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Microbiomes and Computational Medicine. Bryan A. White. Microbes rule the biosphere. People = 6.86 x 10 9 6,868,700,000 Bacteria in people (just GI Tract) 1.5 x 10 22 15,000,000,000,000,000,000,000 Stars = 10 24 1,000,000,000,000,000,000,000,000

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microbes rule the biosphere
Microbes rule the biosphere

People = 6.86 x 1096,868,700,000

Bacteria in people (just GI Tract) 1.5 x 1022 15,000,000,000,000,000,000,000

Stars = 1024 1,000,000,000,000,000,000,000,000

Bacteria on Planet = 1030 100,000,000,000,000,000,000,000,000,000

the human microbiome or the other human genome
The human microbiome or, the “other human genome”

1x1014 microbial cells (micrbiome)

1x1013 human cells

image courtesy of the NIH HMP website http://nihroadmap.nih.gov/hmp/

3x106 microbial genes (metagenome)

2.5x104 human genes


The Human Microbiome

Significant role in Health: Example in the Gastrointestinal tract

  • They foster development of the mucosal wall.
  • The development and maturation of the immune system is dependent on the presence of some members of the intestinal microbiota. Link to human health and disease.
  • Essential for the metabolism of certain compounds as well as xenobiotics.
  • Protection against epithelial cell injury.
  • Regulation of host fat storage.
  • Stimulation of intestinal angiogenesis.
consequences of a perturbed microbiome
Consequences of a Perturbed Microbiome?

Bowel Disorders

Pre-term birth

Kidney Stones

Peptic ulcers





nih human microbiome project
NIH Human Microbiome Project

2007 (The Jumpstart Component)

  • 200 reference genomes at 4 sequencing centers in the USA
  • Light and in-depth 16S rDNA sequencing
  • A total of 250 subjects to be recruited with an estimated 30 sites per subject

2009  (RFA)

  • Bring the entire reference collection up to 1000 genomes
  • Genomic sequencing of viruses and small eukaryotes
  • Metagenomic in depth sequencing on the same subjects

Other RFA’s for development of tools and technologies to handle the HMP data

Coordination with the International efforts

Total ~$157M in NIH funding

challenges with studying the human microbiome

Challenges with studying the human microbiome

Involvement of clinicians – time, IRB, etc.

Study groups – recruitment and maintenance

Sample availability and quantity – Right sample?

How do you get enough DNA?

Data analysis with heavy emphasis on variable

regions rather than full-length sequences

Interpretation of data across different groups, worldwide

Do we have enough reference genomes for scaffolding?

hmp metagenomics

Goal: Generate a healthy, well defined reference cohort of specimens that will be used to analyze the microbiome of healthy adults using metagenomics analysis and establish a reference data set.


  • Developed and executed study protocol
  • Screened 554 subjects
    • 300 enrollees; 150 females, 150 males
    • Sampled 279 enrollees 2X; sampled 100 enrollees 3X
  • Sampled body sites in healthy 18-40 year olds
    • 5 body sites-oral cavity, nares, skin, GI tract, and vagina
    • 15 sites sampled for males; 18 sites sampled for females
    • Collected 17,040 primary specimens
    • Processed at JCVI, Wash U, Broad and Baylor
healthy cohort body sites
“Healthy Cohort” Body Sites
  • Saliva
  • Tongue dorsum
  • Hard palate
  • Buccal mucosa
  • Keratinized (attached) gingiva
  • Palatine tonsils
  • Throat
  • Supragingival plaque
  • Subgingival plaque


  • Retroauricular crease, both ears (2)
  • Antecubitalfossa (inner elbow), both arms (2)
  • Anterior right and left nares (pooled)
  • Stool
  • Posterior fornix, vagina
  • Midpoint, vagina
  • Vaginal introitus





Slide courtesy of NHGRI


definition of some terms
Definition of Some Terms

Microbiome – The collective microbial community, a microbial census of “who is there”.

