Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript

Benelli, Matteo; Pescucci, Chiara; Marseglia, Giuseppina; Severgnini, Marco; Torricelli, Francesca; Magi, Alberto

doi:10.1093/bioinformatics/bts617

Cited by 164 publications

(126 citation statements)

References 28 publications

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“…The deep-sequencing data were mapped to human genome version hg19 and analyzed by using the software SOAPfuse (32). The human tissue RNA-seq was analyzed by EricScript (24). The identified chimeric RNAs were presented by using Circos as described (33).…”

Section: Methodsmentioning

confidence: 99%

“…From paired-end RNA sequencing libraries covering 27 different tissues of 95 noncancer donors, we detected 16,026 fusion transcripts using a different software, EricScript (24), which is faster and yielded more fusion transcripts for downstream analysis. Similar to our previous findings, we found that fusion RNA profiles in each tissue or cell type were very different (Fig.…”

Section: Fusion Rna Profiling Connects Samples With Same or Similar Tmentioning

confidence: 99%

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Fusion transcriptome profiling provides insights into alveolar rhabdomyosarcoma

Xie

Babiceanu

Kumar

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Gene fusions and fusion products were thought to be unique features of neoplasia. However, more and more studies have identified fusion RNAs in normal physiology. Through RNA sequencing of 27 human noncancer tissues, a large number of fusion RNAs were found. By analyzing fusion transcriptome, we observed close clusterings between samples of same or similar tissues, supporting the feasibility of using fusion RNA profiling to reveal connections between biological samples. To put the concept into use, we selected alveolar rhabdomyosarcoma (ARMS), a myogenic pediatric cancer whose exact cell of origin is not clear. PAX3-FOXO1 (paired box gene 3 fused with forkhead box O1) fusion RNA, which is considered a hallmark of ARMS, was recently found during normal muscle cell differentiation. We performed and analyzed RNA sequencing from various time points during myogenesis and uncovered many chimeric fusion RNAs. Interestingly, we found that the fusion RNA profile of RH30, an ARMS cell line, is most similar to the myogenesis time point when PAX3-FOXO1 is expressed. In contrast, full transcriptome clustering analysis failed to uncover this connection. Strikingly, all of the 18 chimeric RNAs in RH30 cells could be detected at the same myogenic time point(s). In addition, the seven chimeric RNAs that follow the exact transient expression pattern as PAX3-FOXO1 are specific to rhabdomyosarcoma cells. Further testing with clinical samples also confirmed their specificity to rhabdomyosarcoma. These results provide further support for the link between at least some ARMSs and the PAX3-FOXO1-expressing myogenic cells and demonstrate that fusion RNA profiling can be used to investigate the etiology of fusion-gene-associated cancers.rhabdomyosarcoma | gene fusion | chimeric RNA | myogenesis | PAX3-FOXO1

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Section: Methodsmentioning

confidence: 99%

Section: Fusion Rna Profiling Connects Samples With Same or Similar Tmentioning

confidence: 99%

Fusion transcriptome profiling provides insights into alveolar rhabdomyosarcoma

Xie

Babiceanu

Kumar

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Repeat the procedure described above in steps 1 through 6 twice to process the same samples using FusionCatcher (Nicorici et al, 2014) and EricScript (Benelli et al, 2012). …”

Section: Basic Protocol 2: Scaling Analysis On the Cgc Platform With mentioning

confidence: 99%

Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data

Malhotra

Seth

Lehnert

et al. 2017

CP in Bioinformatics

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Next-generation sequencing has produced petabytes of data, but accessing and analyzing these data remain challenging. Traditionally, researchers investigating public datasets like The Cancer Genome Atlas (TCGA) would download the data to a high-performance cluster, which could take several weeks even with a highly optimized network connection. The National Cancer Institute (NCI) initiated the Cancer Genomics Cloud Pilots program to allow researchers without these resources to process data with cloud computational resources. We present protocols using one of these Cloud Pilots, the Seven Bridges Cancer Genomics Cloud (CGC), to find and query public datasets, bring your own data to the CGC, analyze data using standard or custom workflows, and benchmark tools for accuracy with interactive analysis features. These protocols demonstrate that the CGC is a data analysis ecosystem that fully empowers researchers with a variety of areas of expertise and interests to collaborate in the analysis of petabytes of data.

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“…Several computational tools have also been developed for the detection of fusion transcripts using RNA-Seq data, such as MapSplice [51], ShortFuse [52], FusionHunter [44], FusionMap [53], SnowShoes-FTD [54],defuse [55], chimerascan [56], FusionCatcher [57], TopHatFusion [44], BreakFusion [58], EricScript [59], SOAPfuse [60], FusionQ [61] , PRADA [62] and JAFFA [63]. Liu et al [64] performed a large-scale comparative study by applying these above 15 fusion transcript detection pipelines to 3 synthetic data sets and 3 real pairedend RNA-seq studies and developed a meta-caller algorithm to combine three top-performing methods (FusionCatcher, SOAPfusea and JAFFA).…”

Section: Next Generation Sequencing (Ngs): a High-performing Strategymentioning

confidence: 99%

Fusion Genes and their Detection through Next Generation Sequencing in Malignant Hematological Disease and Solid Tumors

Liu¹,

Weng²,

Ming³

et al. 2016

Diagn Pathol Open

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Fusion genes are neoplasia-associated mutations, which play a particularly significant role in tumorgenesis and exhibit great importance for clinical applications in malignant hematological diseases and solid tumors. Simultaneously with copy number variants (CNVs), gene fusions are resulting from balanced and unbalanced chromosomal rearrangements. Thus, understanding the mutagenesis and instability of CNV, as well as the underlying molecular mechanisms of chromosomal rearrangements will improve our comprehension of gene fusions. Recently, next generation sequencing (NGS), especially transcriptome sequencing or RNA-Sequencing (RNA-seq), has become a very useful tool to identify gene alterations in cancer and a powerful approach for investigating the tumorgenesis. However, we are still facing with the challenge of minimizing false positives in results of RNA-seq. Whole-genome sequencing (WGS) is also used for the fusion gene detection, which provides us a more comprehensive and integrative way to detect structural variants. WGS may correct the false-negative results from RNA-seq. Additionally; many computational tools with more sensitivity and specificity have been developed for the detection of fusion transcripts from NGS datas. In the future, multi-omics analysis, third-generation sequencing and liquid-biopsy technique all provide opportunities to comprehensively interpret gene fusions and understand the biology of cancer genomes.

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Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript

Cited by 164 publications

References 28 publications

Fusion transcriptome profiling provides insights into alveolar rhabdomyosarcoma

Fusion transcriptome profiling provides insights into alveolar rhabdomyosarcoma

Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data

Fusion Genes and their Detection through Next Generation Sequencing in Malignant Hematological Disease and Solid Tumors

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