Congrats on a new collaborative NSF ABI grant!!!

Thank you NSF – you’ve been very good to us this year!

Looking forward to working with collaborators Alex Zelikovsky, Ion Mandoiu, and Nicole Lopanik.


ABI Innovation: Collaborative Research: Computational framework for inference of metabolic pathway activity from RNA-seq data

Microbial communities, or microbiomes, are an essential part of life on Earth. Microbiomes in the natural environment, including those in association with animals and plants, are comprised of thousands of interacting microbial species. These complex communities influence key aspects of host health and behavior and drive fundamental biochemical processes, ranging from nutrient processing in our guts to sequestration of carbon in the Earth’s oceans. The study of microbiomes has been recently revolutionized by the use of advanced sequencing technologies. However, large-scale initiatives such as the Human Microbiome Project and the Earth Microbiome Project are generating Petabytes (1015    bytes) of sequencing data, greatly challenging existing analysis tools. The goal of this project is to develop transformative computational methods and implement software tools enabling the analysis of these large-scale datasets. Specifically, these tools will provide improved methods to organize community gene expression data (metatranscriptomes) into metabolic pathways, thereby helping to inform predictions of biochemical transformations of matter and energy. To maximize impact, the developed software tools will be made available to the research community as stand alone open source packages and deployed on common cloud computing environments. The project will provide opportunities for mentoring undergraduate and graduate students at Georgia State University, UConn, and Georgia Tech and promote participation of women and underrepresented groups in bioinformatics research and empirical analysis of community-level sequence (DNA/RNA) datasets. Selected aspects of the proposed research will be incorporated in courses at the three universities, and form the basis of innovative curriculum and educational materials, including the creation of mobile apps.

This project brings together an interdisciplinary team of computer scientists and environmental microbiologists to develop and implement computational tools that enable de novo analysis of large multi-sample microbiome sequencing datasets, addressing current challenges in metatranscriptome assembly and inference of metabolic pathway activity. Specific aims of the project include: (i) developing highly scalable algorithms for de novo assembly and quantification from multiple metatranscriptomic samples, (ii) developing highly accurate algorithms for estimation of metabolic pathway activity level and differential activity testing, (iii) developing and validating prototype implementations of developed methods. A distinguishing feature of the developed methods will be their ability to jointly analyze multiple related metatranscriptomic samples. This joint assembly and quantification paradigm is likely to find applications beyond microbiome research, e.g., in the emerging area of single cell genomics. The results of the project, including software packages, research publications, and educational materials, will be
made available at and


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