Data Analyses From RNA-Seq, CHiP-Seq, and Network Inference Experiments
During 2014, these researchers have performed analysis of three RNA-seq data and four ChIP-seq data generated from their lab or from public studies. They developed a new method for integrating multi-dimensional genomic data to infer the regulatory networks during mouse cardiac differentiation. This method can not only infer the gene-gene regulatory relationship but also pinpoint the cis-regulatory elements in the promoter and enhancer regions. A poster for this project won the grand prize in the biological and medical sciences section at the MSI Research Exhibition 2014. The group also developed a novel pipeline for reconstructing the lineage trees and inferring novel markers from single-cell RNA-seq data.
In 2015, in addition to the routine sequencing analysis, these researchers will work on two major projects that requires significant amount of computational resources. The first project is to extend the network inference project to include microRNA information and discover the conserved and species-specific gene regulatory relationships in cardiac differentiation process. The second project is to develop new bioinformatics tools for integrative analysis of large-scale single-cell RNA-seq data, as well as other NGS data at single-cell level.
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