Rcirc helps to identify the coding ability of circRNA and visualize the feature at various aspects. Rcirc provides general visualization for both single circRNA and meta-feature of thousands of circRNAs, Rcirc was designed as a user-friendly tool which covers lots of high automatically functions without running many complicated processes by user.

The main goal of our study was to develop a user-friendly tool which covers the main demands for circRNA research. Rcirc is a capable and user-friendly package based on R language. The package provides numerous analyses for both upstream and downstream research include circRNA detection, coding ability identification, single feature analyses and visualization of meta-feature. Furthermore, the users can visualize the reads mapping situation for each junction site of circRNA by Rcirc with sequencing data. With growing attention on circRNA, Rcirc will become an auxiliary tool to encourage researchers to proceed with further analyses in circRNA world. All the detail of usage is included by the documents of Rcirc in GitHub's online pages.

https://rcirc-doc.readthedocs.io/en/latest/index.html

The functions under R/ expose the main workflows in the package. After loading the project in R (for example with devtools::load_all("/path/to/Rcirc")), you can call the exported helpers directly.

• PredictCirc(sam, fa, out, gtf) wraps the bundled CIRI2 script and takes a mapped SAM file together with the reference genome FASTA and annotation GTF to produce a circRNA prediction report.【F:R/PredictCirc.R†L5-L16】
• TranslateCirc(out, fastq, adapter, trimmomatic, genome, tmp, circgenome, rRNA) orchestrates the preprocessing of Ribo-seq reads: it builds Bowtie/STAR indices, trims adapters with Trimmomatic, removes rRNA and linear reads, and finally aligns the cleaned reads to the circRNA genome.【F:R/TranslateCirc.R†L1-L73】

• circ_summary(bamfile, gff) reads Ribo-seq alignments together with a circRNA annotation GFF, filters junction-spanning reads, and returns a list with the trimmed annotations, supporting reads, coverage summaries, and per-position views that drive the downstream plots.【F:R/rCirc.R†L23-L112】

Rcirc contains multiple high-level plotting functions to explore the detected circRNAs:

• showOverview(circbed, gff, genomefasta, ribo, rna) classifies circRNAs by genomic context (intergenic, exon, intron) and combines annotation, Ribo-seq, and RNA-seq summaries into a whole-genome overview plot.【F:R/showOverview.R†L1-L89】
• showDistribution(gbed, cbed) produces category counts for circRNAs based on their annotated start and end regions, helping compare intergenic, exonic, and intronic origins.【F:R/showDistribution.R†L1-L83】
• showJunction(data, max = 20, title = 0) inspects circRNA FASTA files and plots the most frequent splice signal combinations observed at back-splice junctions.【F:R/showJunction.R†L1-L37】
• showMapping(summary, circ_index, genomefile, ...) (exported as showMapping) visualises read density and sequence context around a selected circRNA junction using the summary list returned by circ_summary.【F:R/mappingPlot.R†L1-L107】
• Additional helpers such as showCodon, showLength, classByType, and stemRing focus on codon usage, length distribution, category summaries, and stem-loop structures respectively; see the source files in R/ for their parameters and plotting options.

These functions can be combined in scripts or notebooks to build a complete analysis workflow: predict circRNAs, process Ribo-seq support, summarise junction evidence, and visualise the resulting catalogue.