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Comparative analysis of circular RNA enrichment methods.

ABSTRACT: The circRNAs sequencing results vary due to the different enrichment methods and their performance is needed to systematic comparison. This study investigated the effects of different circRNA enrichment methods on sequencing results, including abundance and species of circRNAs, as well as the sensitivity and precision. This experiment was carried out by following four common circRNA enrichment methods: including ribosomal RNA depletion (rRNA^-), polyadenylation and poly (A⁺) RNA depletion followed by RNase R treatment (polyA+RNase R), rRNA^-+polyA+RNase R and polyA+RNase R+ rRNA^-. The results showed that polyA+RNase R+ rRNA ^- enrichment method obtained more circRNA number, higher sensitivity and abundance among them; polyA+RNase R method obtained higher precision. The linear RNAs can be thoroughly removed in all enrichment methods except rRNA depletion method. Overall, our results helps researchers to quickly selection a circRNA enrichment of suitable for own study among many enrichment methods, and it provides a benchmark framework for future improvements circRNA enrichment methods.[Figure: see text].

SUBMITTER: Shi H

PROVIDER: S-EPMC8786342 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Comparative analysis of circular RNA enrichment methods.

Shi Huajuan H Zhou Ying Y Jia Erteng E Liu Zhiyu Z Pan Min M Bai Yunfei Y Zhao Xiangwei X Ge Qinyu Q

RNA biology 20211231 1

The circRNAs sequencing results vary due to the different enrichment methods and their performance is needed to systematic comparison. This study investigated the effects of different circRNA enrichment methods on sequencing results, including abundance and species of circRNAs, as well as the sensitivity and precision. This experiment was carried out by following four common circRNA enrichment methods: including ribosomal RNA depletion (rRNA-), polyadenylation and poly (A+ ...[more]

PMID: 34895057

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Project description:BackgroundPathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples.ResultsThe findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment.ConclusionThe analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

Project description:Recent studies have identified circular RNAs (circRNAs) expressed from the Epstein-Barr virus (EBV) and Kaposi's sarcoma herpesvirus (KSHV) human DNA tumor viruses. To gain initial insights into the potential relevance of EBV circRNAs in virus biology and disease, we assessed the circRNAome of the interspecies homologue rhesus macaque lymphocryptovirus (rLCV) in a naturally occurring lymphoma from a simian immunodeficiency virus (SIV)-infected rhesus macaque. This analysis revealed rLCV orthologues of the latency-associated EBV circular RNAs circRPMS1_E4_E3a and circEBNA_U. Also identified in two samples displaying unusually high lytic gene expression was a novel rLCV circRNA that contains both conserved and rLCV-specific RPMS1 exons and whose backsplice junctions flank an rLCV lytic origin of replication (OriLyt). Analysis of a lytic infection model for the murid herpesvirus 68 (MHV68) rhadinovirus identified a cluster of circRNAs near an MHV68 lytic origin of replication, with the most abundant of these, circM11_ORF69, spanning the OriLyt. Lastly, analysis of KSHV latency and reactivation models revealed the latency associated circRNA originating from the vIRF4 gene as the predominant viral circRNA. Together, the results of this study broaden our appreciation for circRNA repertoires in the Lymphocryptovirus and Rhadinovirus genera of gammaherpesviruses and provide evolutionary support for viral circRNA functions in latency and viral replication.IMPORTANCE Infection with oncogenic gammaherpesviruses leads to long-term viral persistence through a dynamic interplay between the virus and the host immune system. Critical for remodeling of the host cell environment after the immune responses are viral noncoding RNAs that modulate host signaling pathways without attracting adaptive immune recognition. Despite the importance of noncoding RNAs in persistent infection, the circRNA class of noncoding RNAs has only recently been identified in gammaherpesviruses. Accordingly, their roles in virus infection and associated oncogenesis are unknown. Here we report evolutionary conservation of EBV-encoded circRNAs determined by assessing the circRNAome in rLCV-infected lymphomas from an SIV-infected rhesus macaque, and we report latent and lytic circRNAs from KSHV and MHV68. These experiments demonstrate utilization of the circular RNA class of RNAs across 4 members of the gammaherpesvirus subfamily, and they identify orthologues and potential homoplastic circRNAs, implying conserved circRNA functions in virus biology and associated malignancies.

Project description:BackgroundThe analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited.ResultsThe simulation study highlights that none of the three method outperforms all others consistently. GSEA and RS are able to detect weak signals of deregulation and they perform differently when genes in a gene set are both differentially up and down regulated. GLAPA is more conservative and large differences between the two phenotypes are required to allow the method to detect differential deregulation in gene sets. This is due to the fact that the enrichment statistic in GLAPA is prediction error which is a stronger criteria than classical two sample statistic as used in RS and GSEA. This was reflected in the analysis on real data sets as GSEA and RS were seen to be significant for particular gene sets while GLAPA was not, suggesting a small effect size. We find that the rank of gene set enrichment induced by GLAPA is more similar to RS than GSEA. More importantly, the rankings of the three methods share significant overlap.ConclusionThe three methods considered in our study recover relevant gene sets known to be deregulated in the experimental conditions and pathologies analyzed. There are differences between the three methods and GSEA seems to be more consistent in finding enriched gene sets, although no method uniformly dominates over all data sets. Our analysis highlights the deep difference existing between associative and predictive methods for detecting enrichment and the use of both to better interpret results of pathway analysis. We close with suggestions for users of gene set methods.

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Comparative analysis of circular RNA enrichment methods.

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Comparative analysis of circular RNA enrichment methods.

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