Dataset Information

ASGAL: aligning RNA-Seq data to a splicing graph to detect novel alternative splicing events.

ABSTRACT: BACKGROUND:While the reconstruction of transcripts from a sample of RNA-Seq data is a computationally expensive and complicated task, the detection of splicing events from RNA-Seq data and a gene annotation is computationally feasible. This latter task, which is adequate for many transcriptome analyses, is usually achieved by aligning the reads to a reference genome, followed by comparing the alignments with a gene annotation, often implicitly represented by a graph: the splicing graph. RESULTS:We present ASGAL (Alternative Splicing Graph ALigner): a tool for mapping RNA-Seq data to the splicing graph, with the specific goal of detecting novel splicing events, involving either annotated or unannotated splice sites. ASGAL takes as input the annotated transcripts of a gene and a RNA-Seq sample, and computes (1) the spliced alignments of each read in input, and (2) a list of novel events with respect to the gene annotation. CONCLUSIONS:An experimental analysis shows that ASGAL allows to enrich the annotation with novel alternative splicing events even when genes in an experiment express at most one isoform. Compared with other tools which use the spliced alignment of reads against a reference genome for differential analysis, ASGAL better predicts events that use splice sites which are novel with respect to a splicing graph, showing a higher accuracy. To the best of our knowledge, ASGAL is the first tool that detects novel alternative splicing events by directly aligning reads to a splicing graph. AVAILABILITY:Source code, documentation, and data are available for download at http://asgal.algolab.eu .

SUBMITTER: Denti L

PROVIDER: S-EPMC6247705 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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ASGAL: aligning RNA-Seq data to a splicing graph to detect novel alternative splicing events.

Denti Luca L Rizzi Raffaella R Beretta Stefano S Vedova Gianluca Della GD Previtali Marco M Bonizzoni Paola P

BMC bioinformatics 20181120 1

<h4>Background</h4>While the reconstruction of transcripts from a sample of RNA-Seq data is a computationally expensive and complicated task, the detection of splicing events from RNA-Seq data and a gene annotation is computationally feasible. This latter task, which is adequate for many transcriptome analyses, is usually achieved by aligning the reads to a reference genome, followed by comparing the alignments with a gene annotation, often implicitly represented by a graph: the splicing graph.< ...[more]

PMID: 30458725

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Project description:MotivationIn eukaryotic cells, alternative splicing expands the diversity of RNA transcripts and plays an important role in tissue-specific differentiation, and can be misregulated in disease. To understand these processes, there is a great need for methods to detect differential transcription between samples. Our focus is on samples observed using short-read RNA sequencing (RNA-seq).MethodsWe characterize differential transcription between two samples as the difference in the relative abundance of the transcript isoforms present in the samples. The magnitude of differential transcription of a gene between two samples can be measured by the square root of the Jensen Shannon Divergence (JSD*) between the gene's transcript abundance vectors in each sample. We define a weighted splice-graph representation of RNA-seq data, summarizing in compact form the alignment of RNA-seq reads to a reference genome. The flow difference metric (FDM) identifies regions of differential RNA transcript expression between pairs of splice graphs, without need for an underlying gene model or catalog of transcripts. We present a novel non-parametric statistical test between splice graphs to assess the significance of differential transcription, and extend it to group-wise comparison incorporating sample replicates.ResultsUsing simulated RNA-seq data consisting of four technical replicates of two samples with varying transcription between genes, we show that (i) the FDM is highly correlated with JSD* (r=0.82) when average RNA-seq coverage of the transcripts is sufficiently deep; and (ii) the FDM is able to identify 90% of genes with differential transcription when JSD* >0.28 and coverage >7. This represents higher sensitivity than Cufflinks (without annotations) and rDiff (MMD), which respectively identified 69 and 49% of the genes in this region as differential transcribed. Using annotations identifying the transcripts, Cufflinks was able to identify 86% of the genes in this region as differentially transcribed. Using experimental data consisting of four replicates each for two cancer cell lines (MCF7 and SUM102), FDM identified 1425 genes as significantly different in transcription. Subsequent study of the samples using quantitative real time polymerase chain reaction (qRT-PCR) of several differential transcription sites identified by FDM, confirmed significant differences at these sites.Availabilityhttp://csbio-linux001.cs.unc.edu/nextgen/software/FDM CONTACT: darshan@email.unc.eduSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

ASGAL: aligning RNA-Seq data to a splicing graph to detect novel alternative splicing events.

Publications

ASGAL: aligning RNA-Seq data to a splicing graph to detect novel alternative splicing events.

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