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Making the Most of Petabases of Genomic Data

Ben Langmead
October 26, 2018

Making the Most of Petabases of Genomic Data

With the advent of modern DNA sequencing, life science is increasingly becoming a big-data science. The main public archive for sequencing data, the Sequence Read Archive (SRA), now contains over a million datasets and many petabytes of data. While large-scale projects like GTEx, ICGC and TOPmed have been major contributors, even larger projects are on the horizon, e.g. the All of Us and Million Veterans programs. The SRA and similar archives are potential gold mines for researchers but they are not organized for everyday use by scientists. The situation resembles the early days of the World Wide Web, before search engines made the web easy to use. I will describe our progress toward the goal of making it easy for researchers to ask scientific questions about public datasets, focusing on datasets that measure abundance of messenger RNA transcripts (RNA-seq). I will describe how we borrow from trends in big-data wrangling and cloud computing to make public data easier to use and query. I will motivate the work with examples of how we are applying it in research areas concerned with novel (e.g. cryptic) splicing patterns and the splicing factors that regulate them. This is work in progress, and I will highlight ways in which we are learning to make our tools better suited to how scientists work. This is joint work with Abhinav Nellore, Chris Wilks, Jonathan Ling, Luigi Marchionni, Jeff Leek, Kasper Hansen, Andrew Jaffe and others.

Ben Langmead

October 26, 2018
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  1. Ben Langmead Assistant Professor, JHU Computer Science langmea@cs.jhu.edu, langmead-lab.org, @BenLangmead

    IBM Research, Almaden Making the Most of Petabases of Genomic Data October 25, 2018
  2. None
  3. None
  4. 2nd-gen sequencing: the (Lego) Movie bit.ly/2genseq_1 bit.ly/2genseq_2 bit.ly/2genseq_3 T G

    CCATAGTATATCTCGGCTCTAGGCCCTCATTTTTT CCATAGTATATCTCGGCTCTAGGCCCTCATTTTTT CCATAGTATATCTCGGCTCTAGGCCCTCATTTTTT CCATAGTATATCTCGGCTCTAGGCCCTCATTTTTT CCATAGTA TATCTCGG CTCTAGGCCCTC ATTTTTT CCA TAGTATAT CTCGGCTCTAGGCCCTCA TTTTTT CCATAGTAT ATCTCGGCTCTAG GCCCTCA TTTTTT CCATAG TATATCT CGGCTCTAGGCCCT CATTTTTT C C A T A G C A DNA polymerase
  5. CGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTG Input DNA GTATGCACGCGATAG TATGTCGCAGTATCT CACCCTATGTCGCAG GAGACGCTGGAGCCG Reads

  6. CGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTG GTATGCACGCGATAG TATGTCGCAGTATCT CACCCTATGTCGCAG GAGACGCTGGAGCCG TAGCATTGCGAGACG GGTATGCACGCGATA TGGAGCCGGAGCACC CGCTGGAGCCGGAGC Input

    DNA Reads
  7. CGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTG GTATGCACGCGATAG TATGTCGCAGTATCT CACCCTATGTCGCAG GAGACGCTGGAGCCG TAGCATTGCGAGACG GGTATGCACGCGATA TGGAGCCGGAGCACC CGCTGGAGCCGGAGC TGTCTTTGATTCCTG

    CGCGATAGCATTGCG GCATTGCGAGACGCT CCTATGTCGCAGTAT Input DNA Reads
  8. CGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTG GTATGCACGCGATAG TATGTCGCAGTATCT CACCCTATGTCGCAG GAGACGCTGGAGCCG TAGCATTGCGAGACG GGTATGCACGCGATA TGGAGCCGGAGCACC CGCTGGAGCCGGAGC TGTCTTTGATTCCTG

