1. The Original Question:
What is the better way to select genomes to sequence so we can maximize gene discovery given our limited resources?
Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. Curr Opin Genet Dev Dec;15(6): Epub 2005 Sep 26

2. GEBA figure 2

3. Next Step:
If we draw gene discovery curve for all the available taxonomy levels (nodes)
1. What is the relationships between gene families number (gene space) and genome number or the phylogenetic diversity (PD) at the taxonomy level?
2. Can we estimate the gene space of all microbial on the planet?

4. Next Step:
If we draw gene discovery curve for all the available taxonomy levels (nodes)
1. What is the relationships between gene families number (gene space) and genome number or the phylogenetic diversity (PD) at the taxonomy level?
2. Can we estimate the gene space of all microbial on the planet?

5. We need:
1.Build gene families for all available genomes (Guillaume Jospin)
2.Build 16S trees for all greengenes 16S and get the subtree for the sequenced genomes
IMG 1804 genomes with 6529,377genes
281,549 families, 939,289 singletons

6. Tree Building for greengenes 16S Sequences
510,111 sequences from greengenes
Reduce Redundancy (99% identity cutoff over 95% length span)
162,365 sequences
Build FastTree

7. Genomes: 1804 Tree: ( 9 genomes are excluded because
16S sequences cannot be found in greengenes
they have <500 genes for incompleted genomes)
Tree:
(pruned 16S tree only include 1804 IMG organisms)

8. Genomes: 1804 Tree: ( 9 genomes are excluded because
16S sequences cannot be found in greengenes
they have <500 genes for incompleted genomes)
Tree:
(pruned 16S tree only include 1804 IMG organisms)

9. Family number
Sample gene families (exclude singletons) from the 1804 genomes
Genome Number

10. For each node from the 16S tree
the genome number is N for a given node
Pick a number at a even internal from 3 to N [interval=(N-3)/200]
For example: a node have 600 nodes, I sample the following numbers
If genomes (3, 6, 9,..,597, 600)
For each X, randomly pick X genomes from the N genome pool, and calculate the
PD of the subtree and the gene family number (repeat this step 100 times, to get the
Average PD and family number for each X)

11. Family number
Genome Number

12. Family number
Genome Number

13. Family/genome

14. Random sampling protocol
Pick a random number N ( )
Select N genomes from the 16S tree, and keep only the subtree
Pick X numbers at a even internal from 3 to N [interval=(N-3)/200]
For each X, randomly pick X genomes from the N genome pool,
and calculate the PD of the subtree and the gene family number
(repeat this step 100 times, to get the
Average PD and family number for each X)
Repeat the above step 1000 times

15. Family number
1000 random genomes from the 1804 genomes
Genome Number

16. 117.32 family/genome x 162,365 = 19,0480,661 families

17. PD
Genome Number
Family number
Genome Number

18. Family number PD

19. Family/PD PD

20. Family number
1000 random genomes from the 1804 genomes PD

21. family/PD x 3644 = 12,340,260 families
Family/PD PD

22. Total PD of the greengenes 16S: 3644.21
According to random sample curve of the root node:
When PD is 225, the slope (family/PD) reaches 0 (regression 0.75)
When PD increase, family space stays constant ???

23. Singletons: To count or not to count?
281,549 families, 939,289 singletons
Not to count: Many singletons are results of false prediction
To count: The singletons here are results of single E value cutoff (not true singles)

24. Only sample 972 finished genomes and count all the singletons
Family number
Genome Number

25. Only sample 972 finished genomes and count all the singletons
Family number
Genome Number

26. PD
Genome Number

27. Family number PD

28. Family number
Genome Number

29. PD
Genome Number

30. Family number PD

31. Family number/Genome
Total PD of subtree

32. Family number/PD
Total PD of subtree

33. Family number/PD

34. Family comparison between Genome A and B
Unique_family_for_B
Overlap
Unique_for_A
Family Distance=(UA+UB)/(UA+UB+Overlap)

35. Family Distance
PD between two genomes

36. Family Contribution of ONE genome vs its PD contribution
Randomly select 100 genomes from the tree
Randomly select 1 genome from the tree, and calculation its family and PD contribution, do it 800 times
Divide the 800 datapoint into categories according to PD contribution
[0-0.1) [ ) [ ) ....
For each category, calculated the average family contribution and the
standard deviation
Sample 100 times

37. PD contribution of one additional genome
Family Contribution of one additional genome

38. PD contribution of one additional genome
Family Contribution of one additional genome

39. Family Contribution of ONE genome vs its PD contribution
Calculate one genome's contribution to a pool of genomes(10, )

40. PD contribution of one additional genome
Genome Number
Family Number PD 10
18595±2551
3.74±0.63 20
32552±2579
6.08±0.80 50
67454±5068
11.20±0.85
100
116192±5768
17.17±0.98
200
197728±6778
25.30±1.11
500
393797±7616
41.07±1.05
PD contribution of one additional genome
Family Contribution of one additional genome

41. Novel families contributed by one genome are related its PD contribution(non-lineal)
Novel families contributed by one genome are related the PD and families of
existing genome pool
Random samples from a genome pool can predict the PD and genome family of the pool
(chao estimator like approach can be applied)
Current genome sequencing data cannot accurately predict the microbial protein spaces
1. it is not a random sample
2. it is at early stage can result in over-estimation even if we have a random sample
(lineal stage of Chao estimater)

42. Greengenes 16S rRNA tree
Total PD
Genome Number

44. Gene Family Building for All the Sequenced Genomes from IMG
Protein encoding genes from all the genomes
All vs all Blastp
(1e-10 covering 80% of both query and hit)
Links between genes
MCL clustering
Gene families
IMG 1813 genomes with 6,530,517 genes
BLAST and MCL clustering would overwhelm our computer resources

45. Identify Universally Distributed Families
100 representative genomes
(85 bacterial and 15 archaeal genomes)
313,139 protein encoded genes
28,710,015 links
17232 gene families
Universality is the percent of genomes a family covers
links
genes
families
Percentage
Universality cutoffs

46. Guillaume Jospin from Eisen's Lab
502 families with universality >= 70 (70%)
502 HMM profiles
Screen and build the 502 families for the 1813 IMG dataset
Gene family building pipeline
1,219,828 families (281,562 if exclude singletons)