Metagenome– The total functional gene content, and therefore metabolic potential, a census of what genes are present in the microbiome

Phylotypes– A microbial type at the Class, Family or Genus. May be a species or even a strain

OTU - Operational taxonomic unit (97% Sequence Similarity of the 16S rDNAgene). A sequence based descriptor.

methods used to investigate microbiomes
Methods used to investigate microbiomes
  • Culture independent-based approaches – 16S rRNA and other phylogenetic marker surveys (who is there)
  • Limited whole genome sequencing (reference genomes) – Single cell and single molecule sequencing on the horizon
  • Subtractive hybridization studies (comparative genomics)
  • Stable Isotope Probing – Active populations
  • Metagenomic sequencing - functional gene content (i.e., metabolic potential)
  • Meta-transcriptomics – which genes are expressed
  • Metabolomics – what products are produced

Microbiome and Metagenomic Analysis









Figure 4. Rarefaction curves.

Wooley JC, Godzik A, Friedberg I (2010) A Primer on Metagenomics. PLoS Comput Biol 6(2): e1000667. doi:10.1371/journal.pcbi.1000667



Tree Generation

  • Phylogenetic tree types
  • Distance Matrix method
    • UPGMA
    • Neighbor joining
  • Character State method
    • Maximum likelihood

Phylogenetic tree?

  • A tree represents graphical relation between organisms, species, or genomic sequence
  • In Bioinformatics, it’s based on genomic sequence

What do they represent?

  • Root: origin of evolution
  • Leaves: current organisms, species, or genomic sequence
  • Branches: relationship between organisms, species, or genomic sequence
  • Branch length: evolutionary time
  • (in cladogram, it doesn't represent time)

Rooted / Unrooted trees

  • Rooted tree: directed to a unique node
    • (2 * number of leaves) - 1 nodes,
    • (2 * number of leaves) - 2 branches
  • Unrooted tree: shows the relatedness of the leaves without assuming ancestry at all
    • (2 * number of leaves) - 2 nodes
    • (2 * number of leaves) - 3 branches



More tree types used in bioinformatics (from cohen article)

  • Unrooted tree
  • Rooted tree
    • Cladograms: Branch length have no meaning
    • Phylograms: Branch length represent evolutionary change
    • Ultrametric: Branch length represent time, and the length from the root to the leaves are the same



How to construct a phylogenetic tree?

  • Step1:
  • Make a multiple alignment from base alignment or amino acid sequence (by using MUSCLE, BLAST, or other method)

How to construct a phylogenetic tree?

  • Step 2:
  • Check the multiple alignment if it reflects the evolutionary process.



How to construct a phylogenetic tree? cont

  • Step3:
  • Choose what method we are going to use and calculate the distance or use the result depending on the method
  • Step 4:
  • Verify the result statistically.

Distance Matrix methods

  • Calculate all the distance between leaves (taxa)
  • Based on the distance, construct a tree
  • Good for continuous characters
  • Not very accurate
  • Fastest method
    • UPGMA
    • Neighbor-joining


  • Abbreviation of “Unweighted Pair Group Method with Arithmetic Mean”
  • Originally developed for numeric taxonomy in 1958 by Sokal and Michener
  • Simplest algorithm for tree construction, so it's fast!

Downside of UPGMA

  • Assume molecular clock (assuming the evolutionary rate is approximately constant)
  • Clustering works only if the data is ultrametric
  • Doesn’t work the following case:

Neighbor-joining method

  • Developed in 1987 by Saitou and Nei
  • Works in a similar fashion to UPGMA
  • Still fast – works great for large dataset
  • Doesn’t require the data to be ultrametric
  • Great for largely varying evolutionary rates

Downside of Neighbor-joining

  • Generates only one possible tree
  • Generates only unrooted tree

Character state methods

  • Need discrete characters
    • Maximum likelihood
    • Maximum parsimony (will be covered by Kyle)

Maximum likelihood

  • Originally developed for statistics by Ronald Fisher between 1912 and 1922
  • Therefore, explicit statistical model
  • Uses all the data
  • Tends to outperform parsimony or distance matrix methods

How to construct a treewith Maximum likelihood?

