ChimPipe: accurate detection of fusion genes and transcription-induced chimeras from RNA-seq data

Architects and Partners: The Dual Roles of Non-coding RNAs in Gene Fusion Events

Extensive research into gene fusions in cancer and other diseases has led to the discovery of novel biomarkers and therapeutic targets. Concurrently, various bioinformatics tools have been developed for fusion detection in RNA sequencing data, which, in the age of increasing affordability of sequencing, have delivered a large-scale identification of transcriptomic abnormalities. Historically, the focus of fusion transcript research was predominantly on coding RNAs and their resultant proteins, often overlooking non-coding RNAs (ncRNAs). This chapter discusses how ncRNAs are integral players in the landscape of gene fusions, detailing their contributions to the formation of gene fusions and their presence in chimeric transcripts. We delve into both linear and the more recently identified circular fusion RNAs, providing a comprehensive overview of the computational methodologies used to detect ncRNA-involved gene fusions. Additionally, we examine the inherent biases and limitations of these bioinformatics approaches, offering insights into the challenges and future directions in this dynamic field.

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

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.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

WFA-GPU: gap-affine pairwise read-alignment using GPUs

MOTIVATION

Advances in genomics and sequencing technologies demand faster and more scalable analysis methods that can process longer sequences with higher accuracy. However, classical pairwise alignment methods, based on dynamic programming (DP), impose impractical computational requirements to align long and noisy sequences like those produced by PacBio and Nanopore technologies. The recently proposed wavefront alignment (WFA) algorithm paves the way for more efficient alignment tools, improving time and memory complexity over previous methods. However, high-performance computing (HPC) platforms require efficient parallel algorithms and tools to exploit the computing resources available on modern accelerator-based architectures.

RESULTS

This paper presents WFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute exact gap-affine alignments based on the WFA algorithm. We present the algorithmic adaptations and performance optimizations that allow exploiting the massively parallel capabilities of modern GPU devices to accelerate the alignment computations. In particular, we propose a CPU-GPU co-design capable of performing inter-sequence and intra-sequence parallel sequence alignment, combining a succinct WFA-data representation with an efficient GPU implementation. As a result, we demonstrate that our implementation outperforms the original multi-threaded WFA implementation by up to 4.3× and up to 18.2× when using heuristic methods on long and noisy sequences. Compared to other state-of-the-art tools and libraries, the WFA-GPU is up to 29× faster than other GPU implementations and up to four orders of magnitude faster than other CPU implementations. Furthermore, WFA-GPU is the only GPU solution capable of correctly aligning long reads using a commodity GPU.

AVAILABILITY AND IMPLEMENTATION

WFA-GPU code and documentation are publicly available at https://github.com/quim0/WFA-GPU.

Optimal gap-affine alignment in O(s) space

MOTIVATION

Pairwise sequence alignment remains a fundamental problem in computational biology and bioinformatics. Recent advances in genomics and sequencing technologies demand faster and scalable algorithms that can cope with the ever-increasing sequence lengths. Classical pairwise alignment algorithms based on dynamic programming are strongly limited by quadratic requirements in time and memory. The recently proposed wavefront alignment algorithm (WFA) introduced an efficient algorithm to perform exact gap-affine alignment in O(ns) time, where s is the optimal score and n is the sequence length. Notwithstanding these bounds, WFA's O(s2) memory requirements become computationally impractical for genome-scale alignments, leading to a need for further improvement.

RESULTS

In this article, we present the bidirectional WFA algorithm, the first gap-affine algorithm capable of computing optimal alignments in O(s) memory while retaining WFA's time complexity of O(ns). As a result, this work improves the lowest known memory bound O(n) to compute gap-affine alignments. In practice, our implementation never requires more than a few hundred MBs aligning noisy Oxford Nanopore Technologies reads up to 1 Mbp long while maintaining competitive execution times.

AVAILABILITY AND IMPLEMENTATION

All code is publicly available at https://github.com/smarco/BiWFA-paper.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RTCpredictor: Identification of Read-Through Chimeric RNAs from RNA Sequencing Data

Read-through chimeric RNAs are gaining attention in cancer and other research fields, yet current tools often fail in predicting them. We have thus developed the first read-through chimeric RNA specific prediction method, RTCpredictor, utilizing a fast ripgrep algorithm to search for all possible exon-exon combinations of parental gene pairs. Compared with other ten popular tools, RTCpredictor achieved top performance on both simulated and real datasets. We randomly selected up to 30 candidate read-through chimeras predicted from each software method and experimentally validated a total of 109 read-throughs and on this set, RTCpredictor outperformed all the other methods. In addition, RTCpredictor ( https://github.com/sandybioteck/RTCpredictor ) has less memory requirements and faster execution time.

