Trends In Genomics
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The document discusses the evolution of genomics from 2000 to 2010, highlighting advancements in sequencing technology and the reduction in cost for genome sequencing. It addresses new challenges in data management, analysis, and the interpretation of genomic data in context, emphasizing the importance of bioinformatics in navigating vast datasets. The emergence of metagenomics as a way to study communities of organisms and the development of tools for analyzing and comparing genomes are also covered.
Trends In Genomics
- 1. Trends in Genomics: An Engineer’s PerspectiveSaul A. Kravitz, PhDDecember 2009
- 2. Biggest Change: Sequencing is free2000: Factory, AB3700 @ Celera - 1k 500bp reads/day/sequener = 0.5Mbp/day- Human Genome = ~ 190 sequencer yr, ~200M$20022002: Factory, AB3730 @ JCVI - 10k 500bp reads/sequencer/day = 5Mbp/day- Human Genome = ~ 19 sequencer yr, ~10M$20102010: Benchtop, 454 GS Junior - 70M 500bp reads/day = 35Gbp/day - Human genome = ~ 1 sequencer day, ~10k$2010: Service, Complete Genomics- Human genome = ~ 1 day, ~1k$
- 3. New BottlenecksGenerating sequence data – freeData ManagementData QueryData AnalysisBreadth: CommunitiesDepth: Populations (e.g., flu, human)Thinking is very pricy!
- 4. Same Thinking $, More DataProject Cost
- 5. The Crux of the ProblemGenomic data interpreted in contextHow does my genome compare to all othersWhich other proteins are similar to mineSize of context is growing exponentiallyGrowth is faster than Moore’s lawHard to fight an exponentialBLASTP against NCBI NRAll against all BLASTP of microbial proteins
- 6. Bioinformatics Isn’t High Energy PhysicsData inputs are changing rapidlyCE Chromatograms, 454 Flowgrams, Color SpaceError models and read lengths are changing rapidlyTools evolving rapidlyDifficult to track many academic toolsHigh quality commercial platforms emergeEven when “cooks” use shared “ingredients” “recipes” vary widelyFaith based scienceMy dataset alone has limited valueComputations are (relatively) IO Intensive
- 7. Some Solutions and DirectionsRepeated process must be automatedEven if labor is free, deviations from SOP costlyCommercial ToolsMarket has expanded, quality improvedTools for exploring Human VariationThe HuRef BrowserMetagenomics Tools and ChallengesGlobal Ocean Sampling ExpeditionVisualization toolsMetagenomic AnnotationGenome Standards Consortium and M5Clouds and GridsScaaS: Science as a Service
- 8. Personal Genomics: The future is now (ca 2008)
- 9. HuRef Browser: Accelerate thinkingCompare 2 published genomesCraig Venter’s Diploid GenomeComposite NCBI-36Are differences real? Noisy data?Assembly errors?Analysis errors?Methods development requires curation by biologistsAs genomes accumulate, more acute challenge
- 11. Zinc Finger ProteinChr19:57564487-57581356TranscriptGeneHaplotype BlocksVariationsNCBI-36Assembly-Assembly MappingHuRefAssembly Structure
- 12. Protein Truncated by 476 bp InsertionHeterozygous SNPHomozygous SNPInsertion
- 14. Genomics vs MetagenomicsGenomics – ‘Old School’Study of a single organism's genome Genome sequence determined using shotgun sequencing and assembly>1300 microbes sequenced, first in 1995 (at TIGR)DNA usually obtained from pure cultures (<1%) or amplication of DNA from single cells Metagenomics Use genomics tricks on communities – no culturingEnvironmental shotgun sequencing of DNA or RNAMetadata provides context
- 15. Metagenomic QuestionsWithin an environmentWhat biological functions are present (absent)?What organisms are present (absent)?Compare data from (dis)similar environmentsWhat are the fundamental rules of microbial ecology Adapting to environmental conditions?How do communities respond to stimuli?How does community structure change?Search for novel proteins and protein familiesAnd diversity within known families
- 16. Global Ocean Sampling Expedition
- 17. Global Ocean Sampling Expedition 178 Total Sampling Locations
- 18. Pilot: 2.0M reads 4/04
- 19. Phase 1: 7.7M reads, >6M proteins 3/07
- 20. Phase 2-IO: 2.2M reads 3/08
- 21. Phase 2: ~30M reads 2010?
