Data-driven hierarchical analysis of de novo transcriptomes

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Recent developments in sequencing technology have made it possible to directly sample sequencing reads from the expressed transcripts present in a population of cells. This has provided an unparalleled view of gene expression, including how expression changes in response to certain stimuli, across different conditions, and between different disease states. Yet, the de novo analysis of this data, wherein the genome of the organism being sequenced is unknown, still poses many technical challenges. The view of gene expression obtained from such experiments is typically fractured, incomplete, and difficult to analyze in the manner one would in a reference-based scenario, where a corresponding genome is available. In this project, a suite of mathematical models and software tools will be developed to help close the gap between reference-based and de novo analysis of gene expression. The developed tools will be able to identify and correct a host of errors in the predicted (assembled) transcripts. The methods that are typically used to assess gene expression from sequencing data will be specialized for the de novo context, by explicitly accounting for the incomplete nature of the data. The recovered genes will be compared to known genes in related organisms, and new methods will be developed that will allow better prediction of the function of the genes discovered. The methods developed herein will estimate measures of uncertainty at each stage of analysis, and will include the measured uncertainty in all resulting conclusions. Finally, these novel methods and tools will be applied to the study of C4 photosynthesis - a highly efficient form of photosynthesis. A large number of sequencing datasets centered around the C4 system have been produced, yet the genetic mechanism underlying this trait is still unknown. The methods developed in this project will be used to improve our understanding of the regulatory elements involved in the C4 photosynthesis pathway. The project will also include the creation and support of an online community where scientists can seek expert advice on transcriptomic methods (including the software developed herein) and experimental design.