Molecular profiling identifies targeted therapy opportunities in pediatric solid cancer

To evaluate the clinical impact of molecular tumor profiling (MTP) with targeted sequencing panel tests, pediatric patients with extracranial solid tumors were enrolled in a prospective observational cohort study at 12 institutions. In the 345-patient analytical population, median age at diagnosis was 12 years (range 0-27.5); 298 patients (86%) had 1 or more alterations with potential for impact on care. Genomic alterations with diagnostic, prognostic or therapeutic significance were present in 61, 16 and 65% of patients, respectively. After return of the results, impact on care included 17 patients with a clarified diagnostic classification and 240 patients with an MTP result that could be used to select molecularly targeted therapy matched to identified alterations (MTT). Of the 29 patients who received MTT, 24% had an objective response or experienced durable clinical benefit; all but 1 of these patients received targeted therapy matched to a gene fusion. Of the diagnostic variants identified in 209 patients, 77% were gene fusions. MTP with targeted panel tests that includes fusion detection has a substantial clinical impact for young patients with solid tumors.

From karyotypes to precision genomics in 9p deletion and duplication syndromes

While 9p deletion and duplication syndromes have been studied for several years, small sample sizes and minimal high-resolution data have limited a comprehensive delineation of genotypic and phenotypic characteristics. In this study, we examined genetic data from 719 individuals in the worldwide 9p Network Cohort: a cohort seven to nine times larger than any previous study of 9p. Most breakpoints occur in bands 9p22 and 9p24, accounting for 35% and 38% of all breakpoints, respectively. Bands 9p11 and 9p12 have the fewest breakpoints, with each accounting for 0.6% of all breakpoints. The most common phenotype in 9p deletion and duplication syndromes is developmental delay, and we identified eight known neurodevelopmental disorder genes in 9p22 and 9p24. Since it has been previously reported that some individuals have a secondary structural variant related to the 9p variant, we examined our cohort for these variants and found 97 events. The top secondary variant involved 9q in 14 individuals (1.9%), including ring chromosomes and inversions. We identified a gender bias with significant enrichment for females (p = 0.0006) that may arise from a sex reversal in some individuals with 9p deletions. Genes on 9p were characterized regarding function, constraint metrics, and protein-protein interactions, resulting in a prioritized set of genes for further study. Finally, we achieved precision genomics in one child with a complex 9p structural variation using modern genomic technologies, demonstrating that long-read sequencing will be integral for some cases. Our study is the largest ever on 9p-related syndromes and provides key insights into genetic factors involved in these syndromes.

The Fusion of CLEC12A and MIR223HG Arises from a trans-Splicing Event in Normal and Transformed Human Cells

Chimeric RNAs are often associated with chromosomal rearrangements in cancer. In addition, they are also widely detected in normal tissues, contributing to transcriptomic complexity. Despite their prevalence, little is known about the characteristics and functions of chimeric RNAs. Here, we examine the genetic structure and biological roles of CLEC12A-MIR223HG, a novel chimeric transcript produced by the fusion of the cell surface receptor CLEC12A and the miRNA-223 host gene (MIR223HG), first identified in chronic myeloid leukemia (CML) patients. Surprisingly, we observed that CLEC12A-MIR223HG is not just expressed in CML, but also in a variety of normal tissues and cell lines. CLEC12A-MIR223HG expression is elevated in pro-monocytic cells resistant to chemotherapy and during monocyte-to-macrophage differentiation. We observed that CLEC12A-MIR223HG is a product of trans-splicing rather than a chromosomal rearrangement and that transcriptional activation of CLEC12A with the CRISPR/Cas9 Synergistic Activation Mediator (SAM) system increases CLEC12A-MIR223HG expression. CLEC12A-MIR223HG translates into a chimeric protein, which largely resembles CLEC12A but harbours an altered C-type lectin domain altering key disulphide bonds. These alterations result in differences in post-translational modifications, cellular localization, and protein-protein interactions. Taken together, our observations support a possible involvement of CLEC12A-MIR223HG in the regulation of CLEC12A function. Our workflow also serves as a template to study other uncharacterized chimeric RNAs.