    CGCGATAGCATTGCG GCATTGCGAGACGCT CCTATGTCGCAGTAT GACGCTGGAGCCGGA GCACCCTATGTCGCA GTATCTGTCTTTGAT CCTCATCCTATTATT TATCGCACCTACGTT CAATATTCGATCATG GATCACAGGTCTATC ACCCTATTAACCACT TGCATTTGGTATTTT CGTCTGGGGGGTATG CACGCGATAGCATTG GTATGCACGCGATAG ACCTACGTTCAATAT TATTTATCGCACCTA CCACTCACGGGAGCT GCGAGACGCTGGAGC CTATCACCCTATTAA CTGTCTTTGATTCCT ACTCACGGGAGCTCT CCTACGTTCAATATT GCACCTACGTTCAAT GTCTGGGGGGTATGC AGCCGGAGCACCCTA GACGCTGGAGCCGGA GCACCCTATGTCGCA GTATCTGTCTTTGAT CCTCATCCTATTATT TATCGCACCTACGTT CAATATTCGATCATG GATCACAGGTCTATC ACCCTATTAACCACT CACGGGAGCTCTCCA TGCATTTGGTATTTT CGTCTGGGGGGTATG CACGCGATAGCATTG CACGGGAGCTCTCCA Input DNA Reads
  9. 100 nt 100,000,000 nt Input DNA Reads

  10. Input DNA 100 nt 100,000,000 nt ? Reads

  11. Input DNA 100,000,000 nt ? Reference genome + Reads

  12. Input DNA Reads Reference genome +

  13. Sequence Read Archive Langmead B, Nellore A. Cloud computing for

    genomic data analysis and collaboration. Nat Rev Genet. 2018 May;19(5):325. Currently ~ 20 petabases
  14. Lab goals Efficient Scalable Interpretable Software: Topics: Bowtie 1&2, Arioc,

    Dashing applied algorithms, text indexing, sketching, thread scaling Rail-RNA, recount2, Snaptron, Boiler parallel and high-performance computing, cloud computing, indexing To make high-throughput life science data as usable as possible for scientific labs, especially small ones Qtip, FORGe modeling mapping quality, graph- genome variants, addressing biases Software: Topics: Software: Topics:
  15. Themes • Cloud computing & supercomputing are poised to add

    big value to archived sequencing data • Archives can tell us how much we don't know about something • Archives can generate hypotheses, inform experimental design, even validate results • When one door opens, another one opens
  16. Sequence Read Archive Langmead B, Nellore A. Cloud computing for

    genomic data analysis and collaboration. Nat Rev Genet. 2018 May;19(5):325. Currently ~ 20 petabases
  17. An index is a great leveler GB Shaw Even a

    summary would be an improvement Not GB Shaw
  18. Public summaries Langmead B, Nellore A. Cloud computing for genomic

    data analysis and collaboration. Nat Rev Genet. 2018 Apr;19(4):208-219.
  19. Indexing raw sequencing data Mantis. Ferdman, M., Johnson, R., &

    Patro, R. Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index. In Research in Computational Molecular Biology (p. 271). Springer. BIGSI: Bradley, P., den Bakker, H., Rocha, E., McVean, G., & Iqbal, Z. (2017). Real-time search of all bacterial and viral genomic data. bioRxiv, 234955. Image from Mantis paper Image from Split SBT paper Sequence Bloom Trees. Solomon B, Kingsford C. Fast search of thousands of short-read sequencing experiments. Nat Biotechnol. 2016 Mar;34(3):300-2. Solomon B, Kingsford C. Improved Search of Large Transcriptomic Sequencing Databases Using Split Sequence Bloom Trees. J Comput Biol. 2018 Mar 12. Sun C, Harris RS, Chikhi R, Medvedev P. AllSome Sequence Bloom Trees. J Comput Biol. 2018 May; 25(5):467-479. 1000 Genomes FM Index: Dolle DD, Liu Z, Cotten M, Simpson JT, Iqbal Z, Durbin R, McCarthy SA, Keane TM. Using reference-free compressed data structures to analyze sequencing reads from thousands of human genomes. Genome Res. 2017 Feb;27(2):300-309.
  20. A search engine for RNA-seq Snaptron Index & query engine

