Your SlideShare is downloading. ×
0
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
[2013.10.29] albertsen genomics metagenomics
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

[2013.10.29] albertsen genomics metagenomics

992

Published on

Published in: Spiritual, Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
992
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
64
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Intro
  • Transcript

    • 1. Genomics and Metagenomics Mads Albertsen Introduction to community systems microbiology 2013 CENTER FOR MICROBIAL COMMUNITIES
    • 2. Agenda Genomics • • • • Introduction Assembly Validation Metabolic reconstruction (SM @ Thursday) Metagenomics • History • Pitfalls • Potentials CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 3. Introduction Genome = Parts list of a single genome CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 4. Introduction How to get from sequenced DNA to metabolic model? CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 5. Introduction Wet lab work Shear DNA Extract DNA Sequence Bioinformatics N Reads 50-500 bp Assembly Contigs 1kb – 100 kbp Scaffolding N Scaffolds Hopefully Mbp CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 6. Definitions > 1 kbp insert A sequenced piece of DNA Paired-end read Sequencing both ends of a short DNA fragment Mate-pair read Sequencing both ends of a long DNA fragment The length of the DNA fragment Contig 300-600 bp insert Read Insert size 50-500 bp A set of overlapping DNA segments that represents a consensus region of DNA Scaffold Contigs separated by gaps of known length Coverage The number of times a specific position in the genome is covered by reads length N 4x CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 7. Assembly Genome Fragment Sequence Paired-end reads Assemble Contig 1 Contig 10 Scaffold 1 Inspiration: http://goo.gl/VOZVVg Contig 19 Scaffold 2 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 8. Assembly Sequencing Genome (3.000.000 letters) Inspiration: http://goo.gl/VOZVVg Assembly Reads (50-500 letters each) Genome (3.000.000 letters) CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 9. Assembly “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity,.... “ Dickens, Charles. A Tale of Two Cities. 1859. London: Chapman Hall Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 10. Assembly Way too much data to make all vs. all comparison Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 11. Assembly Step 1: Convert reads into kmers Reads theageofwi sthebestof astheageof worstoftim Imesitwast the hea eag age geo eof ofw fwi sth the heb ebe bes est sto tof ast sth the hea eag age geo eof wor ors rst sto tof oft fti tim ime mes esi sit itw twa was ast Kmers (k = 3) Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 12. Assembly Step 2: Join kmers with n-1 overlap ast sth Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 the hea eag age geo eof CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 13. Assembly Step 2: Join kmers with n-1 overlap ast eag eag age age geo geo eof eof ofw ebe bes est sto sto tof tof wor ast hea hea heb sth sth the the the ors rst was fwi oft fti twa itw sit esi mes ime tim Do the same for all reads… Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 14. Assembly Step 3: Simplify the graph Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 15. Assembly Contigs It was the = ≠ incredulity age be st epoch times wor wisdom of foolishness belief “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity,.... “ Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 Dickens, Charles. A Tale of Two Cities. 1859. London: Chapman Hall CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 16. Assembly What is the minimum kmer size that results in a single contig? Kmer = 3 Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 17. Assembly What is the minimum kmer size that results in a single contig? Kmer = 3 Kmer = 10 Itwasthebestoftimesitwastheworstoftimesitwastheageofwisdomitwastheageoffoolishnessitwastheepochofbeliefitwastheepochofincredulity Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 18. Assembly Repeat = repeated DNA sequence that can’t be spanned by reads Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 19. Assembly Why not just increase the kmer size? Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 20. Assembly theageofwi Kmer = 3 the hea eag age geo eof ofw fwi Kmer = 9 theageofw heageofwi Kmers with errors = 2/2 Errors! Kmers with errors = 3/8 Example: http://goo.gl/nMWDAk Velvet example courtesy of J. Leipzig 2010 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 21. Validation I’ve assembled my 4.3 Mbp genome into 25 scaffolds with a N50 of 553 kbp. Is it a good assembly? CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 22. Validation N50 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 23. Validation Estimating repeat content CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 24. Validation 4 repeats in 2 copies each CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 25. Validation How could I close this genome? 