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Eisen.indoor air2011

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Talk by Jonathan Eisen and Indoor Air 2011 meeting

Talk by Jonathan Eisen and Indoor Air 2011 meeting

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  • Send it out for sequencing, do an alignment with your gene and blast it (search for other organisms) with a similar sequence\n
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  • Transcript

    • 1. Microbial Ecology Indoor Microbial Ecology (DNA Sequencing Focus) Indoor Air 2011Workshop on Microbiomes of the Built Environment Jonathan A. Eisen, Ph.D. University of California, Davis DOE Joint Genome Institute Twitter: @phylogenomics
    • 2. Outline• Introduction• Sequencing in microbial studies• Sequencing technologies• Current and future issues
    • 3. microBEnet• / http://microbe.net
    • 4. microBEnet
    • 5. MICROBES
    • 6. A Field Guide to Microbes• What should be included • Catalog of types of organism • Functional diversity • Biogeography (space and time) • Niche information • Means for identification• “Natural” locations• “Non natural (i.e., built) locations
    • 7. Microbial Ecology• Much more than just a field guide• Interactions of microbes with each other with macroorganisms, and the environment• Mechanisms and rules of such interactions• Can be applied to any environment(s) including built ones
    • 8. I: Sequencing and Microbes• Sequencing is useful as a tool in studies of microbial ecology for many reasons• It is complimentary to other means of study
    • 9. Era I: rRNA Tree of Life Bacteria • Appearance of microbes not informative (enough) • rRNA Tree of Life Archaea identified two major groups of organisms w/o nuclei • rRNA powerful for many reasons, though not perfect EukaryotesBarton, Eisen et al. “Evolution”, CSHL Press. 2007.Based on tree from Pace 1997 Science 276:734-740
    • 10. Era II: rRNA in environment
    • 11. Great Plate Count Anomaly Culturing Microscope Count Count
    • 12. Great Plate Count Anomaly Culturing Microscope Count <<<< Count
    • 13. Great Plate Count Anomaly DNA Culturing Microscope Count <<<< Count
    • 14. PCR & phylogenetic analysis of rRNA DNA extraction PCR Makes lots Sequence PCR of copies of rRNA genes the rRNA genes in sample rRNA1 5’...ACACACATAGGTGGAGC TAGCGATCGATCGA... 3’ Phylogenetic tree Sequence alignment = Data matrix rRNA2 rRNA1 rRNA2 rRNA1 A C A C A C 5’..TACAGTATAGGTGGAGCT rRNA4 AGCGACGATCGA... 3’rRNA3 rRNA2 T A C A G T rRNA3 rRNA3 C A C T G T 5’...ACGGCAAAATAGGTGGA E. coli Humans rRNA4 C A C A G T TTCTAGCGATATAGA... 3’ Yeast E. coli A G A C A G rRNA4 5’...ACGGCCCGATAGGTGG Humans T A T A G T ATTCTAGCGCCATAGA... 3’ Yeast T A C A G T
    • 15. Era II: rRNA in environmentThe Hidden Majority Richness estimates Hugenholtz 2002 Bohannan and Hughes 2003
    • 16. Era III: Genome Sequencing Genomes Online Fleischmann et al. 1995 Science 269:496-512
    • 17. Lateral Gene TransferPerna et al. 2003
    • 18. Era IV: Genomes in Environment shotgun sequenceMetagenomics
    • 19. Weighted % of Clones 0 0.1250 0.2500 0.3750 0.5000 Al ph a Be pro ta teo G p b am rot ac m eo te ba ria Ep ap ct si ro lo t e np eob ria D el rot ac ta e t pr ob eria ot ac C eo te ya b rEFG no ac iaEFTurRNARecARpoB b teHSP70 Fi act ria rm e Ac ic ria tin ut es ob a C cte hl r or ia ob C i FB C hl o Major Phylogenetic Group Sp rof Metagenomic Phylotyping Sargasso Phylotypes iro lex i Fu cha D304: 66. 2004 ei so et no ba es co ct cc er Euus ia ry -T a hVenter et al., Science C rcherm re na aeous rc t ha a eo ta
    • 20. Metagenomics & Ecology
    • 21. Sequencing Technology
    • 22. Generation I: Manual Sanger
