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Global variation of human gut metagenomes
1. Ruminococcus
2. Bifidobacterium
3. Peptostreptococcus
4. Staphylococcus
5. Lactobacillus
6. Acidaminococcus
7. Fusobacterium
8. Eubacterium
9. Clostridium
10. Coprococcus
11. Escherichia
12. Butyrivibrio
13. Bacteroides
14. Brachyspira
Enterotypes of the human gut microbiome
NATURE
20 April 2011
39 individuals :
(22 faecal metagenomes of individuals from 4 nations) + (published 17 individuals metagenome data from 2 nations)
reads from 39 individuals (6 nations, sanger seq + pyro-seq) => trimming(quality, map to hg18) => assembly & gene prediction by SMASH pipeline
selective pressure from host leads homeostasis, most of species occur in low abundance.
study the presence of abundant functions shared by several low-abundance species could reveal the survival stratigies in human gut.
example : Escherichia's FimA, PapC ; associated with bacterial pilus assembly
map metagenomic reads from 39 samples to 1,511 reference genomes (NCBI, Human Microbiome Project, MetaHIT consortium; extract 16S rRNA gene and assign taxonomy by RDP Classifier)
Trim (lower than 15(phred quality), shorter than 300 bp, and then use makeClip)
Trimming
remove reads mapped to hg18 using BLAT
assemble iteratively using SMASH, predict orf by GeneMark
Assembly &
prediction
1,511 reference genomes are used(NCBI, Human Microbiome Project, MetaHIT consortium; extract 16S rRNA gene and assign taxonomy by RDP Classifier)
Phylogenetic
annotation
map reads to reference genome by WU-BLAST(BLASTN), assign taxonomy of top hit (similarity : >65% for phylum, >85% for genus, length : >75bp & >80% of the read length)
transfer paired-end reads taxonomy to fragment taxonomy => count fragment of reference genome => normalized fragment count by dividing genome size (unassigned fragments was normalized by average genome size )
Abundant functions from low-abundance microbes
twin sample : classify 1,119,519 reads from 154 individuals using RDP Classifier (>= 200bp, >=0.5 confidence score), normalize # of read by average 16S gene copy number in genomes belonging to each genus
Phylogenetic analysis of
external database
danish sample : quality trim & filter 85 danish individuals using FASTX toolkit and map by SOAP
estimate abundance of each predicted gene as below equation (g: gene, r : read, thus gene on a singleton read have an abundance 1)
assign orthologous group in eggNOG to predicted protein by BLASTP, caculate abundance as below (k: eggNOG reference protein, g :predicted gene)
Functional
annotation
align predicted proteins to proteins from KEGG, assign KO (best hit with >= 1 HSP scoring over 60 bits), caculate abundance of each KO, module, pathway as above
cluster the abundance profiles by PAM(Partitioning around medoids) algorithm(this support any arbitary distance measure)
normalize each abundance(genus, OG)to generate abundance distribution , use Jensen-Shannon divergence(JSD) for clustering (see http://graphy21.blogspot.com/search?q=divergence)
Clustering
Detection of entero types, cross national clusters
assess optimal number of clusters using the Calinski-Harabasz(CH) index, cluster validation by silhouette validation technique and cluster similarity by Rand index R
Variaion between enterotypes
Functional biomarkers for host properties