4 repeats in 2 copies each CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 26. Validation How complete is the genome? CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 27. Validation Survey of essential single copy genes across sequenced phyla Genes 100-106 Essential single copy genes (can also be used to identify contamination) Phyla CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 28. Validation Inspect the assembly CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 29. Validation • N50 does not make much sense • Repeat content versus the number of scaffolds • Calculate the percentage of essential genes • Inspect the assembly CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 30. Metabolic reconstruction 4.3 Mbp genome … and so what? (@Thursday) CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 31. Metagenomics Mads Albertsen Introduction to community systems microbiology 2013 CENTER FOR MICROBIAL COMMUNITIES
    • 32. Introduction Genome = Parts list of a single genome CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 33. Introduction Photo: D. Kunkel; color, E. Latypova Metagenome = Parts list of the community CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 34. Introduction ”...functional analysis of the collective genomes of soil microflora, which we term the metagenome of the soil.” - J. Handelsman et al., 1998 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 35. Introduction ”...functional analysis of the collective genomes of soil microflora, which we term the metagenome of the soil.” - J. Handelsman et al., 1998 PubMed: metagenom*[Title/Abstract] CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 36. Introduction ”...functional analysis of the collective genomes of soil microflora, which we term the metagenome of the soil.” - J. Handelsman et al., 1998 Sequencing costs PubMed: metagenom*[Title/Abstract] http://www.genome.gov/sequencingcosts/ CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 37. Introduction Metagenomics ≠ Amplicon sequencing CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 38. Sequencing and assembly ≈3.000.000 bp pr. genome 150 bp reads ≈1000 bp+ contigs CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 39. Assigning information Function Contigs Databases Binning Taxonomy CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 40. What have metagenomics been used for? Exploration Rusch et al., 2007 Plos Biology • 6.3 Gbp of sequence (2x Human genomes, 2000 x Bacterial genomes) • Most sequences were novel compared to the databases Qin et al., 2010 Nature • • • • 127 Human gut metagenomes 600 Gbp sequence (200 x Human genomes) 3.3 million genes identified Minimal gut metagenome definded CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 41. What have metagenomics been used for? Comparative Dinsdale et al., 2008 Nature • A characteristic microbial fingerprint for each of the nine different ecosystem types Specific functions Hess et al., 2011 Science • Identified 27.755 putative carbohydrate-active genes from a cow rumen metagenome • Expressed 90 candidates of which 57% had enzymatic activity against cellulosic substrates CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 42. What have metagenomics been used for? Extracting genomes Garcia Martin et al., 2006 Nat. Biotechnol. Albertsen et al., 2013 Nat. Biotechnol. • Genome extraction from low complexity metagenome • Candidatus Accumulibacter phosphatis • The first genome of a polyphosphate accumulating organism (PAO) with a major role en enhanced biological phosphorus removal • Genome extraction of low abundant species (< 0.1%) from metagenomes • First complete TM7 genome • Access to genomes of the ”uncultured majority” CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 43. Pitfalls CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 44. Metagenomics made easy Great resources – but use with care CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 45. MG-RAST example Contigs CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 46. Dataset overview CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 47. Taxonomy and Function overview Taxonomy Function CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 48. Compare with other samples Samples Functional categories CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 49. Pitfalls You always get billions of data! CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 50. Pitfalls Is your DNA extraction OK? ... and the samples you want to compare with? Did you sequence enough? Did you know the GC bias of your protocol? Did you normalize for sequencing depth? Did you use the same sequencing platform? Assembly = data not quantitative! Are you comparing assembled data with reads? CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 51. Databases Contigs Databases Annotated metagenome ...you only see what is in the database CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 52. What is in the databases? Finshed Genomes in IMG