Skip to main content

dsRNAscan

CI Tests License: GPL v3

dsRNAscan is a bioinformatics tool for genome-wide identification of double-stranded RNA (dsRNA) structures. It uses a sliding window approach to detect inverted repeats that can form dsRNA secondary structures, with support for G-U wobble base pairing and ML-based scoring.

Browse human genome results at dsrna.chpc.utah.edu

Install

pip install dsrnascan

Linux and macOS, Python 3.8+ (Windows: use WSL).

Usage

# Scan a genome or transcriptome
dsrnascan input.fasta

# Scan a region
dsrnascan genome.fasta --only_seq chr21 --start 33455482 --end 33655482

By default dsRNAscan uses the settings of the human dsRNAome resource (--v1, on by default): windows of 10000 and 3000 nt, step 150, and a banded fold that gives ~99% identical results to the exact one. Any setting you change is listed in dsrnascan.log.

Parameter Default Description
-w 10000 and 3000 Window size (one window of this size if set)
-s/--step 150 Step size between windows
-c/--cpus 4 (whole allocation in a SLURM job) Number of CPUs
--score 75 Minimum einverted score
--percent_paired_cutoff 70 Minimum % paired bases after folding
--only_seq, --start/--end Full sequence Sequence and region to scan
--exact off Exact fold (slower)
--format bedpe Output format: bedpe, gff3, or both

Run dsrnascan --help for all options.

Output

  • *.dsrnas.parquet: one row per dsRNA with coordinates, search score, duplex fold, structure and ML scores (pd.read_parquet in Python, arrow::read_parquet in R; --txt writes the same table as tab-separated text)
  • *.bedpe: paired arm coordinates (default)
  • *.gff3: arms as linked features for IGV (--format gff3 or both)
  • *.bp: base-pair arcs for IGV

ML scores (likely_edited, likely_forms) come from structure-only models trained on human data, where inverted Alus dominate. Treat them as a rough guide, especially outside Alus and in other species. Both scores run 0-1 (cutoffs: stability 0.553, probing 0.440).

Browse, filter and export

# Interactive browser: filters, sorting, structure view, export of the filtered set
dsrna-browse results_folder/

# With RNA editing sites (BED or GFF3), counted per arm and shown on the structure
dsrna-browse results_folder/ --editing-file editing_sites.bed

# The same filters from the command line
dsrna-browse results_folder/ --min percent_paired 85 --max "dG(kcal/mol)" -150 --export strong.gff3
dsrna-browse results_folder/ --region chr19:1-3000000 --likely-edited --export region.tsv

Export formats: .tsv, .parquet, .bedpe, .bed, .gff3, .bp, .fa, .dbn. --columns lists the filterable columns; --where takes an SQL condition on them.

The browser also has analysis tabs on the filtered set (distributions, scatter, dsRNA density along chromosomes, group summaries, statistics; charts download as PNG/SVG, their numbers as TSV) and an IGV tab showing the dsRNAs as arcs on a hosted assembly (--genome dm6, guessed from the results name) or the scan's own FASTA (--genome genome.fa).

Citation

If you use dsRNAscan, please cite:

dsRNAscan maps human dsRNAome, revealing conservation, intermolecular dsRNA, and correlates of ADAR dependency. Molecular Cell (2026). https://doi.org/10.1016/j.molcel.2026.08.001

License

GNU General Public License v3.0 - see LICENSE. Issues: GitHub Issues

Acknowledgments

dsRNAscan's compiled engine reimplements:

  • EMBOSS einverted (inverted-repeat search): Rice P, Longden I, Bleasby A (2000) Trends Genet 16:276-277
  • ViennaRNA duplexfold (duplex folding): Lorenz R et al. (2011) Algorithms Mol Biol 6:26, with Turner 2004 energy parameters (Mathews DH et al. (2004) PNAS 101:7287)

Metadata

Release files for dsrnascan 0.7.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for dsrnascan 0.7.4
File Size Uploaded Provenance
dsrnascan-0.7.4.tar.gz 3.0 MB Without provenance Details

Built distributions (wheels)

