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Metagenomics at Second Genome

Metagenomics at Second Genome. Tanya Yatsunenko tanya@secondgenome.com. San Francisco based company leveraging microbiome science to enable the discovery and development of human health products through services, collaborations and internal R& D

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Metagenomics at Second Genome

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  1. Metagenomics at Second Genome Tanya Yatsunenko tanya@secondgenome.com

  2. San Francisco based company leveraging microbiome science to enable the discovery and development of human health products through services, collaborations and internal R&D • Taking a mechanistic approach to discovery • First-of-kind microbiome drug discovery platform with pharma partner validation • NotDx, not nutrition, not fecal transplant, not strains as drugs • Curator of Greengenes™ database (Todd DeSantis) • Qiime developer (Justin Kuczynskifrom Rob Knight Lab) • Over 200 microbiome studies completed to date across industry, government, academic researchers, nutrition companies, and pharma

  3. Sample1_Right.fastq Metagenomic (and RNA-seq) Pipeline at SG Sample1_Left.fastq Remove rRNA Remove poor quality bases and short reads Remove adapters Remove Host DNA Filtered sequences SortmeRNA Bowtie2 prinseq-lite fastq-mcf Metaphlan Taxonomic Table Functional Annotation BioCyc Database RapSearch Samples comparison: PCoA, Hierarchical Clustering; Discriminatory Organisms and Pathways Genes, Genomes, Pathway abundance and coverage Open source software Cloud = Amazon AWS spot instances

  4. Functional annotationGenes -> Enzymes -> Pathways and Strains 1 Query Sequence from Sample1: KDYDTAQRVLGNVLVLNIIIGLAFTVLTLIFLD Functional assignments Bacterial strain assignments Connecting genes/enzymes to bacterial genomes

  5. Challenges • ~1% filtered sequences with a significant hit to BioCyc database • Assembly with complex microbiota? • Paired-end sequences are treated independently (for hi-seq) • Confidence in identification of strains hits from metagenomic and transcriptomicdatasets • Database: KEGG vsBioCycvs others • Some samples forward and reverse reads result in different microbiome profiles

  6. Correlating human with microbial transcriptome +Rho Microbial gene -Rho Human gene Get correlation coefficient (Rho) and p value 23 mln correlations, 400 after bonferroni correction

  7. Best correlation: Peptidoglycan glycosyltransferasevsHuman gene (inflammasome related) Human gene expression Microbial enzyme expression Sample ID

  8. Best correlation: microbial enzyme vs 5 human genes Peptidoglycan glycosyltransferase.

  9. Summary • Will be happy to discuss our methods and some of the findings • Currently working on relating human and microbiome functions in disease states tanya@secondgenome.com

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