Vs. Greengenes 16S rRNA database Genomes 16S Phyla 29 90 Class 46 249 Order 100 405 Species 1268 99322* *97% clustering Note: only including 1 strain pr. species CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 53. MG-RAST example Contigs 650.000 EBPR proteins with taxonomy assigned How similar are they to the genomes in the database? CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 54. Sludge microbes vs. Database genomes 650.000 EBPR proteins Note: not abundance weighted CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 55. Sludge microbes vs. Database genomes 650.000 EBPR proteins 1.260.000 Human gut Qin et al., 2010 Nature RAST ID: 4448044.3 Note: not abundance weighted CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 56. Sludge microbes vs. Database genomes The 7 genera with most EBPR proteins assigned CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 57. Effect of missing genomes What is the effect of not having closely related genomes in the database? 1. Remove a genome from the database 2. Search the removed genome against the database CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 58. Effect of missing genomes Accumulibacter phosphatis blastp 4326 proteins Best hit Related genomes Bacteria 1268 Proteobacteria 564 Betaproteobacteria 84 Rhodocyclales 5 Rhodocyclaceae 5 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 59. Effect of missing genomes Accumulibacter phosphatis blastp Azoarcus 4326 proteins Best hit Related genomes Bacteria 1268 Proteobacteria 564 Betaproteobacteria 84 Rhodocyclales 5 Rhodocyclaceae 5 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 60. Effect of missing genomes Accumulibacter phosphatis blastp 4326 proteins MEGAN LCA Lowest common ancester (LCA) approach: Hit 1: Beta-proteobacteria 80% ID Hit 2: Gamma-proteobacteria 79% ID Hit 3: Actinobacteria 59% ID Assigned to Proteobacteria Related genomes Bacteria 1268 Proteobacteria 564 Betaproteobacteria 84 Rhodocyclales 5 Rhodocyclaceae 5 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 61. Effect of missing genomes Accumulibacter phosphatis blastp 4326 proteins MEGAN LCA Lowest common ancester (LCA) approach: Hit 1: Beta-proteobacteria 80% ID Hit 2: Gamma-proteobacteria 79% ID Hit 3: Actinobacteria 59% ID Bacteria 325 Beta- 853 Genus 4326 proteins: • 27% correctly classified on genus level • 54% not assigned the correct class • 101 genera identified Rhodocyclaceae 1149 Assigned to Proteobacteria Proteobacteria 860 Related genomes Bacteria 1268 Proteobacteria 564 Betaproteobacteria 84 Rhodocyclales 5 Rhodocyclaceae 5 No hits 261 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 62. Effect of missing genomes Phylum Nitrospira defluvii blastp 4268 proteins: • 1% correctly classified on phylum level MEGAN LCA Related genomes Bacteria Nitrospirae 1268 3 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 63. Effect of missing genomes Nitrospira defluvii blastp MEGAN LCA + KEGG What about function? Related genomes Bacteria Nitrospirae 1268 3 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 64. Effect of missing genomes Nitrospira defluvii blastp MEGAN LCA + KEGG Related genomes Bacteria Nitrospirae 1268 3 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 65. Effect of missing genomes Nitrospira defluvii blastp MEGAN LCA + KEGG Related genomes Bacteria Nitrospirae 1268 3 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 66. Implication of missing genomes Function A Function B Function C Function D CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 67. Pitfalls You always get billions of data! CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 68. Metagenomics ”If you want to understand the ecosystem you need to understand the individual species in the ecosystem” CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 69. Metagenomics Lion + Eagle ≠ Flying Lion CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 70. Potentials CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 71. Who - when, where and why? CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 72. How do we get the genomes? Culturing Few microorganisms can be easily cultured (<<5%) Microorganisms needs to be studied in their environment CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 73. How do we get the genomes? What you think you study What you actually study CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 74. How do we get the genomes? Culturing Few microorganisms can be easily cultured (<<5%) Microorganisms needs to be studied in their environment Single cell genomics Only routinely performed in specialized labs Very incomplete genomes (mean 40%, range 10-90%) https://www.bigelow.org/ CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 75. How do we get the genomes? Culturing Few microorganisms can be easily cultured (<<5%) Microorganisms needs to be studied in their environment Single cell genomics Only routinely performed in specialized labs Very incomplete genomes (mean 40%, range 10-90%) https://www.bigelow.org/ Metagenomics CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 