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Nikos Kyrpides – Microbiome Data Science

Research in the Kyrpides group focuses on Microbiome Data Science and analysis of Big Data.

This includes the understanding of structure and function of various microorganisms and microbial communities and the elucidation of the evolutionary dynamics that shape the microbial genomes. To accomplish that members of the group are developing novel methods for enabling large-scale comparative analysis, as well as mining and visualization of big data.

Current big data projects in the group include the sequencing and comparative analysis of thousands of archaeal and bacterial type strains (GEBA-type strains project), uncovering Earth’s Virome, the delineation of host-virus interactions, and the discovery of novel protein families from microbiome data.

Research Team

David Paez-Espino
Nikos Kyrpides, PI David Paez-Espino, 
Research Scientist
 Russell Y. Neches,
Postdoctoral Fellow
  Stephen Nayfach,
Postdoctoral Fellow
nckyrpides@lbl.gov
(925) 296-5718		 adpaezespino@lbl.gov
(925) 927-2518		 ryneches@lbl.gov snayfach@lbl.gov
(925) 296-5848

More about Nikos

 David’s research is focused on the discovery of novel viral genomes and the exploration of host-virus interactions looking through all available microbiome data.

Russell’s research  focuses on the analysis of soil virome and the development of novel approaches to enable biogeographic analysis of big data.

  Stephen’s research is focusing on population genomics and on the development of computational methods for large scale reconstruction of genomes from metagenomes

Selected Publications

  1. Duerkop BA, et al. (2018) Murine colitis reveals a disease-associated bacteriophage community. Nat Microbiol. Epub
  2. Harrington LB et al. (2017) A thermostable Cas9 with increased lifetime in human plasma. Nat Commun. 8(1):1424.
  3. Sczyrba A, et al. (2017) Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software. Nature Methods 14(11):1063-1071
  4. Bowers RM, et al. (2017) Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature Biotechnology 35(8):725-731
  5. Paez-Espino D. et al. (2017) Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data. Nature Protoc. 12(8):1673-1682
  6. Mukherjee S, et al. (2017) 1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life. Nature Biotechnology 35(7):676-683
  7. Schulz F, et al. (2017) Giant viruses with an expanded complement of translation system components.  Science  356: 82-85
  8. Ovchinnikov S. et al. (2017) Protein structure determination using metagenome sequence data. Science 355(6322):294-298
  9. Paez-Espino D. et al. (2017) IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses. Nucleic Acids Res. 45(D1):D457-D465.
  10. Chen IA et al. (2017) IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Res. 45(D1):D507-D516
  11. Paez-Espino, D. et al. (2016) Uncovering Earth’s virome. Nature 536:425-30
  12. Kyrpides NC. et al. (2016) Microbiome Data Science: Understanding Our Microbial Planet. Trends Microbiol. 24(6):425-7.
  13. Eloe-Fadrosh EA, et al. (2016) Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nature Microbiol. 15032
  14. Eloe-Fadrosh EA, et al. (2016) Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs. Nature Commun. 7:10476
  15. Oulas A. et al. (2016) Metagenomic investigation of the geologically unique Hellenic Volcanic Arc reveals a distinctive ecosystem with unexpected physiology. Environ Microbiol. 18(4):1122-36.
  16. Varghese NJ, et al. (2015) Microbial species delineation using whole genome sequences. Nucleic Acids Res. 43(14):6761-71
  17. Seshadri R, et al. (2105) Discovery of Novel Plant Interaction Determinants from the Genomes of 163 Root Nodule Bacteria. Sci Rep. 5:16825
  18. Kyrpides NC et al. (2014) Genomic Encyclopedia of Bacteria and Archaea: Sequencing a Myriad of Type Strains. PLoS Biology 12(8):e1001920
  19. Ivanova N. et al. (2014) Stop Codon Reassignments in the Wild. Science 344(6186):909-13
  20. Pati A et al. (2010) GenePRIMP: A GENE PRediction IMprovement Pipeline for Prokaryotic genomes. Nature Methods. 2010; 7: 455-7.
  21. Kyrpides, NC. (2009) Fifteen Years of Microbial Genomics: Meeting the Challenges and Fulfilling the Dream. Nature Biotechnology 27, 627 -632
  22. Mavromatis K et al. (2007) Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nature Methods. 4: 495-500.