- 23. Open ocean, estuary, embayment, upwelling, fringing reef, atoll…4/043/073/08
- 24. GOS: Sequence Diversity in the OceanRusch et al (PLoS Biology2007)Most sequence reads are uniqueVery limited assemblyMost sequences not taxonomically anchoredReference genomes a basis set? Not really.Several hundred isolatesChallengesRelating shotgun data to reference genomesStructural and Functional Annotation
- 25. Browsing Large Data Collections: Fragment Recruitment ViewerMicrobial Communities vs Reference GenomesMillions of sequence reads vs Thousands of genomesDefinition: A read is recruited to a sequence if:End-to-end blastN alignment existsRapid Hypothesis Generation and ExplorationHow do cultured and wildtype genomes differ?Insertions, deletion, translocationsCorrelation with environmental factors
- 26. Fragment Recruitment ViewerSequence SimilarityGenomic PositionDoug Rusch, JCVI
- 27. Doug Rusch and Michael Press
- 28. Doug Rusch and Michael Press
- 29. GOS Protein AnalysisYooseph et al (PLoS Biology 2007)Novel clustering processSequence similarity based
- 30. Predict putative proteins and group into related clusters
- 31. Include GOS and all known proteinsFindingsGOS proteins
- 32. cover ~all existing prokaryotic families
- 33. expands diversity of known protein families
- 34. ~10% of large clusters are novel
- 35. Many are of viral origin
- 36. No saturation in the rate of novel protein family discoveryAdded Protein Family DiversityYooseph et al (PLoS 2007)Rubisco homologsKnown eukaryotesKnown prokaryotesGOS prokaryotes New Groups
- 37. Annotation ofEnvironmental Shotgun DataChallenges:Lack of contextProtein fragmentsGene FindingYooseph’s Protein Clusters + MetageneFunctional AssignmentVariation of JCVI prok annotation pipeline*Leverages protein cluster annotation -- soonResult:Quality Nearly Comparable to Prokaryotic Genomic Annotation
- 38. Protein ClustersAdvantages and DisadvantagesWeaknessesHomology-basedStateful (also a strength)Less sensitive (for now)StrengthsExponential Linear?Learns over timeEasy to maintain
- 39. Increasing the pressureNextgen + MetagenomicsDeeper collectionsShort sequences less informativeHow should we annotate?When in doubt, use BLAST against NRAA, and other large and fast-growing collectionsAnnotation needs growing dramatically24x7 quality softwareSpecial Hardware: FPGA? Grahics/CUDA? SIMD/SSE?New algorithms?Back to supercomputers?Sharing data and computesStandardization of data, metadata, and computesFolker Meyer, ANL
- 40. Science as a Service (ScaaS)Standard tools as servicesService-Oriented ArchitectureSupported by HPC as necessaryGrid workflow for integrationMaintain tools & data in scalable compute environmentCelera Assembler in the clouds
- 41. Vision for High Throughput ScienceToday:ScientistConstruction of the Ark. Nuremberg Chronicle (1493).
- 42. Vision for High Throughput ScienceEngineersScientist+http://freepages.genealogy.rootsweb.ancestry.com/~thegrove/gec2a.htmlRodin’s Thinker
- 43. CreditsJCVI Informatics TeamSupportDOEGordon and Betty Moore FoundationNIAID
Editor's Notes
- #9: With the publication of the genomes of Craig Venter and Jim Watson, and with many additional human genomes being sequenced, the era of personal genomics is here.We are going to need really good tools to take advantage of this flood of data. My goal today is to share our experience building tools to understand the variation within a single individual’s genome, and try to extrapolate forward to what we will need to understand larger collections of genomes.
- #11: * A chromosome or sequence id followed by a start position and region length e.g., "chr19:450000+100000" to display the region from 450000-550000 on chromosome 19. * A dbSNP id e.g., "rs2691286" * An Ensembl annotation identifer e.g., "ENSG00000104783" * A gene name, e.g. "KLKB1", optionally followed by the amount of flanking sequence to display e.g., "KLKB1^2000"
- #12: Zinc Finger example whole transcript ENST00000334564
- #13: INSERT IS 467 BP TRUNCATES THE PROTEINVNTRPROB HETEROzygousPink = non-synYellow –synpnymous