76. Metagenomics Reads DNA extraction Sequencing 100++ Abundant species (≈3 Mbp each) 100-150 bp Assembly Why not full genomes? Contigs 1000+ bp CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 77. Metagenomics Reads DNA extraction Sequencing 100++ Abundant species (≈3 Mbp each) 100-150 bp Assembly Why not full genomes? Contigs 1000+ bp 1. Micro-diversity 2. Separation of genomes (Binning) CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 78. Extracting genomes Not 1 strain AAAAAAAAAAAAAA AAAAAAAAATAAAA AAAAAAAAACAAAA What you get TAAAA Assembly AAAAAAAAA AAAAA CAAAA Many closely related strains CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 79. Extracting genomes High micro-diversity Low micro-diversity Short term enrichment CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 80. Metagenomics Reads DNA extraction Sequencing 100++ Abundant species (≈3 Mbp each) 100-150 bp Assembly Why not full genomes? Contigs 1000+ bp 1. Micro-diversity 2. Separation of genomes (Binning) CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 81. Binning PhD student Genomic signatures: - GC / Codon usage - Tetranucleotide frequency + statistical method ”Binning” Complex sample CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 82. Binning PhD student Genomic signatures: - GC / Codon usage - Tetranucleotide frequency + statistical method ”Binning” Complex sample Problems: - Short pieces of sequence (1-10kbp) - Local sequence divergence CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 83. Binning Abundance Abundance Sequence composition-independent binning Sample 1 Sample 2 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 84. Binning Abundance Abundance Sequence composition-independent binning Sample 2 Abundance Sample 2 Sample 1 Abundance Sample 1 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 85. Binning 1. Reduce micro-diversity Abundance Sample 2 2. Use multiple related samples Abundance Sample 1 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 86. Binning 1. Reduce micro-diversity Abundance Sample 2 2. Use multiple related samples Abundance Sample 2 Abundance Sample 1 Abundance Sample 1 CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 87. Binning • Nitrospira enrichment running for years • 3 dominant species • No micro-diversity H. Daims & C. Dorninger, DOME, University of Vienna CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 88. SBR reactor Full-scale EBPR plant Short term enrichment Days Albertsen et al., 2013 Nat. Biotech. 1. Reduction of (micro)-diversity CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 89. SBR reactor Full-scale EBPR plant Short term enrichment 2. Two different DNA extraction methods Albertsen et al., 2013 Nat. Biotech. CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 90. Colored using a set of 100 phylogenetic marker genes Albertsen et al., 2013 Nat. Biotech. CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 91. Colored using a set of 100 phylogenetic marker genes TM7-1 (1.6%) TM7-2 (0.7%) TM7-3 (0.2%) TM7-4 (0.06%) Albertsen et al., 2013 Nat. Biotech. CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 92. Colored using a set of 100 phylogenetic marker genes Zoom on target TM7-2 (0.7%) Albertsen et al., 2013 Nat. Biotech. CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 93. Colored using a set of 100 phylogenetic marker genes Zoom on target PCA on genomic signatures PC2 TM7-2 (0.7%) TM7-2 PC1 Albertsen et al., 2013 Nat. Biotech. CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 94. Colored using a set of 100 phylogenetic marker genes Candidatus Saccharimonas aalborgensis TM7-1 (1.6%) Candidate phylum TM7 Saccharibacteria Albertsen et al., 2013 Nat. Biotech. CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 95. Genome validation Assembly inspection Essential single copy genes Genes (HMM models) Phyla Albertsen et al., 2013 Nat. Biotech. CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 96. Multi-metagenome http://madsalbertsen.github.io/multi-metagenome/ Short: goo.gl/0ctA3 • • • • • Albertsen et al., 2013 Nat. Biotech. Guides Workflow scripts Example data All the code Reccomendations CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 97. Complex samples ...add more samples! S. M. Karst, AAU CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 98. It’s just a potential! ..and a poorly translated description of it. CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 99. Understanding ecosystems Metabolites Meta-bolomics Proteins Extraction mRNA Meta-proteomics Meta-transcriptomics DNA In Situ methods Community structure Microbial functions Meta-genomics Microbial needs P-Removal: N-Removal: -Removal: Foaming: Ethanol production: CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY
    • 100. Questions? ma@bio.aau.dk @MadsAlbertsen85 MadsAlbertsen Per H. Nielsen Simon J. McIllroy Søren M. Karst EB group University of Queensland C. Dorringer H. Daims M. Wagner University of Vienna G.W. Tyson P. Hugenholtz CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

    ×