Dataset Information

FusionFinder: a software tool to identify expressed gene fusion candidates from RNA-Seq data.

ABSTRACT: The hallmarks of many haematological malignancies and solid tumours are chromosomal translocations, which may lead to gene fusions. Recently, next-generation sequencing techniques at the transcriptome level (RNA-Seq) have been used to verify known and discover novel transcribed gene fusions. We present FusionFinder, a Perl-based software designed to automate the discovery of candidate gene fusion partners from single-end (SE) or paired-end (PE) RNA-Seq read data. FusionFinder was applied to data from a previously published analysis of the K562 chronic myeloid leukaemia (CML) cell line. Using FusionFinder we successfully replicated the findings of this study and detected additional previously unreported fusion genes in their dataset, which were confirmed experimentally. These included two isoforms of a fusion involving the genes BRK1 and VHL, whose co-deletion has previously been associated with the prevalence and severity of renal-cell carcinoma. FusionFinder is made freely available for non-commercial use and can be downloaded from the project website (http://bioinformatics.childhealthresearch.org.au/software/fusionfinder/).

SUBMITTER: Francis RW

PROVIDER: S-EPMC3384600 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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FusionFinder: a software tool to identify expressed gene fusion candidates from RNA-Seq data.

Francis Richard W RW Thompson-Wicking Katherine K Carter Kim W KW Anderson Denise D Kees Ursula R UR Beesley Alex H AH

PloS one 20120627 6

The hallmarks of many haematological malignancies and solid tumours are chromosomal translocations, which may lead to gene fusions. Recently, next-generation sequencing techniques at the transcriptome level (RNA-Seq) have been used to verify known and discover novel transcribed gene fusions. We present FusionFinder, a Perl-based software designed to automate the discovery of candidate gene fusion partners from single-end (SE) or paired-end (PE) RNA-Seq read data. FusionFinder was applied to data ...[more]

PMID: 22761941

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Project description:BackgroundRNA sequencing (RNA-Seq) measures genome-wide gene expression. RNA-Seq data is count-based rendering normal distribution models for analysis inappropriate. Normalization of RNA-Seq data to transform the data has limitations which can adversely impact the analysis. Furthermore, there are a few count-based methods for analysis of RNA-Seq data but they are essentially for pairwise analysis of treatment groups or multiclasses but not pattern-based to identify co-expressed genes.ResultsWe adapted our extracting patterns and identifying genes methodology for RNA-Seq (EPIG-Seq) count data. The software uses count-based correlation to measure similarity between genes, quasi-Poisson modelling to estimate dispersion in the data and a location parameter to indicate magnitude of differential expression. EPIG-Seq is different than any other software currently available for pattern analysis of RNA-Seq data in that EPIG-Seq 1) uses count level data and supports cases of inflated zeros, 2) identifies statistically significant clusters of genes that are co-expressed across experimental conditions, 3) takes into account dispersion in the replicate data and 4) provides reliable results even with small sample sizes. EPIG-Seq operates in two steps: 1) extract the pattern profiles from data as seeds for clustering co-expressed genes and 2) cluster the genes to the pattern seeds and compute statistical significance of the pattern of co-expressed genes. EPIG-Seq provides a table of the genes with bootstrapped p-values and profile plots of the patterns of co-expressed genes. In addition, EPIG-Seq provides a heat map and principal component dimension reduction plot of the clustered genes as visual aids. We demonstrate the utility of EPIG-Seq through the analysis of toxicogenomics and cancer data sets to identify biologically relevant co-expressed genes. EPIG-Seq is available at: sourceforge.net/projects/epig-seq.ConclusionsEPIG-Seq is unlike any other software currently available for pattern analysis of RNA-Seq count level data across experimental groups. Using the EPIG-Seq software to analyze RNA-Seq count data across biological conditions permits the ability to extract biologically meaningful co-expressed genes associated with coordinated regulation.

Dataset Information

FusionFinder: a software tool to identify expressed gene fusion candidates from RNA-Seq data.

Publications

FusionFinder: a software tool to identify expressed gene fusion candidates from RNA-Seq data.

Similar Datasets

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