Dataset Information

GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data.

ABSTRACT: Fusion gene derived from genomic rearrangement plays a key role in cancer initiation. The discovery of novel gene fusions may be of significant importance in cancer diagnosis and treatment. Meanwhile, next generation sequencing technology provide a sensitive and efficient way to identify gene fusions in genomic levels. However, there are still many challenges and limitations remaining in the existing methods which only rely on unmapped reads or discordant alignment fragments. In this work we have developed GFusion, a novel method using RNA-Seq data, to identify the fusion genes. This pipeline performs multiple alignments and strict filtering algorithm to improve sensitivity and reduce the false positive rate. GFusion successfully detected 34 from 43 previously reported fusions in four cancer datasets. We also demonstrated the effectiveness of GFusion using 24 million 76?bp paired-end reads simulation data which contains 42 artificial fusion genes, among which GFusion successfully discovered 37 fusion genes. Compared with existing methods, GFusion presented higher sensitivity and lower false positive rate. The GFusion pipeline can be accessed freely for non-commercial purposes at: https://github.com/xiaofengsong/GFusion .

SUBMITTER: Zhao J

PROVIDER: S-EPMC5537242 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data.

Zhao Jian J Chen Qi Q Wu Jing J Han Ping P Song Xiaofeng X

Scientific reports 20170731 1

Fusion gene derived from genomic rearrangement plays a key role in cancer initiation. The discovery of novel gene fusions may be of significant importance in cancer diagnosis and treatment. Meanwhile, next generation sequencing technology provide a sensitive and efficient way to identify gene fusions in genomic levels. However, there are still many challenges and limitations remaining in the existing methods which only rely on unmapped reads or discordant alignment fragments. In this work we hav ...[more]

PMID: 28761119

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Project description:Latest 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.In 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.FuGePrior 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.

Project description:Chimeric transcripts are commonly defined as transcripts linking two or more different genes in the genome, and can be explained by various biological mechanisms such as genomic rearrangement, read-through or trans-splicing, but also by technical or biological artefacts. Several studies have shown their importance in cancer, cell pluripotency and motility. Many programs have recently been developed to identify chimeras from Illumina RNA-seq data (mostly fusion genes in cancer). However outputs of different programs on the same dataset can be widely inconsistent, and tend to include many false positives. Other issues relate to simulated datasets restricted to fusion genes, real datasets with limited numbers of validated cases, result inconsistencies between simulated and real datasets, and gene rather than junction level assessment.Here we present ChimPipe, a modular and easy-to-use method to reliably identify fusion genes and transcription-induced chimeras from paired-end Illumina RNA-seq data. We have also produced realistic simulated datasets for three different read lengths, and enhanced two gold-standard cancer datasets by associating exact junction points to validated gene fusions. Benchmarking ChimPipe together with four other state-of-the-art tools on this data showed ChimPipe to be the top program at identifying exact junction coordinates for both kinds of datasets, and the one showing the best trade-off between sensitivity and precision. Applied to 106 ENCODE human RNA-seq datasets, ChimPipe identified 137 high confidence chimeras connecting the protein coding sequence of their parent genes. In subsequent experiments, three out of four predicted chimeras, two of which recurrently expressed in a large majority of the samples, could be validated. Cloning and sequencing of the three cases revealed several new chimeric transcript structures, 3 of which with the potential to encode a chimeric protein for which we hypothesized a new role. Applying ChimPipe to human and mouse ENCODE RNA-seq data led to the identification of 131 recurrent chimeras common to both species, and therefore potentially conserved.ChimPipe combines discordant paired-end reads and split-reads to detect any kind of chimeras, including those originating from polymerase read-through, and shows an excellent trade-off between sensitivity and precision. The chimeras found by ChimPipe can be validated in-vitro with high accuracy.

Dataset Information

GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data.

Publications

GFusion: an Effective Algorithm to Identify Fusion Genes from Cancer RNA-Seq Data.

Similar Datasets

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