Dr. Kyrpides joined the DOE Joint Genome Institute in 2004 to lead the Genome Biology Program and the development of the data management and comparative analysis platforms for microbial genomes and metagenomes (IMG). He became the Metagenomics Program head in 2010 and leads the Prokaryotic Super Program and the Microbiome Data Science Group since 2011. Prior to joining the DOE Joint Genome Institute, Dr. Kyrpides led the development of the genome analysis and Bioinformatics core at Integrated Genomics Inc. in Chicago, IL. He did his postdoctoral studies with Carl Woese at the University of Illinois at Urbana-Champaign and with Ross Overbeek at the Argonne National Laboratory.
- BA in Biology, Aristotle University of Thessaloniki, Greece;
- PhD in Molecular Biology and Biotechnology, University of Crete, Greece
Awards and Service
Awards: USFCC/J. Roger Porter Award (2018); Honorary Doctorate, Aristotle University of Thessaloniki, Greece (2017); The van Niel International Prize for Studies in Bacterial Systematics (2012-2014); American Academy of Microbiology Fellow (2014); Thomson Reuters Highly Cited Researchers (2014-2017); Academic Excellent Prize Award, Empeirikeion Foundation (2012); Outstanding Performance Award, Lawrence Berkeley National Laboratory (2007)
Administrative Boards: Genomics Standards Consortium. Scientific Advisory Boards: Microbiomes in Transition (MinT), PNNL, EarthCube Oceanography & Geobiology Environmental Omics (ECOGEO) RCN, USA; Biocreative programme, USA; AgreenSkills programme, France; Meta-omics of microbial ecosystems, INRA, France; A Knowledge-Based Bioinformatics Framework for Microbial Pathway Genomics (MICROME), EU; Association for Computing Machinery (ACM) Special Interest Group in Bioinformatics, USA; Metagenomics of the Human Intestinal Tract (MetaHIT), EU; Community Cyber-infrastructure for Advanced Marine Microbial Ecology Research and Analysis (CAMERA), USA. Editorial Board: PeerJ; Environmental Microbiology; BMC Microbiome; Life; Journal of Bacteriology; BMC Standards in Genomic Sciences (SIGS)
- Nayfach S, et al, (2019) New insights from uncultivated genomes of the global human gut microbiome. Nature 568(7753):505-510
- Amann R. et al (2019) Toward unrestricted use of public genomic data. Science 363(6425):350-352
- Harrington LB, et al (2018) Programmed DNA destruction by miniature CRISPR-Cas14 enzymes. Science 362(6416):839-842.
- 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
- Schulz F, et al. (2017) Giant viruses with an expanded complement of translation system components. Science 356(6333):82-85.
- Ovchinnikov S. et al. (2017) Protein structure determination using metagenome sequence data. Science 355(6322):294-298
- Paez-Espino, D. et al. (2016) Uncovering Earth’s virome. Nature 536:425-30.
- Eloe-Fadrosh EA, et al. (2016) Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nature Microbiology 15032
- Kyrpides NC. et al. (2016) Microbiome Data Science: Understanding Our Microbial Planet. Trends Microbiology 24(6):425-7.
- Eloe-Fadrosh EA, et al. (2016) Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs. Nature Communications 7:10476
- Varghese NJ, et al. (2015) Microbial species delineation using whole genome sequences. Nucleic Acids Res. 43(14):6761-71.
- Kyrpides NC et al. (2014) Genomic Encyclopedia of Bacteria and Archaea: Sequencing a Myriad of Type Strains. PLoS Biology 12(8):e1001920
- Ivanova NN, et al. (2014) Stop codon reassignments in the wild. Science 344(6186):909-13
- Rinke C et al. (2013) Insights into the phylogeny and coding potential of microbial dark matter. Nature 499(7459):431-7.
- Pati A et al. (2010) GenePRIMP: A GENE PRediction IMprovement Pipeline for Prokaryotic genomes. Nature Methods 2010; 7: 455-7.
- Kyrpides N.C. (2009) Fifteen Years of Microbial Genomics: Meeting the Challenges and Fulfilling the Dream. Nature Biotechnology 27: 627 -632.
- Mavromatis K et al. (2007) Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nature Methods. 4: 495-500.