Table of built distributions (wheels) for dsrnascan 0.7.4
File
dsrnascan-0.7.4-cp38-abi3-musllinux_1_2_x86_64.whl CPython 3.8 abi3 Linux musl 1.2+ x86-64 Details
dsrnascan-0.7.4-cp38-abi3-musllinux_1_2_aarch64.whl CPython 3.8 abi3 Linux musl 1.2+ ARM64 Details
dsrnascan-0.7.4-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.8 abi3 Linux glibc 2.17+ x86-64 Details
dsrnascan-0.7.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.8 abi3 Linux glibc 2.17+ ARM64 Details
dsrnascan-0.7.4-cp38-abi3-macosx_11_0_arm64.whl CPython 3.8 abi3 macOS 11.0+ ARM64 Details

Total release size: 15.3 MB

Release files / dsrnascan-0.7.4.tar.gz

Download URL dsrnascan-0.7.4.tar.gz
Size 3.0 MB
Tags Source
SHA-256 checksum
How to use checksums
3384c16b20300deaa2a7c6288b6748e6cce64f6ca10093c1ffe52e2cb7acc221
BLAKE2b-256 checksum
How to use checksums
a64a5956ee8836cf01cd4a014f01c41fb0cafe221bc7a8250249d9669f9d4722
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Release files / dsrnascan-0.7.4-cp38-abi3-musllinux_1_2_x86_64.whl

Download URL dsrnascan-0.7.4-cp38-abi3-musllinux_1_2_x86_64.whl
Size 2.7 MB
Tags CPython 3.8 Linux musl 1.2+ x86-64 abi3
SHA-256 checksum
How to use checksums
c80607edf8508e438e2dd4b45a9122952458685c461c5c77f17f69dc96d87355
BLAKE2b-256 checksum
How to use checksums
77fbd859e22d9740d6e520f2e679af22cd1b69b90334642a1abc6069638cc7ce
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Release files / dsrnascan-0.7.4-cp38-abi3-musllinux_1_2_aarch64.whl

Download URL dsrnascan-0.7.4-cp38-abi3-musllinux_1_2_aarch64.whl
Size 2.6 MB
Tags CPython 3.8 Linux musl 1.2+ ARM64 abi3
SHA-256 checksum
How to use checksums
63eed4d9a28868db88e0824956807c409f1e8cc6b4654d33451dec694509a3e4
BLAKE2b-256 checksum
How to use checksums
e70f744dc0d9f12fa9bcc22515f784cda874d5a4e22b3601df4ea354adc71d49
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Release files / dsrnascan-0.7.4-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL dsrnascan-0.7.4-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 2.7 MB
Tags CPython 3.8 Linux glibc 2.17+ x86-64 abi3
SHA-256 checksum
How to use checksums
96b30ac20358253a8f77e209a8c5b2944758c53c29cb908fadfc670bfe6b321a
BLAKE2b-256 checksum
How to use checksums
feb6f396ff66b52832946aef4c128b26aba463ba4aea2a49e9183bcce47c871a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Release files / dsrnascan-0.7.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL dsrnascan-0.7.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 2.6 MB
Tags CPython 3.8 Linux glibc 2.17+ ARM64 abi3
SHA-256 checksum
How to use checksums
7dbf3b3868a2889bf41061ebc449cbf702b2b3347cac57b5fd062ed2cd8a7c37
BLAKE2b-256 checksum
How to use checksums
222254ed3c28d446631ff930571189c90957e70cd2fb524111d89728f27c7330
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Release files / dsrnascan-0.7.4-cp38-abi3-macosx_11_0_arm64.whl

Download URL dsrnascan-0.7.4-cp38-abi3-macosx_11_0_arm64.whl
Size 1.7 MB
Tags CPython 3.8 abi3 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
8adc5f420dbe4e39494e70b67571f3f7bb2f08acaac38e58a15dd26ade4e1ae1
BLAKE2b-256 checksum
How to use checksums
442faa19417c40289db87bfbcb4b443a81f0735fc91ae80272f6e3209dab5893
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Security for dsrnascan 0.7.4

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.7.4 This release

6 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page