Comparative Analysis of PacBio and Oxford Nanopore Sequencing Technologies for Transcriptomic Landscape Identification of Penaeus monodon

With the advantages that long-read sequencing platforms such as Pacific Biosciences (Menlo Park, CA, USA) (PacBio) and Oxford Nanopore Technologies (Oxford, UK) (ONT) can offer, various research fields such as genomics and transcriptomics can exploit their benefits. Selecting an appropriate sequencing platform is undoubtedly crucial for the success of the research outcome, thus there is a need to compare these long-read sequencing platforms and evaluate them for specific research questions. This study aims to compare the performance of PacBio and ONT platforms for transcriptomic analysis by utilizing transcriptome data from three different tissues (hepatopancreas, intestine, and gonads) of the juvenile black tiger shrimp, Penaeus monodon. We compared three important features: (i) main characteristics of the sequencing libraries and their alignment with the reference genome, (ii) transcript assembly features and isoform identification, and (iii) correlation of the quantification of gene expression levels for both platforms. Our analyses suggest that read-length bias and differences in sequencing throughput are highly influential factors when using long reads in transcriptome studies. These comparisons can provide a guideline when designing a transcriptome study utilizing these two long-read sequencing technologies.

Identification of the cross-strand chimeric RNAs generated by fusions of bi-directional transcripts

A major part of the transcriptome complexity is attributed to multiple types of DNA or RNA fusion events, which take place within a gene such as alternative splicing or between different genes such as DNA rearrangement and trans-splicing. In the present study, using the RNA deep sequencing data, we systematically survey a type of non-canonical fusions between the RNA transcripts from the two opposite DNA strands. We name the products of such fusion events cross-strand chimeric RNA (cscRNA). Hundreds to thousands of cscRNAs can be found in human normal tissues, primary cells, and cancerous cells, and in other species as well. Although cscRNAs exhibit strong tissue-specificity, our analysis identifies thousands of recurrent cscRNAs found in multiple different samples. cscRNAs are mostly originated from convergent transcriptions of the annotated genes and their anti-sense DNA. The machinery of cscRNA biogenesis is unclear, but the cross-strand junction events show some features related to RNA splicing. The present study is a comprehensive survey of the non-canonical cross-strand RNA junction events, a resource for further characterization of the originations and functions of the cscRNAs.

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.

Role of chimeric transcript formation in the pathogenesis of birth defects

Chimeric transcripts are formed by chromosomal aberrations. Little is known about the role of chimeric transcripts in the pathogenesis of birth defects. We reanalyzed RNA-seq data in alignment map files from the peripheral blood of 56 patients in whom the diagnoses could not be confirmed by standard exome analysis and transcriptome analysis to screen for chimeric transcripts using a dedicated software, ChimPipe. Chimeric analysis led to a diagnosis in two of the 56 patients: (a) the first patient had a chimeric transcript spanning the causative gene ZEB2 and the GTDC1 gene in its neighboring locus. RNA-seq revealed reads spanning exon 5 of ZEB2 and exon 7 of GTDC1. Whole genome sequencing revealed a 436-kb deletion spanning intron 4 of ZEB2 and intron 7 of GTDC1 and the diagnosis of Mowat-Wilson syndrome was made. (b) The second patient had a chimeric transcript spanning the causative gene KCNK9 and the TRAPPC9 gene in its neighboring locus. RNA-seq revealed reads spanning exon 21 of TRAPPC9 and exon 1 of KCNK9. Whole genome sequencing revealed a 186-kb deletion spanning intron 20 of TRAPPC9 and intron 1 of KCNK9 in this patient. KCNK9 gene is a maternally expressed imprinted gene. The diagnosis of Birk-Barel syndrome was made. Thus, both patients had chimeric transcripts that were directly involved in the pathogenesis of the birth defects. The approach reported herein, of detecting chimeric transcripts from RNA-seq data, is unique in that the approach does not rely on any prior information on the presence of genomic deletion.

