Statistical analyses of transcriptome data across cell lineages and developmental stages
The development of high-throughput transcriptome sequencing technology, RNA-sequencing (RNA-seq), offers new insights into the transcriptome. While RNA-seq has emerged in plant sciences as an important tool to characterize and quantify the transcriptome, the analysis of large amounts of transcriptome data still remains a computational and statistical challenge. Several transcriptome data analysis tools and pipelines have been designed in recent years, but there is still no consensus on the best practices for the analysis of RNA-seq data in complex experimental settings. Quantitative analysis of the transcriptome represents an opportunity to recognize functional components of the genome and to decipher the molecular basis for the development of organs. During the development of an organ, the expression level of the genes changes across the co-ordinated and sequential phases of differentiation, and multiple gene expression patterns have been observed, progressively increasing or decreasing over the developmental progression, or showing either multiple expression peaks or a single expression peak over the differentiation stages. In this research, we propose to analyse the developmental timepoint-specific transcriptome profiles of Cardamine hirsuta leaves by analyzing tissue- and timepoint-specific genetic markers. We propose to apply matrix factorization techniques to RNA-seq data analysis that can capture biological condition or tissue and developmental timepoint-specific genetic signals.
The project will be supervised by Stefan Laurent at the Max Planck Institute for Plant Breeding Research.