    w/ REST API • snaptron.cs.jhu.edu • doi:10.1093/bioinformatics/btx547 Summaries of data, metadata, packaged as R objects • jhubiostatistics.shinyapps.io/recount/ • doi:10.1038/nbt.3838 Scalable, cloud-based spliced alignment of archived RNA-seq datasets • rail.bio • doi:10.1093/bioinformatics/btw575
  21. RNA-seq Picture from: Roy H, Ibba M. Molecular biology: sticky

    end in protein synthesis. Nature. 2006 Sep 7;443(7107):41-2. DNA RNA Protein Transcription Translation
  22. Splicing gene Intron Exon Exon

  23. Splicing AGGGCTGGGCATAAAAGTCAGGGCAGAGCCATCTATTGCTTACATTTGCTTCTGACACAACTGTGTTCACTAGCAAC CTCAAACAGACACCATGGTGCATCTGACTCCTGAGGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTG GATGAAGTTGGTGGTGAGGCCCTGGGCAGGTTGGTATCAAGGTTACAAGACAGGTTTAAGGAGACCAATAGAAACTG GGCATGTGGAGACAGAGAAGACTCTTGGGTTTCTGATAGGCACTGACTCTCTCTGCCTATTGGTCTATTTTCCCACC CTTAGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATGCTGT TATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGCTCACCTGGACA ACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGATCCTGAGAACTTCAGGGTG AGTCTATGGGACGCTTGATGTTTTCTTTCCCCTTCTTTTCTATGGTTAAGTTCATGTCATAGGAAGGGGATAAGTAA CAGGGTACAGTTTAGAATGGGAAACAGACGAATGATTGCATCAGTGTGGAAGTCTCAGGATCGTTTTAGTTTCTTTT

    ATTTGCTGTTCATAACAATTGTTTTCTTTTGTTTAATTCTTGCTTTCTTTTTTTTTCTTCTCCGCAATTTTTACTAT TATACTTAATGCCTTAACATTGTGTATAACAAAAGGAAATATCTCTGAGATACATTAAGTAACTTAAAAAAAAACTT TACACAGTCTGCCTAGTACATTACTATTTGGAATATATGTGTGCTTATTTGCATATTCATAATCTCCCTACTTTATT TTCTTTTATTTTTAATTGATACATAATCATTATACATATTTATGGGTTAAAGTGTAATGTTTTAATATGTGTACACA TATTGACCAAATCAGGGTAATTTTGCATTTGTAATTTTAAAAAATGCTTTCTTCTTTTAATATACTTTTTTGTTTAT CTTATTTCTAATACTTTCCCTAATCTCTTTCTTTCAGGGCAATAATGATACAATGTATCATGCCTCTTTGCACCATT CTAAAGAATAACAGTGATAATTTCTGGGTTAAGGCAATAGCAATATCTCTGCATATAAATATTTCTGCATATAAATT GTAACTGATGTAAGAGGTTTCATATTGCTAATAGCAGCTACAATCCAGCTACCATTCTGCTTTTATTTTATGGTTGG GATAAGGCTGGATTATTCTGAGTCCAAGCTAGGCCCTTTTGCTAATCATGTTCATACCTCTTATCTTCCTCCCACAG CTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCACCCCACCAGTGCAGGCTGCCTA TCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCACTAAGCTCGCTTTCTTGCTGTCCAATTTC TATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACTGGGGGATATTATGAAGGGCCTTGAGCATCTGGATTC intron 1 intron 2 exon 1 exon 2 exon 3 ATGGTGCATCTGACTCCTGAGGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGCAGGCTGC TGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATGCTGTTATGGGCAACCCTAAGGTGAAGGCTCATGG CAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGCTCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTG CACGTGGATCCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCACCCCACCAGTGCAGGCTG CCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCACTAA exon 1 exon 2 exon 3
  24. Alternative splicing Genes can have many isoforms Exons can be

    independently included/excluded; boundaries can shift
  25. Gene annotation Gene annotation:curated collection of isoforms UCSC genome browser