Detection of novel and recurrent conjoined genes in non-Hodgkin B-cell lymphoma

For this study, we investigated comprehensive expression of conjoined genes (CGs) in non-Hodgkin B-cell lymphoma (B-NHL) cell line KPUM-UH1 by using paired-end RNA sequencing. Furthermore, we analyzed the expression of these transcripts in an additional 21 cell lines, 37 primary samples of various malignancies and peripheral blood mononuclear cells of four normal individuals. Seventeen CGs were detected in KPUM-UH1: CTBS-GNG5, SRP9-EPHX1, RMND5A-ANAPC, OTX1-EHBP1, ATF2-CHN1, PRKAA1-TTC33, LARP1-MRPL22, LOC105379697-BAK1, TIAM2-SCAF8, SPAG1-VPS13B, WBP1L-CNNM2, NARS2-GAB2, CTSC-RAB38, VAMP1-CD27-AS1, LRRC37A2-NSF, UBA2-WTIP and ZNF600-ZNF611. To our knowledge, 10 of these genes have not been previously reported. The various characteristics of the CGs included in- and out-of-frame fusions, chimeras involving non-coding RNA and transcript variants. A finding of note was that LARP1-MRPL2 was characterized as in-frame fusion and was recurrently expressed in B-NHL samples. In this study, variety of CGs was expressed both in malignant and normal cells, some of which might be specific to lymphoma.

LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing

BACKGROUND

Long-read RNA-Seq techniques can generate reads that encompass a large proportion or the entire mRNA/cDNA molecules, so they are expected to address inherited limitations of short-read RNA-Seq techniques that typically generate < 150 bp reads. However, there is a general lack of software tools for gene fusion detection from long-read RNA-seq data, which takes into account the high basecalling error rates and the presence of alignment errors.

RESULTS

In this study, we developed a fast computational tool, LongGF, to efficiently detect candidate gene fusions from long-read RNA-seq data, including cDNA sequencing data and direct mRNA sequencing data. We evaluated LongGF on tens of simulated long-read RNA-seq datasets, and demonstrated its superior performance in gene fusion detection. We also tested LongGF on a Nanopore direct mRNA sequencing dataset and a PacBio sequencing dataset generated on a mixture of 10 cancer cell lines, and found that LongGF achieved better performance to detect known gene fusions over existing computational tools. Furthermore, we tested LongGF on a Nanopore cDNA sequencing dataset on acute myeloid leukemia, and pinpointed the exact location of a translocation (previously known in cytogenetic resolution) in base resolution, which was further validated by Sanger sequencing.

CONCLUSIONS

In summary, LongGF will greatly facilitate the discovery of candidate gene fusion events from long-read RNA-Seq data, especially in cancer samples. LongGF is implemented in C++ and is available at https://github.com/WGLab/LongGF .

Evidence of constraint in the 3D genome for trans-splicing in human cells

Fusion transcripts are commonly found in eukaryotes, and many aberrant fusions are associated with severe diseases, including cancer. One class of fusion transcripts is generated by joining separate transcripts through trans-splicing. However, the mechanism of trans-splicing in mammals remains largely elusive. Here we showed evidence to support an intuitive hypothesis that attributes trans-sphcing to the spatial proximity between premature transcripts. A novel trans-splicing detection tool (TSD) was developed to reliably identify intra-chromosomal trans-splicing events (iTSEs) from RNA-seq data. TSD can maintain a remarkable balance between sensitivity and accuracy, thus distinguishing it from most state-of-the-art tools. The accuracy of TSD was experimentally demonstrated by excluding potential false discovery from mosaic genome or template switching during PCR. We showed that iTSEs identified by TSD were frequently found between genomic regulatory elements, which are known to be more prone to interact with each other. Moreover, iTSE sites may be more physically adjacent to each other than random control in the tested human lymphoblastoid cell line according to Hi-C data. Our results suggest that trans-splicing and 3D genome architecture may be coupled in mammals and that our pipeline, TSD, may facilitate investigations of trans-splicing on a systematic and accurate level previously thought impossible.

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.