    • 23. Generation II: Automation
    • 24. Generation III: No clones
    • 25. Generation IV: ????
    • 26. Challenges and Outlook
    • 27. What’s Coming?• Sequencing • Speed up; cost down • Mini-sequencers with massive capacity • Automation of sample processing • Portable and remote systems • Massive databases• Computational changes • Clusters vs. RAM • Cloud computing • GPU acceleration
    • 28. Beyond Sequencing• Array methods should not be ignored • Bad gene array • Phylochips• High throughput/low cost approaches to characterizing other macromolecules • Proteomics • Metabolomics • Transcriptomics
    • 29. Challenge 1: Data overload• Major current issue is massive size of sequence data sets• Creates many new challenges not widely anticipated • Data transfer and storage • RAM limits for some processes • Databases overstretched
    • 30. Solutions?• Throw away data (analogous to CERN)• New algorithms to limit RAM needs• Complete automation of algorithms• Distributed data (e.g., Biotorrents)• Emphasis on standards and metadata
    • 31. Challenge 2: Short reads• Some specific challenges come from short reads• Key step in analysis of mixed communities is “binning”• Binning methods perform poorly on short reads • nucleotide composition • blast hits • phylogenetic analysis
    • 32. Solutions• Longer reads• More full length reference data • Reference is annotated • Reads are used to count• New algorithms • Phylogeny w/ short reads • Cobinning/combining data • New markers • Better HMM searches
    • 33. Challenge 3: Real time• New sequencing and array technologies allow almost real time data collection• Analysis generally not done in real time • e.g., metagenome annotation can take weeks to months • e.g., phylogenetics bottleneck • systems not set up for rapid, open sharing of results
    • 34. Solutions?• New automated high throughput methods • Must be updated continuously to deal with new data types • Need to be tested and verified• Rapid sharing of results • PLoS Currents 0.700 0.525 0.350 0.175 0 C eob ria Ba ac ria oi a s or es xi te ri le hl et te b e te de of no ct yc pr bac ya a om er o C ct te ct ot ro an ap Pl ta ph el D Al
    • 35. Challenge 4: Reference Data• Microbial diversity woefully undersampled• Greatly limits ability to • Identify new organisms from DNA fragments • Determine if organisms are out of “place” in some way compared to natural diversity • Perform reliable attribution/matching • Understand EIDs • Know what is “normal”
    • 36. Solution?• Systematic efforts to sample diversity• Some decent efforts in this regard in terms of diversity of known Category ABC pathogens• Much more needed
    • 37. Spatial Diversity of Isolates
    • 38. Genomic Diversity of Isolates Bacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. Based on tree from Pace NR, 2003.
    • 39. Gene tree ≠ Genome tree 16s WGT, 23SBadger et al. 2005 Int J System Evol Microbiol 55: 1021-1026.
    • 40. Phylogenetic Diversity• Phylogenetic diversity poorly sampled• GEBA project at DOE- JGI correcting this
    • 41. Metagenomic Diversity
    • 42. Challenge 5: Knowledge• Data collection is of course not enough• Need to be able to turn the data into knowledge• This is difficult to automate
    • 43. Solutions• More curators• Populate databases with experimental information not more predictions• Bioinformatics expansion• Better linking with ecology, building science, etc.
    • 44. Acknowledgements• $$$ • Sloan Foundation • DOE • NSF • GBMF • DARPA• People, places • DOE JGI: Eddy Rubin, Phil Hugenholtz et al. • UC Davis: Aaron Darling, Dongying Wu • Other: Jessica Green, Katie Pollard, Martin Wu, Tom Slezak, Jack Gilbert

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