The set of RNA transcripts in cells, collectively referred to as the transcriptome, are of fundamental importance as they form the substrate for protein synthesis, while also having the ability to assume functional roles via their secondary and tertiary structure. Transcriptional regulation is frequently controlled epigenetically, i.e. via mechanisms that do not depend strictly on the underlying DNA sequence. The collection of epigenetic marks is known as the epigenome.
Close connections between the transcriptome and epigenome are beginning to be unraveled via new technologies based on high-throughput sequencing that allow for the measurement of the transcriptome and epigenome at the resolution of individual nucleotides. For example, using RNA-seq it is possible to obtain a comprehensive and quantitative view of the cellular transcriptome. The measurement of the epigenome includes the profiling of cytosine metylation in the entire genome using approaches known as BS-seq or MethylC-seq. Protein DNA interactions also play a critical role in epigenetic regulation and chromatin-immunoprecipitation may be used to identify binding sites for specific proteins, using an approach termed ChIP-seq. Much work has already been dedicated to mapping histone modifications across the genome and thus elucidating the “histone code”. Furthermore, nucleosome positions are also important in regulating transcription and replication, and these can be measured using nuclease-based assays. As the epigenome of each type of cell is different, profiling epigenomes from stem cells to differentiated cells presents a daunting challenge for the genomics community.
The analyses of these types of data are presenting the mathematical community a rich set of challenges. These include the development of algorithms for read mapping, transcript assembly, peak calling, and prediction of nucleosome positions from the DNA sequence of the genome. All of these problems require the development of novel statistical and computational techniques, and mathematical foundations for many of the ideas need to be developed.
The workshop will include presentations that discuss the mathematical challenges in each of these fields. It will also include a poster session. A request for posters will be sent to registered participants in advance of the workshop.
(University of California, Berkeley (UC Berkeley), Biostatistics and Statistics)
Lior Pachter, Chair (University of California, Berkeley (UC Berkeley), Mathematics)
Matteo Pellegrini (University of California, Los Angeles (UCLA), Molecular, Cell, and Developmental Biology)
Barbara Wold (California Institute of Technology, Biology Division)
Wing Wong (Stanford University, Statistics)