Gholamali (Ali) Rahnavard

Gholamali (Ali)  Rahnavard

Gholamali (Ali) Rahnavard

M.S., Ph.D.

Assistant Professor

School: Milken Institute School of Public Health

Department: Biostatistics and Bioinformatics


Office Phone: +1 (202) 994-2214
Fax: 202-912-8475
Science & Engineering Hall 800 22nd Street, NW, #7570 Washington DC 20052

Gholamali (Ali) Rahnavard is an assistant professor of Biostatistics and Bioinformatics at George Washington University. Dr. Rahnavard is 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. His 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 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.


Senior Computational Scientist, Metabolomics Platform, The Broad Institute of MIT and Harvard, 2019

Postdoctoral Associate, Infectious Disease and Microbiome Program at The Broad Institute of MIT and Harvard - Department of Biostatistics at Harvard T.H. Chan School of Public Health, 2018

Ph.D. Computer Science and minors in Bioinformatics and Applied Statistics, New Mexico State University, 2014 

M.S. Computer Science and minor in Bioinformatics, New Mexico State University, 2013 

M.S. Computer Engineering, Shiraz University, 2005

B.S. Computer Engineering, Razi University of Kermanshah, 2003





Computational Biology and Bioinformatics


Omics Data Science 



Department of Biostatistics and Bioinformatics 

Computational Biology Institute


The theme of the Rahnavard Lab is Omics Data Science for Public Health and Precision Medicine.  Advances in high-throughput technologies enable capturing snapshots of human biology. These technologies include DNA sequencing techniques, liquid chromatography-mass spectrometry (LC-MS), single-cell RNA sequencing (scRNA-seq), and magnetic resonance angiogram (MRA), which are ideal tools for jointly characterizing the host-biomes interactions.

Rahnavard Lab develops innovative computationalmachine learningdeep learning, and statistical methods aimed at achieving actionable research outcomes in health and disease using high-dimensional omics data.  The goals for these techniques are to provide approaches to find biological patterns in the zoomed-in personalized level and the zoomed-out population-level using omics data. 

A key challenge of using omics data is the noise introduced by machines and batch effects which suppress the biological signals. We develop computational and deep learning methods to process and correct metabolite profiles. Clean data is fundamental to finding patterns and associations in high-dimensional data. Curing metabolomics data enable the translation of microbiome-metabolite discoveries into treatments for human disease, a key focus of the research laboratory. Profiling the human microbiome and the human metabolome in the context of health and disease is critical for translational biomedical research.

The lab also applies computational methods to multi-omics data to generate hypotheses of the underlying processes involved in disease activity. The Rahnavard lab also tests hypotheses with strong evidence in measured data in a laboratory and translation into actionable diagnostics and therapeutics.


For a complete publication list, please visit Dr. Rahnavard's GoogleScholar page.

Mallick, H., Chatterjee, S., Chowdhury, S., Chatterjee, S., Rahnavard, A., & Hicks, S. C. Differential expression of single-cell RNA-seq data using Tweedie models. bioRxiv (2021).

Rahnavard, A., et al. Metabolite, Protein, And Tissue Dysfunction Associated With COVID-19 Disease Severity. PREPRINT (2021).

Rahnavard, A., et al. Epidemiological associations with genomic variation in SARS-CoV-2. Scientific Reports 11.1 (2021): 1-10.

Mallick, H., Rahnavard, A., et al. Multivariable association discovery in population-scale meta-omics studies. PLoS computational biology 17.11 (2021): e1009442.

Rahnavard, A., et al. Omics community detection using multi-resolution clustering. Bioinformatics 37.20 (2021): 3588-3594.

Smith, E. R., He, S., Klatt, K. C., Barberio, M. D., Rahnavard, et al. Limited data exist to inform our basic understanding of micronutrient requirements in pregnancy. Science advances 7.43 (2021): eabj8016.

Amritphale, A., Chatterjee, R., Chatterjee, S., Amritphale, N., Rahnavard, A., et al. Predictors of 30-day unplanned readmission after carotid artery stenting using artificial intelligenceAdvances in therapy 38.6 (2021): 2954-2972.

Stearrett, N., Dawson, T., Rahnavard, A., et al. Expression of human endogenous retroviruses in systemic lupus erythematosus: multiomic integration with gene expression. Frontiers in immunology 12 (2021): 1485.

He, S., Klatt, K. C., Rahnavard, A., et al. Protocol for meta-research on the evidence informing micronutrient dietary reference intakes for pregnant and lactating women. Gates Open Research 4 (2020).

Thingholm, Louise B., et al. Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and CompositionCell host & microbe 26.2 (2019): 252-264.

Lloyd-Price, Jason, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569.7758 (2019): 655.

Franzosa EA, McIver LJ, Rahnavard G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods2018 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 Med2018 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. PeerJ2015 Mar 31;3:e791.