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Workflow for discovering and prioritizing locally conserved RNA secondary structure candidates from multiple sequence alignments

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RNAConSnake

Workflow for discovering and prioritizing conserved RNA secondary structure in multiple sequence alignments. Combines RNALalifold discovery with filtering (RNAz, AlifoldZ, covariation), de-replication, and optional empirical q-values via null-model calibration.

Quick start

# Install
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

# Run (accepts .stk or .aln)
RNAcs --input-alignment aln.stk --output-dir run_dir --cores all
RNAcs --input-alignment aln.aln --output-dir run_dir --cores all

# With calibration (100 null replicates)
RNAcs --input-alignment aln.stk --output-dir run_dir \
  --null-arm sissiz --null-replicates 100 --cores all

Check dependencies:

RNAcs --check-deps

What you get

  • RNAConSnake.nr.csv — one row per locus (de-replicated via de-replicating overlapping windows)
  • RNAConSnake.log.csv — all RNALalifold windows
  • Structure/alignment figures and metrics per candidate
  • alignment_screenability.tsv — blind-region reporting
  • With --null-arm: empirical q-values, FDR, and null score distributions
  • With --benchmark: recovery of known elements (Positive control) against q-values

Dependencies

External toolchain on PATH: ViennaRNA 2.7+, RNAz, Infernal, R-scape (optional).

Two tools are not on package indices and must be built by hand:

Note: Snakemake's --use-conda flag is not supported in this workflow (tools must be pre-installed on PATH). See environment.yaml for conda packages, or use the container (see container/README.md).

Documentation

Container

Pre-built x86_64 image published to ghcr.io on every push to dev:

apptainer pull rnacs.sif docker://ghcr.io/mtw/rnaconsnake:dev-<sha>
apptainer run --cleanenv --bind data:/data --bind run:/work rnacs.sif \
  --input-alignment /data/aln.stk --output-dir /work --cores all

See container/README.md for build and Apptainer details.

License

AGPL-3.0-or-later

About

Workflow for discovering and prioritizing locally conserved RNA secondary structure candidates from multiple sequence alignments

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Contributing

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1 watching

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