About

Jackie Weissman (they/she) is an Assistant Professor at Stony Brook University studying how microbes survive and thrive across diverse environments. She develops new computational tools to infer what microbes are doing and can do from DNA sequences captured directly from the environment (“metagenomes”), aiming to improve the representation of microbially-mediated biogeochemical cycles in global climate models. She also has a special interest in using a combination of comparative genomics, population genetics, and mathematical models to understand the ancient and ongoing battle between microbes and their viruses. She believes all students, with supportive training and mentorship, can become highly-capable computational biologists, and loves to show students how a little coding can go a long way. Previously, JL served as the inaugural Director for Proposal Development at the City College of New York, where they managed large, interdisciplinary efforts to bring center-level funding to the college and trained early-career researchers in grantmaking. They maintain research affiliations in biology at CCNY and the University of Southern California and have taught at The Cooper Union School of Art. Before returning to New York, they were faculty at Chapman University, where they ran a computational biology research lab, taught, and developed initiatives to improve mentorship at the college level.

Research topics

• Biology
• Genetics
• Computational biology
• Evolutionary biology
• Statistics
• Mathematics

Selected publications

• Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

  Proceedings of the National Academy of Sciences · 2021 · 293 citations

  1st authorCorresponding
  • Biology
  • Evolutionary biology
  • Genetics

  Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.

  DOI

• Ribosome-linked mRNA-rRNA chimeras reveal active novel virus host associations

  bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 16 citations

  • Biology
  • Genetics
  • Computational biology

  Abstract Viruses of prokaryotes greatly outnumber their hosts 1 and impact microbial processes across scales, including community assembly, evolution, and metabolism 1 . Metagenomic discovery of novel viruses has greatly expanded viral sequence databases, but only rarely can viral sequences be linked to specific hosts. Here, we adapt proximity ligation methods to ligate ribosomal RNA to transcripts, including viral ones, during translation. We sequenced the resulting chimeras, directly linking marine viral gene expression to specific hosts by transcript association with rRNA sequences. With a sample from the San Pedro Ocean Time-series (SPOT), we found viral-host links to Cyanobacteria, SAR11, SAR116, SAR86, OM75, and Rhodobacteracae hosts, some being the first viruses reported for these groups. We used the SPOT viral and cellular DNA database to track abundances of multiple virus-host pairs monthly over 5 years, e.g. with Roseovarius phages tracking the host. Because the vast majority of proximity ligations should occur between an organism’s ribosomes and its own transcripts, we validated our method by looking for self- vs non-self mRNA-rRNA chimeras, by read recruitment to marine single amplified genomes; verifiable non-self chimeras, suggesting off-target linkages, were very rare, indicating host-virus hits were very unlikely to occur by mistake. This approach in practice could link any transcript and its associated processes to specific microorganisms.

  DOI

• Avoidance of Self during CRISPR Immunization

  Trends in Microbiology · 2020 · 48 citations

  1st authorCorresponding
  • Biology
  • Computational biology
  • Genetics
  DOI

Frequent coauthors

• Jed A. Fuhrman

  University of Southern California

  22 shared

• William F. Fagan

  University of Maryland, College Park

  16 shared

• Shengwei Hou

  University of Southern California

  13 shared

• Philip L. F. Johnson

  University of Maryland, College Park

  13 shared

• Melanie Ortiz‐Alvarez de la Campa

  Brown University

  12 shared

• Robin L. Jones

  Royal Marsden Hospital

  12 shared

• Yi‐Chun Yeh

  University of Southern California

  12 shared

• Anna Weiss

  University of Rochester

  8 shared

Education

• Ph.D., Computer Science

  University of California, Berkeley

  1990

• B.S., Computer Science

  University of California, Berkeley

  1985

Similar researchers at Stony Brook University

• Scott McLennanh-145
• Anatoly I. Frenkelh-101
• Vitaly Citovskyh-98
• Michael R. Douglash-91
• Minghua Zhangh-89