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Partner Group: Li - University of Chicago (Research Grant awardee)


Novel methods to facilitate identification and visualization of aberrant splicing associated with rare human disease

We will develop an innovative topic modeling approach to detect outlier RNA splicing at the isoform level. Topic modeling, also known as "factor analysis", has been used previously in many settings, e.g. to classify texts based on inferred "topics" and to estimate admixture from genetic data. Recently, topic modeling has been extended in several ways to model RNA-seq data. We propose to leverage the topic modeling approach for detecting and visualizing outlier splicing events. STM is well suited for modeling isoform diversity and outlier isoforms because the expression of a gene is composed of multiple isoforms, which can be thought of as different topics or factors.

Date approved