RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data

Abstract

Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.

Keywords: RNA-Seq; RTCpredictor; chimeric RNA; read-through.

Figure 1

Flowchart of the RTCpredictor method. Exemplary genes A and B are located on the same strand with five and four exons, respectively. There are 20 possible exon combinations with the distance between breakpoints is ≤70 kb, which is used to generate a database of junction sequences of potential read-throughs. Pre-filters are applied to discard potential false positive events. dbSNP is used to map and add common variants on junction sequences to generate the final database. This final database is used to search against RNA-Seq input data using ripgrep for the initial predicted read-throughs. Post-filters are applied to exclude additional potential false positive events to generate the final list of predicted read-throughs. The annotations including genomic coordinates and junction sequences are included in the output file, which will help the users to design primers for their experimental validation.

Figure 2

Performance of RTCpredictor and its comparison with other methods on the ChimPipe-simulated (PE50, PE76 and PE101) datasets. (A) % Sensitivity achieved by RTCpredictor and (B) total number of predicted read-throughs after sequential addition of different filters. (C) Comparison of % Sensitivity of RTCpredictor with other methods.

Figure 3

Performance of RTCpredictor and its comparison with other methods on the Qin et al. real dataset. (A) % Sensitivity achieved by RTCpredictor, (B) total number of predicted read-throughs, after sequential addition of different filters, and (C) comparison of % Sensitivity of RTCpredictor with other methods.

Figure 4

Experimental validation results of a subset of predicted read-through chimeric RNAs from the extended Qin et al. real dataset. (A) Experimental validation using RT-PCR. Bands at the bottom of the gel are primer dimers. (B) Junction sequences obtained from Sanger sequencing validation and visualization on UCSC genome browser for the following exemplary read-through chimeric RNAs (i) TBC1D24-ATP6V0C, (ii) ARID3C-DCTN3, (iii) BBS7-CCNA2, (iv) SEH1L-CEP192 and (v) ZCCHC6-ISCA1.

Figure 5

Computational requirements (time and memory) of RTCpredictor when (A) a single core is used and (B) multiple cores are used.