Evidence map›Paper›PMID 28114882›Full record

ArticleBMC bioinformatics2017

FuGePrior: A novel gene fusion prioritization algorithm based on accurate fusion structure analysis in cancer RNA-seq samples.

Giulia Paciello, Elisa Ficarra

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Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Characteristics of fusion genes in breast cancer.Cancer cell international · 2026
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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Giulia PacielloDepartment of Control and Computer Engineering DAUIN, Politecnico di Torino, C.so Duca degli Abruzzi 24, Turin, 10129, Italy. giulia.paciello@polito.it.
Elisa FicarraDepartment of Control and Computer Engineering DAUIN, Politecnico di Torino, C.so Duca degli Abruzzi 24, Turin, 10129, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLatest Next Generation Sequencing technologies opened the way to a novel era of genomic studies, allowing to gain novel insights into multifactorial pathologies as cancer. In particular gene fusion detection and comprehension have been deeply enhanced by these methods. However, state of the art algorithms for gene fusion identification are still challenging. Indeed, they identify huge amounts of poorly overlapping candidates and all the reported fusions should be considered for in lab validation clearly overwhelming wet lab capabilities.

resultsIn this work we propose a novel methodological approach and tool named FuGePrior for the prioritization of gene fusions from paired-end RNA-Seq data. The proposed pipeline combines state of the art tools for chimeric transcript discovery and prioritization, a series of filtering and processing steps designed by considering modern literature on gene fusions and an analysis on functional reliability of gene fusion structure.

conclusionsFuGePrior performance has been assessed on two publicly available paired-end RNA-Seq datasets: The first by Edgren and colleagues includes four breast cancer cell lines and a normal breast sample, whereas the second by Ren and colleagues comprises fourteen primary prostate cancer samples and their paired normal counterparts. FuGePrior results accounted for a reduction in the number of fusions output of chimeric transcript discovery tools that ranges from 65 to 75% depending on the considered breast cancer cell line and from 37 to 65% according to the prostate cancer sample under examination. Furthermore, since both datasets come with a partial validation we were able to assess the performance of FuGePrior in correctly prioritizing real gene fusions. Specifically, 25 out of 26 validated fusions in breast cancer dataset have been correctly labelled as reliable and biologically significant. Similarly, 2 out of 5 validated fusions in prostate dataset have been recognized as priority by FuGePrior tool.

Indexed as

Sequence Analysis, RNAAlgorithmsBreast NeoplasmsCell Line, TumorDatabases, GeneticFemaleGenomicsHumansMaleMCF-7 CellsProstatic NeoplasmsRecombinant Fusion ProteinsReproducibility of ResultsSoftwareRecombinant Fusion ProteinsChimeric transcript discovery toolsGene fusion prioritizationGene fusionsRNA-sequencing

Identifiers

PMID28114882
PMCPMC5260008

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.