Franzosa EA, McIver LJ, Rahnavard G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018 Nov;15(11):962-968.
Rahnavard G, Hitchcock, D., Avila-Pacheco J, et al. netome: a computational framework for metabolite profiling and omics network analysis. BioRxiv 443903 [Preprint]. October 16, 2018.
McDonald D, Hyde E, Debelius JW, Morton JT, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018 May 15;3(3).
Kolde R, Franzosa EA, Rahnavard G, et al. Host genetic variation and its microbiome interactions within the Human Microbiome Project. Genome Med. 2018 Jan 29;10(1):6.
Lloyd-Price J, Mahurkar A, Rahnavard G, et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature. 2017 Oct 5;550(7674):61-66.
Börnigen D, Moon YS, Rahnavard G, et al. A reproducible approach to high-throughput biological data acquisition and integration. PeerJ. 2015 Mar 31;3:e791.
Ali Rahnavard, Ph.D.
Ali Rahnavard is an assistant professor at the George Washington University and a visiting scientist in the Metabolomics Platform at the Broad Institute of MIT and Harvard. Rahnavard and members of his lab are interested in the intersection of the microbiome and metabolome for understanding their interactions in health and disease. Since the metabolome is the interface mediating this interaction, he primarily investigates metabolite and microbiome changes over the course of disease. The lab uses systems-biology-based approaches, applying computational methods to multi-omic data with the goal of generating hypotheses of the underlying processes involved in disease activity. These hypotheses with strong evidence in measured data are suitable for testing in a laboratory and translation into actionable diagnostics and therapeutics.
The Rahnavard lab also develops novel computational methods to investigate how the microbes in the human gut and metabolites interact with each other and with the host during health and disease. As part of this work, Rahnavard lab developed netome: a computational environment for omics data analysis and integration. This framework includes methods for discovering biological patterns in high-dimensional multi-omic datasets, and also analyzing metabolite profiles using liquid chromatography tandem mass spectrometry (LC-MS). Using computational techniques, Rahnavard characterized microbial behavior at a deep resolution of strain and function (e.g., how microbial species at the strain level are associated with human body sites) by applying statistical methods to several large cohort-based microbiome studies, including the expanded NIH Human Microbiome Project (HMP1-II) study of the healthy human microbiome.
Rahnavard earned his Ph.D. in computer science, applied statistics, and bioinformatics at New Mexico State University. Rahnavard completed postdoctoral work in the biostatistics department at Harvard T.H. Chan School of Public Health and the Infectious Disease and Microbiome Program at the Broad. Prior to his position with the George Washington University, Rahnavard was a senior computational scientist with the Broad’s Metabolomics Platform. He also holds a master’s degree in computer engineering/software systems from Shiraz University and a bachelor’s degree in computer engineering from Razi University of Kermanshah.
Contact Ali Rahnavard at firstname.lastname@example.org.