Bayesian Spatial Analysis of High Throughput Sequencing Data

The past decade has witnessed the development and wide use of high-throughput sequencing data in biology. The recent advancement of RNA Sequencing (RNA-Seq) coupled with other molecular technologies such as methylated RNA immunoprecipitation (MeRIP) and spatial barcoding has delivered more specialized platform to investigate certain cellular process and spatial molecular profiling. However, the development of associated analysis tools capable of accommodating the unique features of these new sequencing technologies is still lacking or unsatisfied. For the past few years, I have been devoting to the methodology development of MeRIP-Seq and spatial molecular profiling data. The proposed BaySeqPeak and BOOST-GP methods demonstrated good accuracy, sensitivity and robustness in identifying methylated RNA region and spatial variable genes in both the simulation study and real data analysis.

Author dc:creator

• Zhang, Minzhe

Contributors dc:contributor

• Mendell, Joshua T.
• Nijhawan, Deepak
• Zhan, Xiaowei
• Xie, Yang
• Xiao, Guanghua

• Sequence Analysis, RNA
• Bayes Theorem
• Gene Expression Profiling
• Models, Statistical

Language dc:language

en

Identifier

1381370431

OAI identifier oai:identifier

oai:utswmed-ir.tdl.org:2152.5/10066