Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data

WP7: Statistical Methodologies for Systems Level Modelling

OBJECTIVES
Regarding transcriptional regulation, we will use data from multiple experimental views of the same system (different modalities) to parametrise models of transcriptional regulation. Recently introduced HTS technologies such as RNA-Seq, ChIP-Seq, ChIA-PET, Gro-Seq can be collected in time-series and provide a wealth of information about the process of transcription. However, integrating these data through a model presents significant technical challenges. By fitting simple mechanistic models of transcription using these data we will uncover important aspects of the dynamics of transcriptional regulation, taking initiation, elongation, pausing, release, transport and non-local enhancer-promoter interactions into account. An interesting application, linking to WP5 and WP6, will be an investigation of the relationship between splicing, transcriptional pausing and histone modifications.
Regarding post-transcriptional regulation modelling, we will develop a method to identify microRNA -> mRNA regulatory interactions and post-translational regulatory interactions (kinase, phosphatase -> transcription factor) from RNA-Seq data obtained at steady-state following multiple perturbation experiments. We will build on our expertise in reverse-engineering gene networks. This will be achieved by developing statistical methods based on multi-variate Mutual Information estimation. The hypothesis underlying the proposed approach is that fluctuations in the transcriptional level of modulators across different conditions can be exploited to infer post-translational regulation, by identifying groups of transcripts whose joint probability distribution is affected by the modulator’s expression level. As a case study, the method will be applied to identify new retina-specific microRNAs and kinases/phosphatases, as well as their target genes in the human retina. RNA-Seq and smallRNA-Seq profiles from 50 human retinas are currently being generated by Partner FTELE.IGM in the context of another project.

PARTICIPATING PARTNERS
FTELE.IGM (Lead Partner)
University of Manchester
Genomatix
University of Sheffield