Relative Index of Chimeric Expression (RICE) Analysis: A Quantitative Approach for Chimeric RNAs Using FusionBlaster

Abstract

Chimeric RNA molecules, which contain nucleotide sequences originating from multiple genes, are generated by chromosomal rearrangements, transcriptional read-throughs, or trans-splicing between separate parental transcripts. Chimeric RNAs have been functionally validated in both pathological and normal healthy physiological contexts indicating the biological significance of chimeric RNA expression. There is, however, currently no standard for computationally quantifying chimeric RNA expression and only limited benchmarking data available for the few chimeric RNA detection software that attempt to measure the abundance of the predicted chimeras. Here, we develop the relative index of chimeric expression, RICE, that is calculated based on the relative expression of chimeric transcripts compared to the respective parental WT transcripts. We evaluate three different methods for generating this measurement from simulated RNA sequencing data with known transcript abundances. Our BLAST-based approach outperforms STAR and Kallisto based approaches when considering both accuracy and consistency between simulated data of different read lengths and sequencing depths. We further demonstrate that RICE values can be validated using qPCR and are sensitive to dynamic conditions using siRNA targeting chimeric RNA expression. Finally, we apply our RICE analysis pipeline to clinical prostate cancer data. We quantify over 1200 chimeric RNAs in primary prostate cancer, metastatic prostate cancer, and non-cancer tissue samples from GTEx. Our differential RICE analysis revealed a clustering of prostate cancer tissue samples from three different sequencing cohorts distinct from their associated tissue type noncancer GTEx clusters. Our pipeline is publicly available on github and can be run on a personal laptop with computational resources and processing time dependent on the number of quantified chimeras.