Eisen #microBEnet #IndoorAir2011
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Eisen #microBEnet #IndoorAir2011
- 1. Microbial Ecology Indoor Microbial Ecology (DNA Sequencing Focus) Indoor Air 2011 Workshop 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
- 6. MICROBES
- 7. 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
- 8. 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
- 9. 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
- 10. 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 Eukaryotes Barton, Eisen et al. “Evolution”, CSHL Press. 2007. Based on tree from Pace 1997 Science 276:734-740
- 11. Era II: rRNA in environment
- 12. Great Plate Count Anomaly Culturing Microscope Count Count
- 13. Great Plate Count Anomaly Culturing Microscope Count <<<< Count
- 14. Great Plate Count Anomaly DNA Culturing Microscope Count <<<< Count
- 15. 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
- 16. Era II: rRNA in environment The Hidden Majority Richness estimates Hugenholtz 2002 Bohannan and Hughes 2003
- 17. Era III: Genome Sequencing Genomes Online Fleischmann et al. 1995 Science 269:496-512
- 18. Lateral Gene Transfer Perna et al. 2003
- 19. Era IV: Genomes in Environment shotgun sequence Metagenomics
- 20. 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 r EFG no ac ia EFTu rRNA RecA RpoB b te HSP70 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 D 304: 66. 2004 ei so et no ba es co ct cc er Euus ia ry -T a h Venter et al., Science C rcherm re na aeous rc t ha a eo ta
- 23. Generation I: Manual Sanger
- 25. Generation III: No clones
- 28. 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
- 29. Beyond Sequencing • Array methods should not be ignored • Bad gene array • Phylochips • High throughput/low cost approaches to characterizing other macromolecules • Proteomics • Metabolomics • Transcriptomics
- 30. 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
- 31. 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
- 32. 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
- 33. 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
- 34. 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
- 35. 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
- 36. 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”
- 37. Solution? • Systematic efforts to sample diversity • Some decent efforts in this regard in terms of diversity of known Category ABC pathogens • Much more needed
- 38. Spatial Diversity of Isolates
- 39. Genomic Diversity of Isolates Bacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. Based on tree from Pace NR, 2003.
- 40. Gene tree ≠ Genome tree 16s WGT, 23S Badger et al. 2005 Int J System Evol Microbiol 55: 1021-1026.
- 41. Phylogenetic Diversity • Phylogenetic diversity poorly sampled • GEBA project at DOE- JGI correcting this
- 43. 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
- 44. Solutions • More curators • Populate databases with experimental information not more predictions • Bioinformatics expansion • Better linking with ecology, building science, etc.
- 45. 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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- 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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