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Detects head-to-tail spliced (back-spliced) sequencing reads, indicative of circular RNA (circRNA) in RNA-seq data. find_circ is a pipeline that can find circRNAs in any genomic region. It takes advantage of long (,100 nucleotides) reads, and predicts the acceptor and donor splice sites used to link the ends of the RNAs. This method provides evidence that circRNAs form an important class of post-transcriptional regulators.
A combined strategy to identify junction reads from back spliced exons and intron lariats. CIRCexplorer is now only a circular RNA annotating tool, and it parses fusion junction information from mapping results of other aligners. The result of circular RNA annotating is directly dependent on the mapping strategy of aligners. Different aligners may have different circular RNA annotations. CIRCexplorer is now only in charge of giving fusion junctions a correct gene annotation.
A single-cell RNAseq technique which can detect both polyA+ mRNAs and polyA– RNAs from a single mammalian cell. SUPeR-seq has been successfully applied for investigating polyA– RNAs including circRNAs during mouse pre-implantation development. We apply SUPeR-seq to systematically analyze the transcriptomes of individual human pre-implantation embryos and we have identified a total of 10,032 exonic circRNAs from 2974 hosting genes in human pre-implantation embryos, including a large proportion of circRNA hosting genes of mouse pre-implantation embryos.
Identifies circular RNAs (circRNAs) using multiple seed matching. CIRI is designed to differentiate back-spliced junction (BSJ) reads from non-BSJ reads using efficient maximum likelihood estimation (MLE) based on multiple seed matching. The software is optimized for the key steps in circRNA detection, including inferring original region for sequencing read segments and distinguishing BSJ reads from forward-spliced junction (FSJ) reads. The software is applicable to sequencing data with mixed read lengths, and can be run with multiple threads.
Increases the sensitivity and specificity of circular RNA detection by discovering and quantifying circular and linear RNA splicing events at both annotated and un-annotated exon boundaries, including intergenic regions of the genome, with high statistical confidence. Unlike approaches that rely on read count and exon homology to determine confidence in prediction of circular RNA expression, KNIFE uses a statistical approach. It can analyze single-end or paired-end reads stored in plain-text fastq or gzipped fastq files.
A computational pipeline to detect circular RNA from RNA-Seq data, based on junction reads from back spliced exons. By applying UROBORUS to RNA-seq data from 46 gliomas and normal brain samples, we detected thousands of circRNAs supported by at least two read counts, followed by successful experimental validation on 24 circRNAs from the randomly selected 27 circRNAs. UROBORUS is an efficient tool that can detect circRNAs with low expression levels in total RNA-seq without RNase R treatment. The current version of the UROBORUS pipeline can only detect those circRNAs supported by exon–exon junctions, and may miss those circRNAs derived from an intron region or intergenic region.
A high accurate method for detecting intragenic and intergenic non-co-linear (NCL) transcripts. NCLscan utilizes a stepwise alignment strategy to almost completely eliminate false calls (>98% precision) without sacrificing true positives. NCLscan outperforms 18 other publicly-available tools (including fusion- and circular-RNA-detecting tools) in terms of sensitivity and precision, regardless of the generation strategy of simulated dataset, type of intragenic or intergenic NCL event, read depth of coverage, read length or expression level of NCL transcript. NCLscan promises to facilitate the comprehensive characterization of various types of NCL transcripts on a transcriptome-wide scale.
Detects circRNAs from chimeric reads. DCC uses output from the STAR read mapper to systematically detect back-splice junctions in next-generation sequencing data. DCC applies a series of filters and integrates data across replicate sets to arrive at a precise list of circRNA candidates. We assessed the detection performance of DCC on a newly generated mouse brain data set and publicly available sequencing data. Our software achieves a much higher precision than state-of-the-art competitors at similar sensitivity levels. Moreover, DCC estimates circRNA vs. host gene expression from counting junction and non-junction reads.
Estimates the relative abundance of circular RNA transcripts from high-throughput RNA-seq data. Sailfish-cir transforms circular transcripts to pseudo-linear transcripts. Comparing to count-based tools, it is superior in estimating the amount of circRNA expression from both simulated and real ribosomal RNA-depleted (rRNAdepleted) RNA-seq datasets. Several factors, such as gene length, amount of expression, and the ratio of circular to linear transcripts, had impacts on quantification performance of the tool.
STARChip / STAR Chimeric Post
Offers a platform for dedicated to circRNA detection, quantification and annotation as well as to high-precision fusion forecasting. STARChip is divided into two distinct parts : (i) STARChip-Circles exploits multithreading and all available samples to discover high quality circRNA and to provide outputs suited for exporting towards external pipelines or software; and (ii) STARChip-Fusions performs its analysis with each sample run separately and is able to complete large cohorts to recognize fusion transcripts.
Identifies Circular RNAs (circRNAs) with protein-coding potential. CircPro can detect circRNAs, predict their protein-coding potential and discovering junction reads from Ribo-Seq data. It provides the sequences of circRNA and possible proteins translated from circRNAs with protein-coding potential. The tool was evaluated on real biological sequencing data. It contains three different steps: the cricRNA detection, the protein-coding potential score and the junction reads from Ribo-Seq.
Converts the raw fastq files into gene/isoform expression matrix and differentially expressed genes or isoforms. hppRNA is a one-in-all solution composed of four scenarios such as pre-mapping, core-workflow, post-mapping and sequence variation detection. It also turns the identification of fusion genes, single nucleotide polymorphisms (SNP), long noncoding RNAs and circular RNAs. Finally, this pipeline is specifically designed for performing the systematic analysis on a huge set of samples in one go, ideally for the researchers who intend to deploy the pipeline on their local servers.
Acfs / Accurate circRNA finder suite
Allows circRNA identification and quantification. Acfs can handle both single-end (SE) and paired-end (PE) data. It consists of three main steps: (1) preprocessing - reads are collapsed, indexed and then aligned to the reference genome sequences using the split-read mapper BWA-MEM; (2) identification - reads are examined by first selecting those whose aligned segments locate on the same chromosome and the same strand; and (3) quantification - it deploys an alignment procedure to assess the abundance of the predicted circRNAs.
FUCHS / FUll CHaracterization of circular RNA using RNA Sequencing
Characterizes circRNAs candidates. FUCHS provides the user with directions for further steps to investigate the circRNA’s function and biogenesis. FUCHS is able to identify alternative exon usage within the same circle boundaries, summarize the different circles emerging from the same host-gene, quantify double-breakpoint fragments as indicator for circularity and visualize a circRNA’s read coverage profile independent of any genome browser.
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