Microbial Phylogenomics (EVE161) at UC Davis Spring 2016. Co-taught by Jonathan Eisen and Holly Ganz. Class 17: Genomes from Uncultured

3. What was Sampled?

4. What was Sampled?

6. How to Sample?
• Sample “natural” system or modified system?

9. Comments on Sampling?

10. What Obtained from Samples?
• What did they collect from the samples and why?

11. What Obtained from Samples?

12. Comments on What Done
with Samples?

13. Binning
• Why bin metagenomies?
• How did they bin metagenomes?
• How did they test their bins?

14. Binning

16. How do ESOMs work?

20. Self Organized Maps
To analyze the distribution of genome signatures among and between populations, all contigs
and assembled genomes were fragmented into 5-kb pieces, then pooled and clustered by self-
organizing map (SOM) [58] based on tetranucleotide frequency distributions (Figure 1; see
Materials and methods for details). The SOM is an unsupervised neural network algorithm that
clusters multidimensional data and represents it on a two-dimensional map. SOMs of
tetranucleotide frequencies have been used previously to successfully bin sequence fragments
from isolate genomes [33, 59] and some environmental samples [46, 48, 52]. We utilized an
implementation of the SOM, emergent SOM (ESOM), which is distinguished by its use of large
borderless maps (for example, thousands of neurons) and visualization of underlying distance
structure with background topography [60]. This visualization, where map 'elevation' represents
the distance in tetranucleotide frequency between data points, is referred to as the U-Matrix
[60]. Thus, genomic clusters were visualized not only by the cohesive clustering of fragments
from each genome, but also by distance structure whereby barriers between clusters represent
the large differences in genome signatures between genomes relative to those within genomes
(Figure 3). This visualization of genomic clustering was used to evaluate the accuracy of the
binning based on assembled genomes and to identify novel regions of sequence signature
space.

21. ESOMs
Contigs were clustered by tetranucleotide frequency utilizing Databionics ESOM Tools [94]. The
input for tetra-ESOM was a 136-dimensional vector (representing the frequencies of the 136
unique reverse complement tetranucleotide pairs, normalized for contig length) for each contig/
window. These raw frequencies were transformed with the 'Robust ZT' option built into
Databionics ESOM Tools, which normalizes the data using robust estimates of mean and
variance. Data were permuted before each run to avoid errors due to sampling order. Maps
were toroidal (borderless) with Euclidean grid distance and dimensions scaled from the default
map size (50 × 82) as a function of the number of data points, to a ratio of approximately 5.5
map nodes per data point. For example, a typical clustering with approximately 7,500 data
points was run on map with dimensions 155 × 255. Training was conducted with the K-Batch
algorithm (k = 0.15%) for 20 training epochs. The standard best match search method was
used with local best match search radius of 8. Other training parameters were as follows:
Gaussian weight initialization method; Euclidean data space function; starting value for training
radius of 50 with linear cooling to 1; starting value for learning rate of 0.5 with linear cooling to
0.1; Gaussian kernel function.

22. Comments on Binning?

23. Binning

24. CPR

25. How did they name new
phyla?

26. CPR

27. CPR

29. CPR Genomes

30. Comments on Phyla and
Genomes?

31. CPR Size
• Why does size matter here?

32. CPR are Small

33. Comments on Size?

34. Unusual rRNAs 1
• Why so much focus on unusual rRNAs?

35. Unusual rRNAs 1

36. Insertions Diverse

38. Unusual rRNAs 1

39. Insertion Sites

40. Unusual rRNAs 2

41. Insertion Locations by Phyla

43. Unusual rRNAs 2

44. rRNA Copy = 1 in CPR

45. Comments on rRNAs?

46. Metatranscriptomes and rRNAs
• What is metatranscriptomics?
• Why do it?

47. Metatranscriptomes and rRNAs

49. Comments on
Metatranscriptomes?

50. rRNA insertions

51. 23S rRNA insertions

52. Metagenomics and rRNA
• Why examine rRNAs in metagenomic data not PCR
data?
• What can you learn from this?

53. Metagenomics and rRNA

54. Universal Primers?
a, b, PrimerProspector was used
to assess the ability of primers
515F and 806R to bind a non-
redundant set of assembled near-
complete 16S rRNA gene
sequences (clustered at 97%
sequence identity). The
percentage of sequences that
would be amplified by these
primers is shown on the left axis,
the total number of sequences
analysed is on the top of each bar,
and the number of sequences
these primers would not bind to is
indicated by the shading. Many
assembled groundwater-
associated 16S rRNA gene
sequences would evade
amplification by PCR primers 515F
and 806R. Results of the analysis
are shown at the domain (a) and
superphylum or phylum (b) levels.

55. Comments on Primers?

56. Phylogeny
• How infer phylogeny of CPR?

57. Phylogeny

58. CPR

59. Comments on Phylogeny?

60. Phyla
• How many phyla are there?
• Why does it matter?
• How determine # of phyla

61. Phyla

62. Comments on Phyla?

63. Novel Ribosomes
• Why examine ribosomes (beyond rRNA) in these
organisms?
• What do the findings mean?

64. Novel Ribosomes

65. Assembled genomes were analysed
using ggKbase (Supplementary Data
4). Shown here is a non-redundant set
of complete and near-complete
genomes (≥75% of single copy genes,
≤1.125 copies) organized based on a
subset of a maximum-likelihood 16S
rRNA gene phylogeny (Supplementary
Fig. 1). CPR organisms have partial
tricarboxylic acid (TCA) cycles and
lack electron transport chain (ETC)
complexes. In addition, they have
incomplete biosynthetic pathways for
nucleotides and amino acids. The
Peregrinibacteria are a notable
exception to some of these limitations.
Several Parcubacteria exhibit a
complete ubiquinol (cytochrome bo)
oxidase operon, as previously seen in
Saccharibacteria3. However, lack of
NADH dehydrogenase and other ETC
components suggests that this
enzyme is involved in oxygen
scavenging/detoxification rather than
energy production. AA Syn., amino
acid synthesis; PP, pentose phosphate
pathway.

66. Novel Ribosomes 2

67. Comments on Ribosomes?

68. Novel Metabolism
• Why examine metabolism in these organisms?

69. Novel Metabolism

70. Assembled genomes were analysed
using ggKbase (Supplementary Data
4). Shown here is a non-redundant set
of complete and near-complete
genomes (≥75% of single copy genes,
≤1.125 copies) organized based on a
subset of a maximum-likelihood 16S
rRNA gene phylogeny (Supplementary
Fig. 1). CPR organisms have partial
tricarboxylic acid (TCA) cycles and
lack electron transport chain (ETC)
complexes. In addition, they have
incomplete biosynthetic pathways for
nucleotides and amino acids. The
Peregrinibacteria are a notable
exception to some of these limitations.
Several Parcubacteria exhibit a
complete ubiquinol (cytochrome bo)
oxidase operon, as previously seen in
Saccharibacteria3. However, lack of
NADH dehydrogenase and other ETC
components suggests that this
enzyme is involved in oxygen
scavenging/detoxification rather than
energy production. AA Syn., amino
acid synthesis; PP, pentose phosphate
pathway.

71. Comments on Metabolism?