FuGePrior: A novel gene fusion prioritization algorithm based on accurate fusion structure analysis in cancer RNA-seq samples

Comparative study of bioinformatic tools for the identification of chimeric RNAs from RNA Sequencing

Chimeric RNAs are gaining more and more attention as they have broad implications in both cancer and normal physiology. To date, over 40 chimeric RNA prediction methods have been developed to facilitate their identification from RNA sequencing data. However, a limited number of studies have been conducted to compare the performance of these tools; additionally, previous studies have become outdated as more software tools have been developed within the last three years. In this study, we benchmarked 16 chimeric RNA prediction software, including seven top performers in previous benchmarking studies, and nine that were recently developed. We used two simulated and two real RNA-Seq datasets, compared the 16 tools for their sensitivity, positive prediction value (PPV), F-measure, and also documented the computational requirements (time and memory). We noticed that none of the tools are inclusive, and their performance varies depending on the dataset and objects. To increase the detection of true positive events, we also evaluated the pair-wise combination of these methods to suggest the best combination for sensitivity and F-measure. In addition, we compared the performance of the tools for the identification of three classes (read-through, inter-chromosomal and intra-others) of chimeric RNAs. Finally, we performed TOPSIS analyses and ranked the weighted performance of the 16 tools.

Novel and Rare Fusion Transcripts Involving Transcription Factors and Tumor Suppressor Genes in Acute Myeloid Leukemia

Approximately 18% of acute myeloid leukemia (AML) cases express a fusion transcript. However, few fusions are recurrent across AML and the identification of these rare chimeras is of interest to characterize AML patients. Here, we studied the transcriptome of 8 adult AML patients with poorly described chromosomal translocation(s), with the aim of identifying novel and rare fusion transcripts. We integrated RNA-sequencing data with multiple approaches including computational analysis, Sanger sequencing, fluorescence in situ hybridization and in vitro studies to assess the oncogenic potential of the ZEB2-BCL11B chimera. We detected 7 different fusions with partner genes involving transcription factors (OAZ-MAFK, ZEB2-BCL11B), tumor suppressors (SAV1-GYPB, PUF60-TYW1, CNOT2-WT1) and rearrangements associated with the loss of NF1 (CPD-PXT1, UTP6-CRLF3). Notably, ZEB2-BCL11B rearrangements co-occurred with FLT3 mutations and were associated with a poorly differentiated or mixed phenotype leukemia. Although the fusion alone did not transform murine c-Kit+ bone marrow cells, 45.4% of 14q32 non-rearranged AML cases were also BCL11B-positive, suggesting a more general and complex mechanism of leukemogenesis associated with BCL11B expression. Overall, by combining different approaches, we described rare fusion events contributing to the complexity of AML and we linked the expression of some chimeras to genomic alterations hitting known genes in AML.

Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods

BACKGROUND

Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly.

RESULTS

We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes.

CONCLUSION

The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.

Transcriptional-Readthrough RNAs Reflect the Phenomenon of "A Gene Contains Gene(s)" or "Gene(s) within a Gene" in the Human Genome, and Thus Are Not Chimeric RNAs

Tens of thousands of chimeric RNAs, i.e., RNAs with sequences of two genes, have been identified in human cells. Most of them are formed by two neighboring genes on the same chromosome and are considered to be derived via transcriptional readthrough, but a true readthrough event still awaits more evidence and trans-splicing that joins two transcripts together remains as a possible mechanism. We regard those genomic loci that are transcriptionally read through as unannotated genes, because their transcriptional and posttranscriptional regulations are the same as those of already-annotated genes, including fusion genes formed due to genetic alterations. Therefore, readthrough RNAs and fusion-gene-derived RNAs are not chimeras. Only those two-gene RNAs formed at the RNA level, likely via trans-splicing, without corresponding genes as genomic parents, should be regarded as authentic chimeric RNAs. However, since in human cells, procedural and mechanistic details of trans-splicing have never been disclosed, we doubt the existence of trans-splicing. Therefore, there are probably no authentic chimeras in humans, after readthrough and fusion-gene derived RNAs are all put back into the group of ordinary RNAs. Therefore, it should be further determined whether in human cells all two-neighboring-gene RNAs are derived from transcriptional readthrough and whether trans-splicing truly exists.