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A combined computational pipeline to detect circular RNAs in human cancer cells under hypoxic stress.

ABSTRACT: Hypoxia is associated with several diseases, including cancer. Cells that are deprived of adequate oxygen supply trigger transcriptional and post-transcriptional responses, which control cellular pathways such as angiogenesis, proliferation, and metabolic adaptation. Circular RNAs (circRNAs) are a novel class of mainly non-coding RNAs, which have been implicated in multiple cancers and attract increasing attention as potential biomarkers. Here, we characterize the circRNA signatures of three different cancer cell lines from cervical (HeLa), breast (MCF-7), and lung (A549) cancer under hypoxia. In order to reliably detect circRNAs, we integrate available tools with custom approaches for quantification and statistical analysis. Using this consolidated computational pipeline, we identify ~12000 circRNAs in the three cancer cell lines. Their molecular characteristics point to an involvement of complementary RNA sequences as well as trans-acting factors in circRNA biogenesis, such as the RNA-binding protein HNRNPC. Notably, we detect a number of circRNAs that are more abundant than their linear counterparts. In addition, 64 circRNAs significantly change in abundance upon hypoxia, in most cases in a cell type-specific manner. In summary, we present a comparative circRNA profiling in human cancer cell lines, which promises novel insights into the biogenesis and function of circRNAs under hypoxic stress.

SUBMITTER: Di Liddo A

PROVIDER: S-EPMC6884703 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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A combined computational pipeline to detect circular RNAs in human cancer cells under hypoxic stress.

Di Liddo Antonella A de Oliveira Freitas Machado Camila C Fischer Sandra S Ebersberger Stefanie S Heumüller Andreas W AW Weigand Julia E JE Müller-McNicoll Michaela M Zarnack Kathi K

Journal of molecular cell biology 20191001 10

Hypoxia is associated with several diseases, including cancer. Cells that are deprived of adequate oxygen supply trigger transcriptional and post-transcriptional responses, which control cellular pathways such as angiogenesis, proliferation, and metabolic adaptation. Circular RNAs (circRNAs) are a novel class of mainly non-coding RNAs, which have been implicated in multiple cancers and attract increasing attention as potential biomarkers. Here, we characterize the circRNA signatures of three dif ...[more]

PMID: 31560396

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Project description:BackgroundExtrachromosomal circular DNAs (eccDNAs) are ring-like DNA structures physically separated from the chromosomes with 100 bp to several megabasepairs in size. Apart from carrying tandemly repeated DNA, eccDNAs may also harbor extra copies of genes or recently activated transposable elements. As eccDNAs occur in all eukaryotes investigated so far and likely play roles in stress, cancer, and aging, they have been prime targets in recent research-with their investigation limited by the scarcity of computational tools.ResultsHere, we present the ECCsplorer, a bioinformatics pipeline to detect eccDNAs in any kind of organism or tissue using next-generation sequencing techniques. Following Illumina-sequencing of amplified circular DNA (circSeq), the ECCsplorer enables an easy and automated discovery of eccDNA candidates. The data analysis encompasses two major procedures: first, read mapping to the reference genome allows the detection of informative read distributions including high coverage, discordant mapping, and split reads. Second, reference-free comparison of read clusters from amplified eccDNA against control sample data reveals specifically enriched DNA circles. Both software parts can be run separately or jointly, depending on the individual aim or data availability. To illustrate the wide applicability of our approach, we analyzed semi-artificial and published circSeq data from the model organisms Homo sapiens and Arabidopsis thaliana, and generated circSeq reads from the non-model crop plant Beta vulgaris. We clearly identified eccDNA candidates from all datasets, with and without reference genomes. The ECCsplorer pipeline specifically detected mitochondrial mini-circles and retrotransposon activation, showcasing the ECCsplorer's sensitivity and specificity.ConclusionThe ECCsplorer (available online at https://github.com/crimBubble/ECCsplorer ) is a bioinformatics pipeline to detect eccDNAs in any kind of organism or tissue using next-generation sequencing data. The derived eccDNA targets are valuable for a wide range of downstream investigations-from analysis of cancer-related eccDNAs over organelle genomics to identification of active transposable elements.

Project description:BackgroundCircular RNA (circRNA), a class of RNA molecule with a loop structure, has recently attracted researchers due to its diverse biological functions and potential biomarkers of human diseases. Most of the current circRNA detection methods from RNA-sequencing (RNA-Seq) data utilize the mapping information of paired-end (PE) reads to eliminate false positives. However, much of the practical RNA-Seq data such as cross-linking immunoprecipitation sequencing (CLIP-Seq) data usually contain single-end (SE) reads. It is not clear how well these tools perform on SE RNA-Seq data.ResultsIn this study, we present a systematic evaluation of six advanced RNA-based methods and two CLIP-Seq based methods for detecting circRNAs from SE RNA-Seq data. The performances of the methods are rigorously assessed based on precision, sensitivity, F1 score, and true discovery rate. We investigate the impacts of read length, false positive ratio, sequencing depth and PE mapping information on the performances of the methods using simulated SE RNA-Seq simulated datasets. The real datasets used in this study consist of four experimental RNA-Seq datasets with ≥100bp read length and 124 CLIP-Seq samples from 45 studies that contain mostly short-read (≤50bp) RNA-Seq data. The simulation study shows that the sensitivities of most of the methods can be improved by increasing either read length or sequencing depth, and that the levels of false positive rates significantly affect the precision of all methods. Furthermore, the PE mapping information can improve the method's precision but can not always guarantee the increase of F1 score. Overall, no method is dominant for all SE RNA-Seq data. The RNA-based methods perform better for the long-read datasets but are worse for the short-read datasets. In contrast, the CLIP-Seq based methods outperform the RNA-Seq based methods for all the short-read samples. Combining the results of these methods can significantly improve precision in the CLIP-Seq data.ConclusionsThe results provide a systematic evaluation of circRNA detection methods on SE RNA-Seq data that would facilitate researchers' strategies in circRNA analysis.

Project description:Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger in vivo accumulation of extensively overlapping small RNAs. Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs.

Dataset Information

A combined computational pipeline to detect circular RNAs in human cancer cells under hypoxic stress.

Publications

A combined computational pipeline to detect circular RNAs in human cancer cells under hypoxic stress.

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