Dataset Information

CirComPara: A Multi-Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA-seq Data.

ABSTRACT: Circular RNAs (circRNAs) are generated by backsplicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and posttranscriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily detectable disease markers, circRNAs undoubtedly represent a molecular class with high bearing on molecular pathobiology. CircRNAs can be detected from RNAseq data using appropriate computational methods to identify the sequence reads spanning backsplice junctions that do not colinearly map to the reference genome. To this end, several programs were developed and critical assessment of various strategies and tools suggested the combination of at least two methods as good practice to guarantee robust circRNA detection. Here,we present CirComPara (http://github.com/egaffo/CirComPara), an automated bioinformatics pipeline, to detect, quantify and annotate circRNAs from RNAseq data using in parallel four different methods for backsplice identification. CirComPara also provides quantification of linear RNAs and gene expression, ultimately comparing and correlating circRNA and gene/transcript expression level. We applied our method to RNAseqdata of monocyte and macrophage samples in relation to haploinsufficiency of the RNAbinding splicing factor Quaking (QKI). The biological relevance of the results, in terms of number, types and variations of circRNAs expressed, illustrates CirComPara potential to enlarge the knowledge of the transcriptome, adding details on the circRNAome, and facilitating further computational and experimental studies.

SUBMITTER: Gaffo E

PROVIDER: S-EPMC5832002 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CirComPara: A Multi-Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA-seq Data.

Gaffo Enrico E Bonizzato Annagiulia A Kronnie Geertruy Te GT Bortoluzzi Stefania S

Non-coding RNA 20170210 1

Circular RNAs (circRNAs) are generated by backsplicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and posttranscriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily detectable disease markers, circRNAs undoubtedly represent a molecular class with high bearing on molecular pathobiology. CircRNAs can be detected fro ...[more]

PMID: 29657280

Similar Datasets

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.

Project description:BackgroundIdentifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work.The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data.ResultsIn this study, we transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a scaling-free minimum enclosing ball (SFMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed SFMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the SFMEB method could be easily extended to different species without normalization.ConclusionsSimulation studies demonstrate that the SFMEB method works well in a wide range of settings, especially when the data are heterogeneous or biological replicates. Analysis of the real data also supports the conclusion that the SFMEB method outperforms other existing competitors. The R package of the proposed method is available at https://bioconductor.org/packages/MEB .

Project description:Most existing tools for detecting next-generation sequencing-based splicing events focus on generic splicing events. Consequently, special types of non-canonical splicing events of short mRNA regions (IRE1? targeted) have not yet been thoroughly addressed at a genome-wide level using bioinformatics approaches in conjunction with next-generation technologies. During endoplasmic reticulum (ER) stress, the gene encoding the RNase Ire1? is known to splice out a short 26 nt region from the mRNA of the transcription factor Xbp1 non-canonically within the cytosol. This causes an open reading frame-shift that induces expression of many downstream genes in reaction to ER stress as part of the unfolded protein response (UPR). We previously published an algorithm termed "Read-Split-Walk" (RSW) to identify non-canonical splicing regions using RNA-Seq data and applied it to ER stress-induced Ire1? heterozygote and knockout mouse embryonic fibroblast cell lines. In this study, we have developed an improved algorithm "Read-Split-Run" (RSR) for detecting genome-wide Ire1?-targeted genes with non-canonical spliced regions at a faster speed. We applied the RSR algorithm using different combinations of several parameters to the previously RSW tested mouse embryonic fibroblast cells (MEF) and the human Encyclopedia of DNA Elements (ENCODE) RNA-Seq data. We also compared the performance of RSR with two other alternative splicing events identification tools (TopHat (Trapnell et al., Bioinformatics 25:1105-1111, 2009) and Alt Event Finder (Zhou et al., BMC Genomics 13:S10, 2012)) utilizing the context of the spliced Xbp1 mRNA as a positive control in the data sets we identified it to be the top cleavage target present in Ire1? (+/-) but absent in Ire1? (-/-) MEF samples and this comparison was also extended to human ENCODE RNA-Seq data.Proof of principle came in our results by the fact that the 26 nt non-conventional splice site in Xbp1 was detected as the top hit by our new RSR algorithm in heterozygote (Het) samples from both Thapsigargin (Tg) and Dithiothreitol (Dtt) treated experiments but absent in the negative control Ire1? knock-out (KO) samples. Applying different combinations of parameters to the mouse MEF RNA-Seq data, we suggest a General Linear Model (GLM) for both Tg and Dtt treated experiments. We also ran RSR for a human ENCODE RNA-Seq dataset and identified 32,597 spliced regions for regular chromosomes. TopHat (Trapnell et al., Bioinformatics 25:1105-1111, 2009) and Alt Event Finder (Zhou et al., BMC Genomics 13:S10, 2012) identified 237,155 spliced junctions and 9,129 exon skipping events (excluding chr14), respectively. Our Read-Split-Run algorithm also outperformed others in the context of ranking Xbp1 gene as the top cleavage target present in Ire1? (+/-) but absent in Ire1? (-/-) MEF samples. The RSR package including source codes is available at http://bioinf1.indstate.edu/RSR and its pipeline source codes are also freely available at https://github.com/xuric/read-split-run for academic use.Our new RSR algorithm has the capability of processing massive amounts of human ENCODE RNA-Seq data for identifying novel splice junction sites at a genome-wide level in a much more efficient manner when compared to the previous RSW algorithm. Our proposed model can also predict the number of spliced regions under any combinations of parameters. Our pipeline can detect novel spliced sites for other species using RNA-Seq data generated under similar conditions.

Dataset Information

CirComPara: A Multi-Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA-seq Data.

Publications

CirComPara: A Multi-Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA-seq Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets