Revision as of 10:22, 5 February 2014

We are working on RNA at the interface of Systems Biology, Computer Science and Molecular Neuroscience, currently sponsored by NIH, Simons Foundation, and Columbia University Medical Center startup funds.

Elucidating neuronal RNA-regulatory networks

It is increasingly recognized that post-transcriptional regulation at the RNA level plays critical roles for orchestrated gene expression in mammalian systems. Such regulation is conferred through interaction of at least several hundred RNA-binding proteins (RBPs) with their target transcripts, or RNA-regulatory networks. The challenge to infer RNA-regulatory networks lies in the fact that most RBPs recognize very short and generate sequence motifs with limited information content. We takes advantage of recent advances in biochemical and high-throughput assays, such as HITS-CLIP (or CLIP-Seq) and RNA-Seq that profile transcriptomes and protein-RNA interactomes. We apply these assays, mostly using mouse brain as a model system, and intersect them with statistical and machine learning approaches to develop methods that predict specific protein-RNA interactions. These predictions complement and enhance the information we can obtain from experimental data. We also develop integrative modeling approaches to combine multiple types of data to infer direct and functional targets of specific RBPs.

• Zhang, C.†, Frias, M.A., Mele, A., Ruggiu, M., Eom, T., Marney, C.B., Wang, H., Licatalosi, D.D., Fak, J.J., Darnell, R.B.† 2010. Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls. Science, 329: 439-443.
• Zhang, C.*, Zhang, Z.*, Castle, J., Sun, S., Johnson, J., Krainer, A.R. and Zhang, M.Q. 2008. Defining the regulatory network of the tissue-specific splicing factors Fox-1 and Fox-2. Genes Dev, 22:2550-2563.

RNA-regulatory networks in evo-devo processes and in neuronal disorders

One of the ultimate goals to infer RNA-regulatory networks is to understand their forms and functions in normal physiological and pathological contexts. In real systems, the networks are almost always under combinatorial and dynamic regulation, which tremendously increase the complexity. We are working toward a better understanding of how such dynamic regulation drives the developing mammalian brain, differentiation of neurons from stem cells and (at a larger scale) mammalian evolution. We are also interested in understanding how such networks are perturbed in motor neuron diseases (such as ALS and SMA) and brain tumors (such as glioblastoma).

• Weyn-Vanhentenryck,S.,M.*, Mele,A.*, Yan,Q.*, Sun,S., Farny,N., Zhang,Z., Xue,C., Herre,M., Silver,P.A., Zhang,M.Q., Krainer,A.R., Darnell,R.B. Zhang,C. † 2014. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Rep In press.

High-throughput transcriptomic data analysis

Our work heavily relies on high-throughput technologies which produce enormous amount of data, and algorithms to transform these data into useful information. We are interested in developing better algorithms to process transcroptomic data, such as mapping RNA-Seq reads, discovering and quantifying RNA processing in specific conditions, and analyzing CLIP data to map protein-RNA interactions at a single nucleotide resolution.

• Moore, M.*, Zhang, C.*, Gantman, E.C., Mele, A., Darnell, J.C., Darnell, R.B. 2014. Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis. Nat Protocols. 9:263-293. (Software)

• Wu,J., Anczukow,O., Krainer,A.R., Zhang,M.Q. †, Zhang,C. †, 2013. OLego: Fast and sensitive mapping of spliced mRNA-Seq reads using small seeds. Nucleic Acids Res. , In press. (Software)

• Zhang, C.†, Darnell, R.B.† 2011. Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat Biotech, 29:607-614.