Computational framework for inference of metabolic pathway activity from RNA-seq data
Funding agency: National Science Foundation, Division of Biological Infrastructure
Award #: DBI-1564936
PI: Ion I. Mandoiu
Microbial communities are an essential part of life on Earth. Natural communities comprised of up to thousands of interacting microbial species 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. 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, and (iii) developing and validating prototype implementations of developed methods.