quantitative metagenomics

Quantitative Metagenomics

Lea Benedicte Skov Hansen, PhD NGS Course 13th of June 2016

13/06/2016 Quantitative Metagenomics 2 DTU Sytems Biology, Technical University of Denmark

Exercise


Exercise

•  Metagenome assembly –  Preassembled with two methods:

•  Soap •  Meta Velvet

–  Contig coverage –  Assembly statistics


Exercise




•  Gene prediction –  Prodigal –  Gene clustering based on similarity –  Gene catalogue


Exercise





•  Gene abundance matrix –  Align reads to gene catalogue with bwa –  Count number of reads mapping to a

gene – samtools


Exercise 2





•  Gene abundance matrix –  Align reads to gene catalogue with bwa –  Count number of reads mapping to a

gene – samtools •  Taxonomic annotation of gene catalogue

–  Blast gene catalogue •  NCBI Bacterial Genomes •  373 additional genomes

–  Rearranging gene abundance to taxonomic abundance


Ecology

Is the scientific analysis and study of interactions among organisms and their environment, such as the interactions organisms have with each other and with their abiotic environment.


Nothing new – except the technology

Classical measures • Abundance • Diversity • Richness


Abundance (Counts)


Abundance (Count)

Lion 64 Zebra 128 Giraffe 64 leopard 64 rhinoceros 64 hippopotamus 128 gazelle 128 elephant 64 monkey 9


Richness

Lion 64 Zebra 128 Giraffe 64 Leopard 64 Rhinoceros 64 Hippopotamus 128 Gazelle 128 Elephant 64 Monkey 9

9 observed species


Richness

Rarefaction curves


Richness

Lion 1 Zebra 2 Giraffe 1 Leopard 1 Rhinoceros 1 Hippopotamus 2 Gazelle 2 Elephant 1 Monkey 0

Species richness estimators: Chao1 index = Sobs + f12/(2f2) Sobs = observed species f1 = species observed once f2 = species observed twice

8 observed species

Chao1 index = 8 + 52/(2*3) = 12.17


Evenness

Lion 1 1 Zebra 2 1 Giraffe 1 8 Leopard 1 1 Rhinoceros 1 1 Hippopotamus 2 1 Gazelle 2 1 Elephant 1 1 Monkey 0 0


Alpha Diversity

Richness Evenness Richness: s1 = s2



Alpha Diversity

Richness: s1 = s2 Evenness s1 ≠ s2



Alpha Diversity

Shannon index

H = Σ i=1

R

pi ln pi

H = Shannon index p = count of species i / total counts R = observed species



Alpha Diversity


Shannon index

H = Σ i=1

R

pi ln pi

Hs1 = 2.02 Hs2 = 1.60

p1 = p2 = p3 .. pR

H = ln(R) = 2.08


Sample Sizes


Sample Sizes

Accounting for different sample sizes:

•  Normalize to sample size

•  Rarefy samples

•  Statistical model of sample variance


Sample Sizes

Lion 64 1 Zebra 128 2 Giraffe 64 1 Leopard 64 1 Rhinoceros 64 1 Hippopotamus 128 2 Gazelle 128 2 Elephant 64 1 Monkey 9 0 Total 713 11

Normalize to library size: Norm = ni/ntot

Lion 8.98 9.09 Zebra 17.95 18.18 Giraffe 8.98 9.09 Leopard 8.98 9.09 Rhinoceros 8.98 9.09 Hippopotamus 17.95 18.18 Gazelle 17.95 18.18 Elephant 8.98 9.09 Monkey 1.26 0 Total 100 100


Sample Sizes

Rarefying to smaller library size:




Sample sizes

Normalization and downsizing does not account for heteroscedasticity! Statistically modeled variance: •  DESeq2 •  EdgeR


Beta-Diversity

Diversity between communities!


Beta-Diversity

Lion 0 2 Zebra 3 2 Giraffe 0 4 Leopard 0 2 Rhinoceros 1 2 Hippodrome 4 0 Gazelle 0 1 Elephant 1 0 Total 9 13


Beta-Diversity Lion 0 2 Zebra 3 2 Giraffe 0 4 Leopard 0 2 Rhinoceros 1 2 Hippodrome 4 0 Gazelle 0 1 Elephant 1 0 Total 9 13

Bray-Curtis dissimilarity metric

Bij = 1 - 2Cij / (Si + Sj) C = sum of the lowest count of common species S = total count of the sample Bs1s2 = 1 – 2*3 / 22 = 0.73 - Dissimilar C = 3 Ss1 + Ss2 = 22

0 ≤ B ≤ 1


Beta-Diversity

Other similarity metrics •  Eucledian distance

•  Jensen Shannon Distance

M=(x+y)/2


Beta-Diversity

Distance matrix


Diversity - example


Hands on!

quantitative metagenomics

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