  26. Abhinav Nellore OHSU Jeff Leek, JHU Image by Rgocs http://rail.bio

    Nellore A, Collado-Torres L, Jaffe AE, Alquicira-Hernández J, Wilks C, Pritt J, Morton J, Leek JT, Langmead B. Rail-RNA: scalable analysis of RNA-seq splicing and coverage. Bioinformatics. 2016 Sep 4.
  27. Spliced RNA-seq aligner for analyzing many samples at once •

    Aggregate across samples to borrow strength and eliminate redundant alignment work • Let data prune false junction calls, not annotation • Concise outputs: junctions, junction evidence, coverage vectors; no alignments, unless asked for • Ready for commercial AWS cloud, other clusters http://rail.bio Nellore A, Collado-Torres L, Jaffe AE, Alquicira-Hernández J, Wilks C, Pritt J, Morton J, Leek JT, Langmead B. Rail-RNA: scalable analysis of RNA-seq splicing and coverage. Bioinformatics. 2016 Sep 4.
  28. dbGaP http://docs.rail.bio/dbgap/ Nellore A, Wilks C, Hansen KD, Leek JT,

    Langmead B. Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce. Bioinformatics. 2016 Aug 15;32(16):2551-3.
  29. Toward recount2 • Analyzed ~21,500 human RNA-seq samples with Rail-RNA;

    about 62 Tbp • http://github.com/nellore/runs • ~ $0.72 / sample (Compare to sequencing costs) (Commands we used to run on AWS) jxs samples http://intropolis.rail.bio Nellore A, et al. Human splicing diversity and the extent of unannotated splice junctions across human RNA-seq samples on the Sequence Read Archive. Genome Biol. 2016 Dec 30;17(1):266.
  30. a 0 2000 4000 6000 8000 10000 12000 14000 0

    100000 200000 300000 400000 500000 600000 700000 Minimum number S of samples in which jx is called Junction (jx) count J 18.6% 56,861 jx 100% 96.5% 81.4% 85.8% Novel Alternative donor/acceptor Exon skip Fully annotated 800 900 1000 1100 1200 240000 260000 280000 300000 320000 b 8000 10000 samples c 2500 3000 Annotation includes: UCSC, GENCODE v19 & v24, RefSeq, CCDS, MGC, lincRNAs, SIB genes, AceView, Vega http://intropolis.rail.bio Nellore A, et al. Human splicing diversity and the extent of unannotated splice junctions across human RNA-seq samples on the Sequence Read Archive. Genome Biol. 2016 Dec 30;17(1):266.
  31. • Discovery of new splicing has leveled off • Time

    ripe for a more complete annotation? http://intropolis.rail.bio Nellore A, et al. Human splicing diversity and the extent of unannotated splice junctions across human RNA-seq samples on the Sequence Read Archive. Genome Biol. 2016 Dec 30;17(1):266. Toward recount2
  32. recount2 • >50K human RNA-seq samples from SRA (open) •

    >10K human RNA-seq samples spanning cancer types in The Cancer Genome Atlas (dbGaP) Image: https://www.sevenbridges.com/welcome-to-the-cancer-genomics-cloud-2/ • >10K human RNA-seq samples from Genotype-Tissue Expression (GTEx) project (dbGaP) • Total: ~4.4 trillion reads, 100s of terabases Image: doi:10.1038/ng.2653 Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nature Biotechnology. 2017 Apr 11;35(4):319-321.
  33. Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA,

    Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nature Biotechnology. 2017 Apr 11;35(4):319-321. https://jhubiostatistics.shinyapps.io/recount/ recount2 Leo Collado Torres Abhinav Nellore
  34. Search engine for RNA-seq Snaptron

  35. Snaptron Query planner delegates query components to appropriate systems (sqlite,

    tabix, lucene) and indexes (R-tree, B-tree, Lucene inverted text index) Chris Wilks Sample Filter 8 Region Limited Region Limited & Filtered Region Junction Records Sample Metadata Records Junction Records Filtered Region Filtered Samples Snaptron Query Planner Query Data Store/Index Output 1 2 6 7 3 9 4 5 10 11 12 13 4 7 3 1 2 8 5 6 Sample Metadata Terms Samples "Brain" 1,2,3,6 "Liver" 4,6,9,11 Sample Filter Tabix/R-tree Index Lucene/Inverted Document Index SQLite/B-tree Index Wilks C, Gaddipati P, Nellore A, Langmead B. Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples. Bioinformatics. 2018 Jan 1;34(1):114-116.
  36. Snaptron Provides command-line tool and REST API for querying junctions,

    gene & exon expression, coverage Wilks C, Gaddipati P, Nellore A, Langmead B. Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples. Bioinformatics. 2018 Jan 1;34(1):114-116.
  37. Snaptron • How prevalent is each junction in gene ABCD3

    in each of 50K public datasets? • What is a junction's tissue specificity in the GTEx dataset? • In which samples is splicing pattern A overrepresented relative to B? Example queries http://snaptron.cs.jhu.edu Wilks C, Gaddipati P, Nellore A, Langmead B. Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples. Bioinformatics. 2018 Jan 1;34(1):114-116.
  38. Snaptron case studies • • • • • • •

    • • • 0 5000 10000 15000 20000 GTEx SRAv2 Data compilation Shared sample count (SSC) Validation Failed Passed A. ABCD3 B. KMT2E 3 1 2 1 2 3 C. ALKATI 1 2 3 4 Wilks C, Gaddipati P, Nellore A, Langmead B. Snaptron: querying splicing patterns across tens of thousands of RNA-seq samples. Bioinformatics. 2018 Jan 1;34(1):114-116.
  39. In the field: splicing factors Dr. Ling studies how splicing

    factors affect certain cryptic splicing patterns • cryptic: infrequent, not conserved, "shouldn't happen" Jonathan Ling TDP-43 Seth Blackshaw
  40. In the field: splicing factors Ling JP, Pletnikova O, Troncoso

    JC, Wong PC. TDP-43 repression of nonconserved cryptic exons is compromised in ALS-FTD. Science. 2015 Aug 7;349(6248):650-5.
  41. In the field: splicing factors splicing factors splicing patterns

  42. Ling JP, Wilks C, Charles R, Ghosh D, Jiang L,

    Santiago CP, Pang B, Venkataraman A, Clark BS, Nellore A, Langmead B, Blackshaw S. ASCOT identifies key regulators of photoreceptor-specific splicing. In preparation. Rods have characteristic patterns of exon usage Rod photoreceptors
  43. Rod photoreceptors Exon usage is a useful cell-type signature; often

    not visible at the gene level Ling JP, Wilks C, Charles R, Ghosh D, Jiang L, Santiago CP, Pang B, Venkataraman A, Clark BS, Nellore A, Langmead B, Blackshaw S. ASCOT identifies key regulators of photoreceptor-specific splicing. In preparation.
  44. Certain exons are used only in rods Ling JP, Wilks

    C, Charles R, Ghosh D, Jiang L, Santiago CP, Pang B, Venkataraman A, Clark BS, Nellore A, Langmead B, Blackshaw S. ASCOT identifies key regulators of photoreceptor-specific splicing. In preparation. Rod photoreceptors
  45. Certain splicing factors are specific to rods -- could they

    drive rod-specific splicing? Rod photoreceptors Ling JP, Wilks C, Charles R, Ghosh D, Jiang L, Santiago CP, Pang B, Venkataraman A, Clark BS, Nellore A, Langmead B, Blackshaw S. ASCOT identifies key regulators of photoreceptor-specific splicing. In preparation.
  46. Rod photoreceptors Ling JP, Wilks C, Charles R, Ghosh D,

