1. Use of the genomic data o Reconstruction of metabolic properties o Nature’s Microbiome o NGS in Population Genetics

3. Why are the diatoms so successful?

5. canonical urea metabolism in animals

6. duplication of CPS in secondary algae!

9. Diatoms have somehow reversed urea cycle, which server as a hub for the metabolism of the nitrogen and (to some extent) also the carbon!

10. Nature’s Microbiome hologenome Non specific in situ culture dependent approaches vs Metagenomics

11. Community composition 16S rDNA marker 95%, 97%, or 99% for OTUs/phylotype Operational Taxonomic Unit binning set of abundance histograms or set of binary presence/absence vectors Nature’s Microbiome Community function Decomposition methods: Principal Component Analyses Canonical Correlation Analyses

12. Nature’s Microbiome the number (richness) and distribution (evenness) of taxa expected within a single population Alpha Diversity Beta Diversity ‘‘How much do I need to sequence to completely characterize my microbiome?’’ absolute or relative overlap describe how many taxa are shared between samples

13. Nature’s Microbiome Community function Community composition 16S rDNA marker 95%, 97%, or 99% for OTUs/phylotype Operational Taxonomic Unit binning set of abundance histograms or set of binary presence/absence vectors Decomposition methods: Principal Component Analyses Canonical Correlation Analyses

14. How many microbial strains and species colonize eukaryotic hosts? Alpha diversity inflated due to PCR and sequencing errors X Diversity involving closely related microbes omitted in NGS data sets (OTUs cutt-off) Nature’s Microbiome: Chalanges Raw data „denoising“Detecting hidden strain diversity Analyses Sequencing tools (e.g.LEA-Seq)

15. How many microbial strains and species colonize eukaryotic hosts? Low Error Amplicon Sequencing, LEA-Seq Nature’s Microbiome:Chalanges Dilute barcoded primer solution 20x coverage

16. How to detect sequence libraries plagued by contamination? Nature’s Microbiome: Chalanges Results from the source-tracking analysis using a previously published set of mammal gut samples, as well as soil samples, as potential sources to determine which of the 92 samples were potentially contaminated with soil. Representative source data sets >10% soil assignment <0.01 mammalian gut 92/27(5) Negative controls Generation of source libraries marine/fresh water common lab microbes Number of biological replicates

17. Nature’s Microbiome: Outcomes

18. NGS and Population Genetics Problem of genotyping uncertainty on NGS data machine learning to separate true segregating variation from artifacts Sequencing errors Misalignment errors Low coverage Random sampling of homologous base pairs in heterozygotes initial read mapping local realignment around indels base quality score recalibration SNP discovery and genotyping to find all potential variants

19. NGS and Population Genetics Problem of genotyping uncertainty on NGS data initial read mapping local realignment around indels base quality score recalibration Gap enabled alignment with Burrows-Wheeler transform: >15% reads spanning homozygous indels misalgned

20. NGS and Population Genetics Problem of genotyping uncertainty on NGS data Sequencing errors Misalignment errors Low coverage Random sampling of homologous base pairs in heterozygotes

21. NGS and Population Genetics: Outcomes