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

FusionPro, a Versatile Proteogenomic Tool for Identification of Novel Fusion Transcripts and Their Potential Translation Products in Cancer Cells

Fusion proteoforms are translation products derived from gene fusion. Although very rare, the fusion proteoforms play important roles in biomedical science. For example, fusion proteoforms influence the development of tumors by serving as cancer markers or cell cycle regulators. Although numerous studies have reported bioinformatics tools that can predict fusion transcripts, few proteogenomic tools are available that can predict and identify proteoforms. In this study, we develop a versatile proteogenomic tool "FusionPro," which facilitates the identification of fusion transcripts and their potential translatable peptides. FusionPro provides an independent gene fusion prediction module and can build sequence databases for annotated fusion proteoforms. FusionPro shows greater sensitivity than the available fusion finders when analyzing simulated or real RNA sequencing data sets. We use FusionPro to identify 18 fusion junction peptides and three potential fusion-derived peptides by MS/MS-based analysis of leukemia cell lines (Jurkat and K562) and ovarian cancer tissues from the Clinical Proteomic Tumor Analysis Consortium. Among the identified fusion proteins, we molecularly validate two fusion junction isoforms and a translation product of FAM133B:CDK6. Moreover, sequence analysis suggests that the fusion protein participates in the cell cycle progression. In addition, our prediction results indicate that fusion transcripts often have multiple fusion junctions and that these fusion junctions tend to be distributed in a nonrandom pattern at both the chromosome and gene levels. Thus, FusionPro allows users to detect various types of fusion translation products using a transcriptome-informed approach and to gain a comprehensive understanding of the formation and biological roles of fusion proteoforms.

Identification of the UBA2-WTIP fusion gene in acute myeloid leukemia

Identifying and targeting oncogenic fusion genes have revolutionized the treatment of leukemia, such as PML-RARα fusion gene in acute promyelocytic leukemia. Here we identified an intrachromosomal fusion gene located on chromosome 19q.13 between UBA2 and WTIP gene in a case of acute myeloid leukemia. The UBA2-WTIP fusion gene contains the N-terminal E1_enzyme_family, VAE_Ubl domains of UBA2, and the C-terminal LIM domains of WTIP. The UBA2-WTIP fusion was detected by reverse transcriptase polymerase chain reaction and Sanger sequencing in 19 of 56 acute myeloid leukemia samples (33.9%). Ectopic expression of the UBA2-WTIP fusion in human acute myeloid leukemia KG-1a cells showed enhanced cell proliferation both in vitro and in vivo. The UBA2-WTIP fusion induced phosphorylation of STAT3, STAT5 and ERK1/2, and abrogates WTIP-mediated mammalian processing body formation. Finally, triptolide displayed selective cytotoxicity against KG-1a cells harboring the UBA2-WTIP fusion. Collectively, our findings suggest that the UBA2-WTIP fusion is an oncogenic fusion gene, as well as a promising therapeutic target for the treatment of acute myeloid leukemia.

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.

Connections between Transcription Downstream of Genes and cis-SAGe Chimeric RNA

cis-Splicing between adjacent genes (cis-SAGe) is being recognized as one way to produce chimeric fusion RNAs. However, its detail mechanism is not clear. Recent study revealed induction of transcriptions downstream of genes (DoGs) under osmotic stress. Here, we investigated the influence of osmotic stress on cis-SAGe chimeric RNAs and their connection to DoGs. We found, the absence of induction of at least some cis-SAGe fusions and/or their corresponding DoGs at early time point(s). In fact, these DoGs and their cis-SAGe fusions are inversely correlated. This negative correlation was changed to positive at a later time point. These results suggest a direct competition between the two categories of transcripts when total pool of readthrough transcripts is limited at an early time point. At a later time point, DoGs and corresponding cis-SAGe fusions are both induced, indicating that total readthrough transcripts become more abundant. Finally, we observed overall enhancement of cis-SAGe chimeric RNAs in KCl-treated samples by RNA-Seq analysis.

GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data

Fusion gene derived from genomic rearrangement plays a key role in cancer initiation. The discovery of novel gene fusions may be of significant importance in cancer diagnosis and treatment. Meanwhile, next generation sequencing technology provide a sensitive and efficient way to identify gene fusions in genomic levels. However, there are still many challenges and limitations remaining in the existing methods which only rely on unmapped reads or discordant alignment fragments. In this work we have developed GFusion, a novel method using RNA-Seq data, to identify the fusion genes. This pipeline performs multiple alignments and strict filtering algorithm to improve sensitivity and reduce the false positive rate. GFusion successfully detected 34 from 43 previously reported fusions in four cancer datasets. We also demonstrated the effectiveness of GFusion using 24 million 76 bp paired-end reads simulation data which contains 42 artificial fusion genes, among which GFusion successfully discovered 37 fusion genes. Compared with existing methods, GFusion presented higher sensitivity and lower false positive rate. The GFusion pipeline can be accessed freely for non-commercial purposes at: https://github.com/xiaofengsong/GFusion .