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

Gene structure alterations, such as chromosomal rearrangements that develop fusion genes, often contribute to tumorigenesis. It has been shown that the fusion genes identified in public RNA-sequencing datasets are mainly derived from intrachromosomal rearrangements. In this study, we explored fusion transcripts in clinical ovarian cancer specimens based on our RNA-sequencing data. We successfully identified an in-frame fusion transcript SPON1-TRIM29 in chromosome 11 from a recurrent tumor specimen of high-grade serous carcinoma (HGSC), which was not detected in the corresponding primary carcinoma, and validated the expression of the identical fusion transcript in another tumor from a distinct HGSC patient. Ovarian cancer A2780 cells stably expressing SPON1-TRIM29 exhibited an increase in cell growth, whereas a decrease in apoptosis was observed, even in the presence of anticancer drugs. The siRNA-mediated silencing of SPON1-TRIM29 fusion transcript substantially impaired the enhanced growth of A2780 cells expressing the chimeric gene treated with anticancer drugs. Moreover, a subcutaneous xenograft model using athymic mice indicated that SPON1-TRIM29-expressing A2780 cells rapidly generated tumors in vivo compared to control cells, whose growth was significantly repressed by the fusion-specific siRNA administration. Overall, the SPON1-TRIM29 fusion gene could be involved in carcinogenesis and chemotherapy resistance in ovarian cancer, and offers potential use as a diagnostic and therapeutic target for the disease with the fusion transcript.

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FusionCatcher - a tool for finding somatic fusion genes in paired-end RNA-sequencing data

10.1101/011650 ◽

2014 ◽

Cited By ~ 118

Author(s):

Daniel Nicorici ◽

Mihaela Satalan ◽

Henrik Edgren ◽

Sara Kangaspeska ◽

Astrid Murumagi ◽

...

Keyword(s):

Real Time ◽

Tumor Cells ◽

Rna Sequencing ◽

Real Time Pcr ◽

Software Tool ◽

Fusion Genes ◽

Sequencing Data ◽

Detection Rates ◽

Somatic Fusion

FusionCatcher is a software tool for finding somatic fusion genes in paired-end RNA-sequencing data from human or other vertebrates. FusionCatcher achieves competitive detection rates and real-time PCR validation rates in RNA-sequencing data from tumor cells. FusionCatcher is available at http://code.google.com/p/fusioncatcher

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InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data

PLoS ONE ◽

10.1371/journal.pone.0167417 ◽

2016 ◽

Vol 11 (12) ◽

pp. e0167417 ◽

Cited By ~ 16

Author(s):

Konstantin Okonechnikov ◽

Aki Imai-Matsushima ◽

Lukas Paul ◽

Alexander Seitz ◽

Thomas F. Meyer ◽

...

Keyword(s):

Rna Sequencing ◽

Fusion Genes ◽

Sequencing Data ◽

Chimeric Transcripts

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UNCURL-App: Interactive Database-Driven Analysis of Single Cell RNA Sequencing Data

10.1101/2020.04.15.043737 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yue Zhang ◽

Shunfu Mao ◽

Sumit Mukherjee ◽

Sreeram Kannan ◽

Georg Seelig

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Biological Knowledge ◽

Analysis Tool ◽

Sequencing Data ◽

Link Type ◽

Analysis Process ◽

Single Cell Rna Sequencing ◽

Key Innovations

AbstractAnalysis of single cell RNA sequencing (scRNA-Seq) datasets is a complex and time-consuming process, requiring both biological knowledge and technical skill. In order to simplify and systematize this process, we introduce UNCURL-App, an online GUI-based interactive scRNA-Seq analysis tool. UNCURL-App introduces two key innovations: First, prior knowledge in the form of cell type, anatomy, and Gene Ontology databases is integrated directly with the rest of the analysis process, allowing users to automatically map cell clusters to known cell types based on gene expression. Second, tools for interactive re-analysis allow the user to iteratively create, merge, or delete clusters in order to arrive at an optimal mapping between clusters and cell types.AvailabilityThe website is at https://uncurl.cs.washington.edu/. Source code is available at https://github.com/yjzhang/uncurl_app

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miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale

Nucleic Acids Research ◽

10.1093/nar/gkab268 ◽

2021 ◽

Author(s):

Tobias Fehlmann ◽

Fabian Kern ◽

Omar Laham ◽

Christina Backes ◽

Jeffrey Solomon ◽

...

Keyword(s):

Rna Sequencing ◽

Small Rna ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Small Rna Sequencing ◽

Analysis Tool ◽

Sequencing Data ◽

Effect Analysis ◽

Non Coding Rna ◽

Wide Range

Abstract Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at https://www.ccb.uni-saarland.de/mirmaster2.

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Reanalysis of RNA-Sequencing Data Reveals Several Additional Fusion Genes with Multiple Isoforms

PLoS ONE ◽

10.1371/journal.pone.0048745 ◽

2012 ◽

Vol 7 (10) ◽

pp. e48745 ◽

Cited By ~ 40

Author(s):

Sara Kangaspeska ◽

Susanne Hultsch ◽

Henrik Edgren ◽

Daniel Nicorici ◽

Astrid Murumägi ◽

...

Keyword(s):

Rna Sequencing ◽

Fusion Genes ◽

Sequencing Data

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CLAN: the CrossLinked reads ANalyais tool

10.1101/233841 ◽

2017 ◽

Author(s):

Cuncong Zhong ◽

Shaojie Zhang

Keyword(s):

Next Generation Sequencing ◽

Rna Sequencing ◽

Alignment Accuracy ◽

Analysis Tool ◽

Sequencing Data ◽

Sequencing Technology ◽

Read Mapping ◽

Functional Inference ◽

Generation Sequencing ◽

Novel Algorithm

AbstractThe crosslinked RNA sequencing technology ligates interacting RNA strands followed by next-generation sequencing. Mapping of the resulting duplex reads allows for functional inference of the corresponding intramolecular/intermolecular RNA-RNA interactions. However, duplex read mapping remains computationally challenging, and the existing best-performing software fails to map a significant portion of the duplex reads. To address this challenge, we develop a novel algorithm for duplex read mapping, called CrossLinked reads ANalysis tool (CLAN). CLAN demonstrates drastically improved sensitivity and high alignment accuracy when applied to real crosslinked RNA sequencing data. CLAN is implemented in GNU C++, and is freely available from http://sourceforge.net/projects/clan-mapping.

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HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

Genes ◽

10.3390/genes12010120 ◽

2021 ◽

Vol 12 (1) ◽

pp. 120

Author(s):

Yiyun Sun ◽

Dandan Xu ◽

Chundong Zhang ◽

Yitao Wang ◽

Lian Zhang ◽

...

Keyword(s):

Rna Sequencing ◽

Squamous Cell ◽

Gene Pair ◽

Gene List ◽

Ectopic Expression ◽

Lung Squamous Cell Carcinoma ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Gene Set

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

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