RCAS
Bioc currentRNA Centric Annotation System
Release Lineage
Entered 3.4 · Oct 18, 2016
Current · Requires R 4.6
Description
RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
50 40 exported
Complexity
2.4 avg / 8 max
Call network
50 nodes / 30 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
4,345
Files
79
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
40
Internal functions
10
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.08
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
0
Dep constraint coverage
21.4%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
3.3.0
System requirements
1 externalpandoc (>= 1.12.3)
C++ standard
–
License
Artistic-2.0
License flags
SPDX valid, OSI approved
History
Versions
20
First release
2017-03-06
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
26
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 42
- Examples that run
- 89%25 run in a plain check · 3 never run
- Exported functions with examples
- 70%
- Documented parameters
- 100%
- Return-value docs
- 70%
- Help pages citing references
- 0%
Repository practices
Checks read the Bioconductor git repository (git.bioconductor.org), branch RELEASE_3_23.
1 practice detected in the Bioconductor git repository
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| gff | – | – | – |
| hg19.sample.gtf.granges | – | – | – |
| queryRegions | – | – | – |
Topics
Depended on by (1)
Bioconductor (1)
People
- Bora Uyar author maintainer
- Altuna Akalin author
- Ricardo Wurmus author
- Dilmurat Yusuf author
Cite
Cite this package
Run in R for the authors' entry:
citation("RCAS")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1093/nar/gkx120.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.