FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data

Sboner, Andrea; Habegger, Lukas; Pflueger, Dorothee; Terry, Stéphane; Chen, David Z.; Rozowsky, Joel; Tewari, Ashutosh; Kitabayashi, Naoki; Moss, Benjamin; Chee, Mark S.; Demichelis, Francesca; Rubin, Mark A.; Gerstein, Mark

doi:10.1186/gb-2010-11-10-r104

Cited by 142 publications

(150 citation statements)

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“…Different computational methods and software for the detection of fusion transcripts in tumors have been developed. [68][69] To this purpose, a novel computational method, deFuse, has allowed to discover for the first time gene fusions in ovarian cancer specimen, also showing novel chimeric mRNAs in sarcoma. 66 Novel fusion transcripts have been also discovered, especially in breast cancer (Table 1).…”

Section: Rna-seq In Cancermentioning

confidence: 99%

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

et al. 2012

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Section: Rna-seq In Cancermentioning

confidence: 99%

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

et al. 2012

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“…New algorithms have been developed to map splice-crossing reads, some of which utilize previously known splice events (e.g., ERANGE [129]), while others (e.g., GSNAP [130], MapSplice [131], RUM [132], SpliceMap [133], TopHat [134]) do not rely upon prior knowledge. In particular, some algorithms have been specifically designed for the identification of gene fusions, including deFuse [157], FusionSeq [158], ShortFuse [159] and TopHat-Fusion [160]. Despite high sensitivity of these aligners in detecting junctions, misalignment of multi-reads can readily occur and lead to a high false positive rate of identification.…”

Section: Bioinformatics Challenges and Solutionsmentioning

confidence: 99%

Next-generation sequencing in the clinic: Promises and challenges

et al. 2013

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The advent of next generation sequencing (NGS) technologies has revolutionized the field of genomics, enabling fast and cost-effective generation of genome-scale sequence data with exquisite resolution and accuracy. Over the past years, rapid technological advances led by academic institutions and companies have continued to broaden NGS applications from research to the clinic. A recent crop of discoveries have highlighted the medical impact of NGS technologies on Mendelian and complex diseases, particularly cancer. However, the everincreasing pace of NGS adoption presents enormous challenges in terms of data processing, storage, management and interpretation as well as sequencing quality control, which hinder the translation from sequence data into clinical practice. In this review, we first summarize the technical characteristics and performance of current NGS platforms. We further highlight advances in the applications of NGS technologies towards the development of clinical diagnostics and therapeutics. Common issues in NGS workflows are also discussed to guide the selection of NGS platforms and pipelines for specific research purposes.

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“…We used a novel computational approach, FusionSeq (Sboner et al 2010b; also described in the Supplemental material), to identify chimeric transcripts based on PE reads where the two ends are mapped to different genes ( Fig. 1A; Supplemental Fig.…”

Section: Computational Approachmentioning

confidence: 99%

“…About 600 million mapped reads derived from the paired-end (PE) RNA-seq data were processed through a novel computational tool called FusionSeq (an openly available resource at http://rnaseq. gersteinlab.org/fusionseq/; Sboner et al 2010b) developed to specifically nominate high-confident chimeric RNA transcripts by accounting for sources of noise that can introduce artifacts ( Fig. 1A; Supplemental Fig.…”

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confidence: 99%

Discovery of non-ETS gene fusions in human prostate cancer using next-generation RNA sequencing

Pflueger

Terry²,

Sboner³

et al. 2010

Genome Res.

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Half of prostate cancers harbor gene fusions between TMPRSS2 and members of the ETS transcription factor family. To date, little is known about the presence of non-ETS fusion events in prostate cancer. We used next-generation transcriptome sequencing (RNA-seq) in order to explore the whole transcriptome of 25 human prostate cancer samples for the presence of chimeric fusion transcripts. We generated more than 1 billion sequence reads and used a novel computational approach (FusionSeq) in order to identify novel gene fusion candidates with high confidence. In total, we discovered and characterized seven new cancer-specific gene fusions, two involving the ETS genes ETV1 and ERG, and four involving non-ETS genes such as CDKN1A (p21), CD9, and IKBKB (IKK-beta), genes known to exhibit key biological roles in cellular homeostasis or assumed to be critical in tumorigenesis of other tumor entities, as well as the oncogene PIGU and the tumor suppressor gene RSRC2. The novel gene fusions are found to be of low frequency, but, interestingly, the non-ETS fusions were all present in prostate cancer harboring the TMPRSS2-ERG gene fusion. Future work will focus on determining if the ETS rearrangements in prostate cancer are associated or directly predispose to a rearrangement-prone phenotype.

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FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data

Cited by 142 publications

References 54 publications

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

Next-generation sequencing in the clinic: Promises and challenges

Discovery of non-ETS gene fusions in human prostate cancer using next-generation RNA sequencing

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