Eisen #microBEnet #IndoorAir2011

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

Outline• Introduction• Sequencing in microbial studies• Sequencing technologies• Current and future issues

microBEnet• / http://microbe.net

microBEnet

MICROBES

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

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

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

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

Era II: rRNA in environment

Great Plate Count Anomaly Culturing Microscope Count Count

Great Plate Count Anomaly Culturing Microscope Count <<<< Count

Great Plate Count Anomaly DNA Culturing Microscope Count <<<< Count

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

Era II: rRNA in environmentThe Hidden Majority Richness estimates Hugenholtz 2002 Bohannan and Hughes 2003

Era III: Genome Sequencing Genomes Online Fleischmann et al. 1995 Science 269:496-512

Lateral Gene TransferPerna et al. 2003

Era IV: Genomes in Environment shotgun sequenceMetagenomics

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

Metagenomics & Ecology

Sequencing Technology

Generation I: Manual Sanger

Generation II: Automation

Generation III: No clones

Generation IV: ????

Challenges and Outlook

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

Beyond Sequencing• Array methods should not be ignored • Bad gene array • Phylochips• High throughput/low cost approaches to characterizing other macromolecules • Proteomics • Metabolomics • Transcriptomics

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

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

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

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

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

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

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”

Solution?• Systematic efforts to sample diversity• Some decent efforts in this regard in terms of diversity of known Category ABC pathogens• Much more needed

Spatial Diversity of Isolates

Genomic Diversity of Isolates Bacteria Archaea Eukaryotes Figure from Barton, Eisen et al. “Evolution”, CSHL Press. Based on tree from Pace NR, 2003.

Gene tree ≠ Genome tree 16s WGT, 23SBadger et al. 2005 Int J System Evol Microbiol 55: 1021-1026.

Phylogenetic Diversity• Phylogenetic diversity poorly sampled• GEBA project at DOE- JGI correcting this

Metagenomic Diversity

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

Solutions• More curators• Populate databases with experimental information not more predictions• Bioinformatics expansion• Better linking with ecology, building science, etc.

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

Eisen #microBEnet #IndoorAir2011

by Jonathan Eisen on Jun 09, 2011

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