  • Step 1:
  • Make all possible trees depending on the number of leaves
  • Step 2: Calculate likelihood of occurring with the given data
  • L(Tree) = probability of each tree.
    • optimizing branch length
    • generating tree topology
  • Step 3:
  • Pick the tree that have the highest likelihood.

Sounds really great?

  • Maximum likelihood is very expensive and extremely slow to compute

What microbial species are shared between sites and different species?

Dethlefsen et al. Nature 2007 vol. 449 (7164) pp. 811-818


In adults, each part of the body supports

a distinct microbial community.

With no apparent relationship with gender, age, weight, ethnicity or race.

“Structure, Function and Diversity of the Human Microbiome in an Adult Reference Population” The Human Microbiome Consortium.

HMP Consortium (2012)


Microbiome is acquired anew each generation.

2) Microbial succession over ~1-2 yrs.

Koenig et al. (2010)

Dominguez-Bello et al. (2010).

Palmer et al. (2007)

1) Infants obtain microbes from mother or environment.

Dominguez-Bello et al. PNAS | June 29, 2010 | vol. 107 | no. 26 | 11975

3) Microbiome becomes “adult-like” in ~1-2 yrs.

microbe microbe metabolic interactions can influence composition
Microbe:Microbe Metabolic Interactions Can Influence Composition









Co-abundance:Pearson correlations as a proxy for testing the interdependent structure of a microbiome

Abundance of OTU A





Pearsons correlation =

Abundance of OTU B

degree distribution not affected by natural plasticity
Degree Distribution Not Affected by Natural Plasticity

Slope = -1.2

Slope = -1.1

Slope = -1.3

Figure 4. Rarefaction curves.

Wooley JC, Godzik A, Friedberg I (2010) A Primer on Metagenomics. PLoS Comput Biol 6(2): e1000667. doi:10.1371/journal.pcbi.1000667



Biome specific signatures based on the functional gene content (Metagenome Wide Association Studies - MWAS)

Hugenholtz and Tyson. 2008. Nature 455:481.

Figure 2. Topics in the study of the human microbiome with outstanding computational biology challenges.

Gevers D, Pop M, Schloss PD, Huttenhower C (2012) Bioinformatics for the Human Microbiome Project. PLoS Comput Biol 8(11): e1002779. doi:10.1371/journal.pcbi.1002779


Figure 1. Environmental Shotgun Sequencing (ESS).

Wooley JC, Godzik A, Friedberg I (2010) A Primer on Metagenomics. PLoS Comput Biol 6(2): e1000667. doi:10.1371/journal.pcbi.1000667


Figure 3. Fragment assembly.

Wooley JC, Godzik A, Friedberg I (2010) A Primer on Metagenomics. PLoS Comput Biol 6(2): e1000667. doi:10.1371/journal.pcbi.1000667



M Arumugam et al.Nature000, 1-7 (2011) doi:10.1038/nature09944


Ravel et al. www.pnas.org/cgi/doi/10.1073/pnas.1002611107PNAS


INFORMATICS Tool development for data analysis: A distributed, scalable metagenomic analysis system using clouds

  • Distributed, cloud-based design for METAREP
  • Registry for metagenomic data at different institutes / labs, data queries run across all sites
  • Metagenomic pipelines on the cloud, no need for local data centers, benefit for smaller labs
  • Option to install pipelines on traditional data centers / clusters for security
  • JCVI Metagenomics Reports (METAREP)
  • data mining metagenomic datasets from HMP
  • rich web interface for analysis and comparison of annotated metagenomicsdatasets
  • high-performance search engine to query large data collections

Goll et al. Bioinformatics (2010) 26 (20): 2631-2632.