babappasnake 0.4.1

A simple Snakemake pipeline for episodic selection analysis

babappasnake

babappasnake is a command-line workflow for episodic positive selection analysis on a single orthogroup. It is designed for practical use: one command to launch, automatic checkpointing, resumable execution, and clear summary outputs.

What the pipeline does

1. Runs reciprocal best-hit (RBH) ortholog discovery.
2. Builds an orthogroup from your query and proteomes.
3. Maps user CDS records to orthogroup proteins (after lowercase intron clipping and uppercase ORF window extraction).
4. Creates protein and codon alignments with babappalign.
5. Trims alignments with ClipKIT (kpic-smart-gap).
6. Removes terminal stop codon artifacts after ClipKIT on the codon alignment.
7. Infers an ML tree with IQ-TREE (-m MFP -B 1000 -redo).
8. Roots the inferred tree using a user-supplied outgroup label query (case-insensitive header matching).
9. Runs HyPhy aBSREL and MEME on leaf branches (--branches Leaves).
10. Selects foreground branches from aBSREL using dynamic thresholding.
11. Runs branch-site codeml only for selected branches (alt and null models).
12. Runs codeml ancestral sequence reconstruction (ASR).
13. Produces final summary and tabular outputs.

Installation (for end users)

pip installs Python packages, but external bioinformatics binaries must also be available on PATH.

Recommended setup (conda + pip)

conda create -n babappasnake -c conda-forge -c bioconda \
  python=3.11 blast iqtree hyphy paml clipkit pip
conda activate babappasnake
pip install babappalign babappasnake

Quick verification

babappasnake --help
which blastp makeblastdb hyphy codeml clipkit babappalign

Notes:

• IQ-TREE binary detection is flexible (iqtree2, iqtree3, or iqtree).
• On Apple Silicon, iqtree3 is common and is accepted automatically.

Input requirements

• --prot: directory containing proteome FASTA files.
• --query: protein FASTA containing the query sequence.
• --cds (optional at first run): CDS FASTA for the orthogroup.
• --outgroup: outgroup query string used to root the IQ-TREE output (e.g., culex matches headers containing culex).

CDS quality checks (when --cds is supplied):

• Lowercase intron characters are clipped from each CDS.
• For each CDS, the best uppercase ATG ... STOP ORF window is retained.
• CDS records that still fail ORF/start-stop/frame checks are excluded.
• Proteins without a qualifying CDS match are skipped from codon/tree downstream analyses.

Quick start

Case A: you already have the CDS file

babappasnake \
  --prot /path/to/proteomes \
  --query /path/to/query.fasta \
  --cds /path/to/orthogroup_cds.fasta \
  --outgroup culex \
  --outdir run01 \
  --threads 8

Case B: two-stage run (CDS provided later)

Run once:

babappasnake \
  --prot /path/to/proteomes \
  --query /path/to/query.fasta \
  --outgroup culex \
  --outdir run01 \
  --threads 8

The first run stops intentionally and writes:

• run01/orthogroup/orthogroup_proteins.fasta
• run01/orthogroup/orthogroup_headers.txt
• run01/orthogroup/WAITING_FOR_CDS.txt

Then place your CDS FASTA at:

• run01/user_supplied/orthogroup_cds.fasta

Re-run the same command. The workflow resumes automatically.

Dynamic significance logic

Foreground selection from aBSREL uses dynamic p-thresholding:

• start at p <= 0.05
• if no branch passes, increase by 0.01
• stop as soon as at least one branch is found
• hard upper bound: 1.0

Only those selected branches go to branch-site codeml. Each selected branch runs two codeml fits (alternative + null), then BH-FDR correction is applied.

Output guide

All outputs are written under --outdir (default: babappasnake_run).

Most important files:

• summary/episodic_selection_summary.txt: human-readable final report.
• hyphy/foreground_threshold.json: selected dynamic threshold and hit count.
• hyphy/significant_foregrounds.tsv: selected aBSREL foreground branches.
• branchsite/branchsite_results.tsv: codeml branch-site statistics and BH significance.
• asr/asr_done.json: ASR completion record.
• asr/mlc_asr.txt: codeml ASR main output.
• asr/rst: reconstructed ancestral states.
• tree/orthogroup.treefile: inferred ML tree (unrooted IQ-TREE output).
• tree/orthogroup.rooted.treefile: rooted tree used by HyPhy and codeml downstream steps.

CLI reference

babappasnake --prot PROTEOMES_DIR --query QUERY_FASTA [options]

Options:

• --cds PATH: CDS FASTA (optional for initial run).
• --outgroup TEXT: outgroup query used for tree rooting (case-insensitive substring match against tip headers).
• --outdir PATH: output directory (default: babappasnake_run).
• --coverage FLOAT: RBH reciprocal coverage minimum (default: 0.70).
• --threads INT: parallel threads/cores (default: 4).
• --absrel-p FLOAT: compatibility parameter retained in config (dynamic mode is used for branch selection).
• --meme-p FLOAT: MEME reporting threshold in summary (default: 0.1).
• --use-clipkit {yes,no}: enable/disable ClipKIT (default: yes).
• --snake-args "...": extra raw arguments forwarded to Snakemake.

Example with additional Snakemake flags:

babappasnake \
  --prot /path/to/proteomes \
  --query /path/to/query.fasta \
  --cds /path/to/orthogroup_cds.fasta \
  --outgroup culex \
  --outdir run01 \
  --threads 8 \
  --snake-args "--keep-going"

Troubleshooting

"Missing required external tools"

Install missing binaries and ensure they are on PATH in the same shell where you run babappasnake.

Run stops with WAITING_FOR_CDS.txt

This is expected for two-stage usage. Add user_supplied/orthogroup_cds.fasta and re-run.

codeml returns non-zero or writes warnings

The workflow accepts codeml warning-heavy runs when valid output files are produced. Hard failure is triggered only when required codeml result files are missing.

Can I resume after interruption?

Yes. Re-run the same command; Snakemake resumes and re-runs incomplete jobs as needed.

Developer/local source install

For local development:

pip install -e .

The package entry-point command is still babappasnake.

Maintainer release checklist (GitHub + PyPI)

1. Update version in pyproject.toml.
2. Commit and push to GitHub.
3. Build distributions:

python -m pip install --upgrade build twine
python -m build
twine check dist/*

4. Publish to PyPI:

twine upload dist/*

5. Optionally create and push a matching git tag.

License

MIT