNA align reads estimate transcript abundances test for differential expression DE pipeline: assemble transcripts spike-in experiment
52. How is accuracy assessed? sequence RNA align reads estimate transcript abundances test for differential expression DE pipeline: assemble transcripts simulate reads
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
54. Polyester annotated transcript sequences $ R > library(polyester) > simulate_experiment(fasta, baseline_counts, fold_changes, ...) Frazee, Jaffe, Langmead, and Leek. Manuscript under revision.
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.
56. Statistical model for read counts samples indexed by i transcripts indexed by j groups indexed by k
57. $ R > library(polyester) > simulate_experiment(fasta, baseline_counts, fold_changes, ...) Polyester user-set differential expression Frazee, Jaffe, Langmead, and Leek. Manuscript under revision.
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
59. Compare to real data
60. Assess differential expression methods Frazee et al, Nature Biotechnology (accepted)
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
62. Thank you! Committee Members: Jeff Leek Kasper Hansen Steven Salzberg Anthony Leung Dan Arking
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
64. Thank you! My parents Shelley and Dave and my sister Kayla
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.
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
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
68. Emission distribution parameter estimation Efron, Statistical Science 2008
69. Emission distribution parameter estimation
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
71. turning “big” data into small data “Make big data as small as possible as quick as is possible” -Robert Gentleman
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)
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
74. Reproducible; freely available
75. Download my processed data; save time! 3653 hours 999 hours 651 hours 5299 total hours, assuming 4 cores available