Statistical Bioinformatics Seminar: Jingqi Duan

Speaker: Jingqi Duan (University of Wisconsin-Madison) 

Abstract: The ENCODE project generated a large collection of eCLIP-seq RNA binding
protein (RBP) profiling data with accompanying transcriptome data from RNA-seq
experiments of RBP knockdowns by shRNA.  However, these datasets are not fully exploited
to elucidate the impact of genetic variants on RBP activities.  We implement INCA
(Integrative annotation scores of variants for impact on RBP activities) as a multi-step
genetic variant scoring approach that leverages the ENCODE RBP data together with
ClinVar and integrates multiple computational approaches to aggregate evidence.  INCA
hinges upon evaluating the impact of the variants on the RBP activities by leveraging
the genotyped cell lines that harbor these variants.  We show that INCA provides
critical specificity for the set of candidate variants and their linkage disequilibrium
partners even after they are generically scored for impact on RBP binding.  As a result,
it can augment scoring of 46.2% of the candidate variants for follow-up on average.  

About the speaker: Jingqi Duan is a fourth-year Ph.D.  student in the Department of
Statistics at the University of Wisconsin-Madison.  She is currently a research
assistant in the Keles Research Group.  Her research focuses on advancing statistical
and computation methods tailored for the analysis of high-throughput sequencing data,
such as eCLIP-seq and Perturb-seq, with the aim of enhancing gene regulation analysis.