2017-2018 WRF and Moore/Sloan Postdoctoral Fellow, School of Oceanography
Jamie Collins was a Moore/Sloan Data Science and WRF Innovation in Data Science Postdoctoral Fellow from 2017-2018. He is currently working at the Oregon Department of Environmental Quality.
School of Oceanography
E. Virginia Armbrust, School of Oceanography
Anitra Ingalls, School of Oceanography
David Beck, Department of Chemical Engineering and eScience Institute
Postdoctoral Investigator, Woods Hole Oceanographic Institution, 2017
Ph.D., Chemical Oceanography, MIT/WHOI Joint Program in Oceanography, 2017
M.E.Sc., Marine Biogeochemistry, Yale School of Forestry & Environmental Studies, 2011
B.A., Political Science, with distinction, Yale College, 2004
Scientists can now measure hundreds of thousands of unique chemical compounds in just a few drops of natural seawater or human blood serum. The large volume of data that can be obtained from each of these samples — multiplied many times over in a typical scientific experiment where multiple samples are collected — represents a new challenge in analytical chemistry. The nature of the problem is particularly formidable for those working the natural environment, where the collection of these molecules (a “metabolome”) reflects the complex, simultaneous imprint of hundreds of different microbes.
New computational tools are needed to ask even the most basic questions of these data. What are the identities of the many compounds in these samples? Are certain molecules more important than others for the function of a given ecosystem? Can these chemicals, or metabolites, be linked to the genes and proteins from which they are derived? And can these cryptic linkages tell us about undiscovered or poorly understood metabolic pathways?
At the eScience Institute, Jamie Collins is working with mentors in the School of Oceanography and Department of Chemical Engineering to develop new, open-source computational tools for manipulating, storing and interrogating large, high-resolution mass spectrometry datasets that contain chemical “signatures” from a variety of natural and engineered environments.
These new tools will be used to discover and characterize poorly understood metabolisms in a multi-meta-omic dataset collected from a sector of the Pacific Ocean near Hawaii. Multi-omics — the systematic integration of complementary datasets containing genes, transcripts, proteins, and metabolites — is a potentially powerful strategy for relating genes in these microbiomes to molecules and proteins, and for identifying which genes of known or unknown function are the best candidates for follow-on studies. Collins’ efforts will focus on lipids — a class of molecules which make up cell membranes, store energy, and play critical roles in communication between microbes.