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 a senior computational scientist in the Broad's Metabolomics Platform, and he is interested in the intersection of 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. He 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 with strong evidence in measured data, suitable for testing in a laboratory.
He 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 developed netome: a computational environment for metabolome and biological network analysis using omic data. This framework includes methods to analyze metabolite profiles using liquid chromatography tandem mass spectrometry (LC-MS), and also pattern discovery in high-dimensional datasets. Rahnavard introduced block-wise association discovery for omics data in the HAllA software package.
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, followed by 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 Institute of MIT and Harvard. 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.