FuMa: reporting overlap in RNA-seq detected fusion genes

The landscape of fusion transcripts in plants: a new insight into genome complexity

BACKGROUND

Fusion transcripts (FTs), generated by the fusion of genes at the DNA level or RNA-level splicing events significantly contribute to transcriptome diversity. FTs are usually considered unique features of neoplasia and serve as biomarkers and therapeutic targets for multiple cancers. The latest findings show the presence of FTs in normal human physiology. Several discrete reports mentioned the presence of fusion transcripts in planta, has important roles in stress responses, morphological alterations, or traits (e.g. seed size, etc.).

RESULTS

In this study, we identified 169,197 fusion transcripts in 2795 transcriptome datasets of Arabidopsis thaliana, Cicer arietinum, and Oryza sativa by using a combination of tools, and confirmed the translational activity of 150 fusion transcripts through proteomic datasets. Analysis of the FT junction sequences and their association with epigenetic factors, as revealed by ChIP-Seq datasets, demonstrated an organised process of fusion formation at the DNA level. We investigated the possible impact of three-dimensional chromatin conformation on intra-chromosomal fusion events by leveraging the Hi-C datasets with the incidence of fusion transcripts. We further utilised the long-read RNA-Seq datasets to validate the most reoccurring fusion transcripts in each plant species followed by further authentication through RT-PCR and Sanger sequencing.

CONCLUSIONS

Our findings suggest that a significant portion of fusion events may be attributed to alternative splicing during transcription, accounting for numerous fusion events without a proportional increase in the number of RNA pairs. Even non-nuclear DNA transcripts from mitochondria and chloroplasts can participate in intra- and inter-chromosomal fusion formation. Genes in close spatial proximity are more prone to undergoing fusion formation, especially in intra-chromosomal FTs. Most of the fusion transcripts may not undergo translation and serve as long non-coding RNAs. The low validation rate of FTs in plants indicated that the fusion transcripts are expressed at very low levels, like in the case of humans. FTs often originate from parental genes involved in essential biological processes, suggesting their relevance across diverse tissues and stress conditions. This study presents a comprehensive repository of fusion transcripts, offering valuable insights into their roles in vital physiological processes and stress responses.

Molecular Profiling Reclassifies Adult Astroblastoma into Known and Clinically Distinct Tumor Entities with Frequent Mitogen-Activated Protein Kinase Pathway Alterations

BACKGROUND

Astroblastoma (ABM) is a rare glial brain tumor. Recurrent meningioma 1 (MN1) alterations have been recently identified in most pediatric cases. Adolescent and adult cases, however, remain molecularly poorly defined.

MATERIALS AND METHODS

We performed clinical and molecular characterization of a retrospective cohort of 14 adult and 1 adolescent ABM.

RESULTS

Strikingly, we found that MN1 fusions are a rare event in this age group (1/15). Using methylation profiling and targeted sequencing, most cases were reclassified as either pleomorphic xanthoastrocytomas (PXA)-like or high-grade glioma (HGG)-like. PXA-like ABM show BRAF mutation (6/7 with V600E mutation and 1/7 with G466E mutation) and CD34 expression. Conversely, HGG-like ABM harbored specific alterations of diffuse midline glioma (2/5) or glioblastoma (GBM; 3/5). These latter patients showed an unfavorable clinical course with significantly shorter overall survival (p = .021). Mitogen-activated protein kinase pathway alterations (including FGFR fusion, BRAF and NF1 mutations) were present in 10 of 15 patients and overrepresented in the HGG-like group (3/5) compared with previously reported prevalence of these alterations in GBM and diffuse midline glioma.

CONCLUSION

We suggest that gliomas with astroblastic features include a variety of molecularly sharply defined entities. Adult ABM harboring molecular features of PXA and HGG should be reclassified. Central nervous system high-grade neuroepithelial tumors with MN1 alterations and histology of ABM appear to be uncommon in adults. Astroblastic morphology in adults should thus prompt thorough molecular investigation aiming at a clear histomolecular diagnosis and identifying actionable drug targets, especially in the mitogen-activated protein kinase pathway.

IMPLICATIONS FOR PRACTICE

Astroblastoma (ABM) remains a poorly defined and controversial entity. Although meningioma 1 alterations seem to define a large subset of pediatric cases, adult cases remain molecularly poorly defined. This comprehensive molecular characterization of 1 adolescent and 14 adult ABM revealed that adult ABM histology comprises several molecularly defined entities, which explains clinical diversity and identifies actionable targets. Namely, pleomorphic xanthoastrocytoma-like ABM cases show a favorable prognosis whereas high-grade glioma (glioblastoma and diffuse midline gliome)-like ABM show significantly worse clinical courses. These results call for in-depth molecular analysis of adult gliomas with astroblastic features for diagnostic and therapeutic purposes.

Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data

Recurrent oncogenic fusion genes play a critical role in the development of various cancers and diseases and provide, in some cases, excellent therapeutic targets. To date, analysis tools that can identify and compare recurrent fusion genes across multiple samples have not been available to researchers. To address this deficiency, we developed Co-occurrence Fusion (Co-fuse), a new and easy to use software tool that enables biologists to merge RNA-seq information, allowing them to identify recurrent fusion genes, without the need for exhaustive data processing. Notably, Co-fuse is based on pattern mining and statistical analysis which enables the identification of hidden patterns of recurrent fusion genes. In this report, we show that Co-fuse can be used to identify 2 distinct groups within a set of 49 leukemic cell lines based on their recurrent fusion genes: a multiple myeloma (MM) samples-enriched cluster and an acute myeloid leukemia (AML) samples-enriched cluster. Our experimental results further demonstrate that Co-fuse can identify known driver fusion genes (e.g., IGH-MYC, IGH-WHSC1) in MM, when compared to AML samples, indicating the potential of Co-fuse to aid the discovery of yet unknown driver fusion genes through cohort comparisons. Additionally, using a 272 primary glioma sample RNA-seq dataset, Co-fuse was able to validate recurrent fusion genes, further demonstrating the power of this analysis tool to identify recurrent fusion genes. Taken together, Co-fuse is a powerful new analysis tool that can be readily applied to large RNA-seq datasets, and may lead to the discovery of new disease subgroups and potentially new driver genes, for which, targeted therapies could be developed. The Co-fuse R source code is publicly available at https://github.com/sakrapee/co-fuse .

The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis.

AVAILABILITY

The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench.

Identifying fusion transcripts using next generation sequencing

Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions have been used successfully for cancer diagnosis, prognosis, and therapeutic applications. In addition, many fusion transcripts are found in normal human cell lines and tissues, with some data supporting their role in normal physiology. Besides chromosomal rearrangement, intergenic splicing can generate them. Global identification of fusion transcripts becomes possible with the help of next generation sequencing technology like RNA-Seq. In the past decade, major advancements have been made for chimeric RNA discovery due to the development of advanced sequencing platform and software packages. However, current software tools behave differently in terms of specificity, sensitivity, time, and computational memory usage. Recent benchmarking studies showed that none of the tools are inclusive. The development of high performance (accurate and fast), and user-friendly fusion detection tool/pipeline is still an open quest. In this article, we review the existing software packages for fusion detection. We explain the methods of the tools, and discuss various factors that affect fusion detection. We summarize conclusions drawn from several comparative studies, and then discuss some of the pitfalls of these studies. We also describe the limitations of current tools, and suggest directions for future development. WIREs RNA 2016, 7:811-823. doi: 10.1002/wrna.1382 For further resources related to this article, please visit the WIREs website.