PRADA: pipeline for RNA sequencing data analysis

Torres-García, Wandaliz; Zheng, Siyuan; Sivachenko, Andrey; Vegesna, Rahulsimham; Wang, Qianghu; Ye, Rong; Berger, Michael F.; Weinstein, John N.; Getz, Gad; Verhaak, Roel G.W.

doi:10.1093/bioinformatics/btu169

Cited by 152 publications

(146 citation statements)

References 12 publications

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“…We applied the Pipeline for RNA sequencing Data Analysis (PRADA)25 to data from the above RNA sequencing experiments and obtained a list of fusion transcripts and gene expression data for each sample.…”

Section: Methodsmentioning

confidence: 99%

Novel MXD4–NUTM1 fusion transcript identified in primary ovarian undifferentiated small round cell sarcoma

Tamura

Nakaoka

Yoshihara

et al. 2018

Genes Chromosomes & Cancer

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Primary ovarian sarcomas are extremely rare tumors, and their genomic and transcriptomic alterations remain to be elucidated. We performed whole exome sequencing of primary tumor and matched normal blood samples derived from one patient with ovarian undifferentiated small round cell sarcoma. We identified 8 nonsynonymous somatic mutations, and all mutations were missense or nonsense changes. Next, we performed RNA sequencing of the tumor sample and identified two in‐frame fusion transcripts: MXD4–NUTM1 and ARL6–POT1. Most NUTM1 exons were retained in the MXD4–NUTM1 fusion transcript, and we confirmed an increase in NUTM1 mRNA and protein expression in tumor tissue. Further genomic and transcriptomic analyses might lead to the development of new therapeutic strategies based on the molecular characteristics of ovarian undifferentiated small round cell sarcoma.

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Section: Methodsmentioning

confidence: 99%

Novel MXD4–NUTM1 fusion transcript identified in primary ovarian undifferentiated small round cell sarcoma

Tamura

Nakaoka

Yoshihara

et al. 2018

Genes Chromosomes & Cancer

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“…Currently, many RNA-seq gene fusion discovery algorithms utilize spanning reads (one read partially aligns to both genes corresponding to the fusion junction) or encompassing reads (each read of a pair aligns to a different gene, thereby surrounding the fusion junction) such as TopHat-Fusion (Kim and Salzberg 2011), deFuse (McPherson et al 2011a), ChimeraScan , BreakFusion , FusionCatcher (Nicorici et al 2014), pyPRADA (Torres-Garcia et al 2014), and TRUP (Fernandez-Cuesta et al 2015). However, despite the successful application of these algorithms to discover gene fusions, a recent comparison of eight gene fusion discovery tools revealed a lot of variability between callers.…”

Section: [Supplemental Materials Is Available For This Article]mentioning

confidence: 99%

“…To compare the performance of INTEGRATE to other available algorithms, we reanalyzed the HCC1395 data with three WGS and RNA-seq callers (Comrad [McPherson et al 2011b], nFuse [McPherson et al 2012], and BreakTrans [Chen et al 2013]) and five commonly used and recently published RNA-seq gene fusion tools (TopHat-Fusion [Kim and Salzberg 2011], ChimeraScan , FusionCatcher [Nicorici et al 2014], pyPRADA [Torres-Garcia et al 2014], and TRUP [Fernandez-Cuesta et al 2015]). BreakTrans was provided with fusion and SV candidates called by BreakDancer ).…”

Section: Application To Hcc1395 Breast Cancer Cellsmentioning

confidence: 99%

INTEGRATE: gene fusion discovery using whole genome and transcriptome data

et al. 2015

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While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping.

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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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PRADA: pipeline for RNA sequencing data analysis

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 152 publications

References 12 publications

Novel MXD4–NUTM1 fusion transcript identified in primary ovarian undifferentiated small round cell sarcoma

Novel MXD4–NUTM1 fusion transcript identified in primary ovarian undifferentiated small round cell sarcoma

INTEGRATE: gene fusion discovery using whole genome and transcriptome data

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

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