    Jiang L, Santiago CP, Pang B, Venkataraman A, Clark BS, Nellore A, Langmead B, Blackshaw S. ASCOT identifies key regulators of photoreceptor-specific splicing. In preparation. Up-regulating those splicing factors yields rod-like splicing
  47. Future: public data Rod photoreceptor study involved >90K public datasets

    Most figures I showed used public data only Desire: querying public data = everyday activity in bio research • "Leveler" in a field of haves & have nots One of the best ways for a neuroscientist like me to keep up to date with what colleagues are working on is to attend confer- ences. But on recent trips I have noticed a problem. Too few researchers are consulting and using publicly available data — my own included. What is going on? Massive amounts of biological information are being accumu- discrepancy, and propose a biologically valid reason for it. Why are so many bench biologists overlooking this wealth of cell-type-specific expression data? My hunch is there are two reasons. First, researchers under estimate how many of these data have been published over the past few years because they are being generated across so many different fields. Don’t let useful data go to waste Researchers must seek out others’ deposited biological sequences in community databases, urges Franziska Denk. MEGHNA ABRAHAM WORLD VIEW A personal take on events
  48. Future: cloud computing Clouds are a natural fit for reanalyzing

    public data and for far-flung genomics collaborations • Elasticity, security, reproducibility, less copying Next-generation sequencing (NGS) technologies have been improving rapidly and have become the work- horse technology for studying nucleic acids. NGS plat- forms work by collecting information on a large array of poly merase reactions working in parallel, up to bil- lions at a time inside a single sequencer1. The speed and decreasing cost of NGS have led to the rapid accu- mulation of raw sequencing data (sequencing reads), used in published studies, in public archives2 such as programme17, among others (TABLE 1). gnomAD now spans over 120,000 exomes and over 15,000 whole genomes. ICGC encompasses over 70 subprojects target- ing distinct cancer types, which are conducted in more than a dozen countries and have already collected sam- ples from more than 20,000 donors. Aligned sequenc- ing reads for ICGC require over 1 petabyte (PB; that is, a million GB) of storage. The TOPMed programme, which plans to sequence more than 120,000 genomes17, ads A sequence as NA sequencer. f a computer . onent of a ich the Cloud computing for genomic data analysis and collaboration Ben Langmead1 and Abhinav Nellore2 Abstract | Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data. COMPUTATIONAL TOOLS REVIEWS Langmead B, Nellore A. Cloud computing for genomic data analysis and collaboration. Nature Reviews Genetics. 2018 Apr;19(4):208-219.
  49. Future: data science One dataset All of SRA Public data

    quickly confronts us with technical confounders & missing/incorrect metadata What questions can we answer robustly? At what points on the spectrum? Is metadata fixable? Ellis SE, Collado-Torres L, Jaffe A, Leek JT. Improving the value of public RNA-seq expression data by phenotype prediction. Nucleic Acids Res. 2018 May 18;46(9):e54.
  50. Jeff Leek Jacob Pritt Abhinav Nellore Kasper Hansen Leo Collado

    Torres Chris Wilks Andrew Jaffe José Alquicira- Hernández Jamie Morton Kai Kammers Shannon Ellis Margaret Taub • NIH R01GM118568 • NSF CAREER IIS-1349906 • Sloan Research Fellowship • IDIES Seed Funding program • Amazon Web Services • NIH R01GM105705 (Leek) langmead-lab.org, @BenLangmead Thank you: IDIES Seed funding SciServer SciServer Compute Jonathan Ling Seth Blackshaw