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Nikos Kyrpides – Microbiome Data Science

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

Biology is currently experiencing a revolution brought on by rapid developments in genomics and other omics technologies. The exponential growth of DNA sequencing data, coupled with recent computational technology advances in management, processing and visualization of “big data,” and artificial Intelligent/machine learning approaches in data analysis and interpretation is creating new opportunities for breakthrough discoveries and catalyzing a major transition of biology into data science.

The research efforts of the group have concentrated in demonstrating the power of data science in tackling grand challenges in Biology, with a special focus on microbiome research.  These efforts include exploration of the global diversity of alternative genetic codes (Science 2014), unearthing hundreds of thousands of new viral genomes and their predicted hosts (Nature 2016, Science 2017, Nature Microbiology 2018), discovery of new genes with important biotechnological applications (e.g., new CRISPR-Cas variants and new CRISPR-Cas types) (Nature Communications 2017, Science 2018, Molecular Cell 2019) and massive reconstruction of genomes from uncultivated microbes (Nature 2019).

Other projects in the group include the sequencing and comparative analysis of thousands of archaeal and bacterial type strains (GEBA-type strains project), the delineation of host-virus interactions, the exploration of the functional dark matter as well as the development of novel methods to enable large-scale comparative analysis, mining and visualization of big data.

 

Research Team

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

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.

David is also looking for novel proteins that perform gene editing

Interview with David

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

Stephen’s GitHub

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

Meet Russell

Russell’s GitHub

Lee’s research  focuses on large scale microbiome and virome data analysis

 

 

Selected-Publications

  1. Nayfach S, et al, (2019) New insights from uncultivated genomes of the global human gut microbiome. Nature  568(7753):505-510
  2. Amann R. et al (2019) Toward unrestricted use of public genomic data. Science  363(6425):350-352
  3. Harrington LB, et al (2018) Programmed DNA destruction by miniature CRISPR-Cas14 enzymes. Science 362(6416):839-842.
  4. Duerkop BA, et al. (2018) Murine colitis reveals a disease-associated bacteriophage community. Nat Microbiol. 3(9):1023-1031
  5. Harrington LB et al. (2017) A thermostable Cas9 with increased lifetime in human plasma. Nat Commun. 8(1):1424.
  6. Sczyrba A, et al. (2017) Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software. Nature Methods 14(11):1063-1071
  7. 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
  8. Paez-Espino D. et al. (2017) Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data. Nature Protoc. 12(8):1673-1682
  9. 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
  10. Schulz F, et al. (2017) Giant viruses with an expanded complement of translation system components.  Science  356: 82-85
  11. Ovchinnikov S. et al. (2017) Protein structure determination using metagenome sequence data. Science 355(6322):294-298
  12. 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.
  13. Chen IA et al. (2017) IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Res. 45(D1):D507-D516
  14. Paez-Espino, D. et al. (2016) Uncovering Earth’s virome. Nature 536:425-30
  15. Kyrpides NC. et al. (2016) Microbiome Data Science: Understanding Our Microbial Planet. Trends Microbiol. 24(6):425-7.
  16. Eloe-Fadrosh EA, et al. (2016) Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nature Microbiol. 15032
  17. Eloe-Fadrosh EA, et al. (2016) Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs. Nature Commun. 7:10476
  18. 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.
  19. Varghese NJ, et al. (2015) Microbial species delineation using whole genome sequences. Nucleic Acids Res. 43(14):6761-71
  20. Seshadri R, et al. (2105) Discovery of Novel Plant Interaction Determinants from the Genomes of 163 Root Nodule Bacteria. Sci Rep. 5:16825
  21. Kyrpides NC et al. (2014) Genomic Encyclopedia of Bacteria and Archaea: Sequencing a Myriad of Type Strains. PLoS Biology 12(8):e1001920
  22. Ivanova N. et al. (2014) Stop Codon Reassignments in the Wild. Science 344(6186):909-13
  23. Pati A et al. (2010) GenePRIMP: A GENE PRediction IMprovement Pipeline for Prokaryotic genomes. Nature Methods. 2010; 7: 455-7.
  24. Kyrpides, NC. (2009) Fifteen Years of Microbial Genomics: Meeting the Challenges and Fulfilling the Dream. Nature Biotechnology 27, 627 -632
  25. Mavromatis K et al. (2007) Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nature Methods. 4: 495-500.